R/data.R

In artitzco/fraping: Package for FRAP Data Analysis and Interpretation

#' Download test database
#'
#' This function is used to download a test database for demonstration or testing purposes.
#'
#' @details The `downloadData` function downloads a test database and does not perform any further operations on it.
#'
#' @examples
#' \dontrun{
#'   # Download the test database
#'   downloadData()
#' }
#'
#' @export
downloadData <- function() {
  lista<-NULL
lista[[1]]<-"data"
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/01 R-Neurona 1_GFP-ROI10_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897,21.897)
Mean<-c(9.344,9.387,9.211,1.421,1.414,1.628,1.462,1.662,1.757,1.703,1.67,1.718,1.859,1.662,1.924,2.166,1.879,1.811,1.484,1.817,1.567,1.633,1.858,1.664,1.69,1.702,2.029,1.949,1.954,1.832,2.134,2.23)
StdDev<-c(21.022,22.133,20.507,4.057,3.808,4.517,3.634,4.491,4.426,4.072,4.367,4.949,4.402,4.17,4.872,5.498,4.619,4.397,4.048,4.632,3.996,3.851,4.543,4.164,4.265,4.271,4.938,4.516,4.617,4.333,5.645,4.931)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(186,189,188,38,28,46,35,71,34,37,45,54,29,38,53,58,36,38,45,38,42,34,42,34,49,32,42,34,36,43,78,32)
IntDen<-c(204.617,205.542,201.697,31.118,30.972,35.644,32.019,36.398,38.466,37.298,36.568,37.615,40.704,36.398,42.14,47.42,41.142,39.658,32.505,39.78,34.306,35.765,40.68,36.447,37.006,37.274,44.427,42.675,42.797,40.121,46.738,48.831)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(8410,8448,8290,1279,1273,1465,1316,1496,1581,1533,1503,1546,1673,1496,1732,1949,1691,1630,1336,1635,1410,1470,1672,1498,1521,1532,1826,1754,1759,1649,1921,2007)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[2]]<-list(file=file, name=name, value=value)
cat("-------------",1,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/01 R-Neurona 1_GFP-ROI4_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566,17.566)
Mean<-c(3.875,4.481,4.086,0.609,0.593,0.639,0.867,0.875,0.76,0.788,0.86,0.752,0.816,1.017,0.881,0.976,0.733,0.789,0.742,0.932,0.86,0.843,1.079,0.938,0.929,1.072,0.895,0.95,0.97,0.945,0.96,0.731)
StdDev<-c(7.025,9.382,7.939,2.246,1.792,2.353,2.704,2.179,2.134,2.047,2.22,1.965,2.129,2.48,2.374,2.566,1.828,1.988,2.024,2.552,2.153,2.336,2.708,2.58,3.133,2.686,2.736,2.555,2.523,2.338,2.729,1.949)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(43,110,66,18,14,28,31,19,26,17,17,19,25,22,24,21,16,18,27,22,18,25,22,32,54,24,33,23,20,21,28,20)
IntDen<-c(68.076,78.708,71.774,10.705,10.413,11.216,15.231,15.377,13.357,13.844,15.109,13.211,14.33,17.858,15.474,17.153,12.871,13.868,13.041,16.374,15.109,14.817,18.953,16.472,16.326,18.832,15.717,16.691,17.031,16.593,16.861,12.846)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2798,3235,2950,440,428,461,626,632,549,569,621,543,589,734,636,705,529,570,536,673,621,609,779,677,671,774,646,686,700,682,693,528)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[3]]<-list(file=file, name=name, value=value)
cat("-------------",2,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/01 R-Neurona 1_GFP-ROI5_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887,45.887)
Mean<-c(5.944,6.239,6.336,0.95,1.166,1.064,1.033,1.137,1.112,1.164,1.037,1.272,1.167,1.251,1.148,1.291,1.109,1.327,1.173,1.226,1.341,1.185,1.225,1.301,1.35,1.393,1.38,1.353,1.335,1.492,1.256,1.245)
StdDev<-c(12.098,12.949,12.453,2.869,3.277,2.946,2.78,2.961,3.224,2.961,2.709,3.013,2.964,3.444,2.92,3.089,2.952,3.367,3.414,2.962,3.448,3.114,2.983,3.099,3.437,3.991,3.289,3.299,3.392,3.642,2.904,3.297)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(87,114,139,24,34,24,34,28,53,32,35,28,55,56,28,24,29,34,59,35,34,36,32,32,38,90,35,28,45,32,26,73)
IntDen<-c(272.766,286.294,290.722,43.575,53.526,48.831,47.42,52.188,51.02,53.405,47.59,58.368,53.551,57.419,52.675,59.244,50.874,60.899,53.818,56.276,61.555,54.378,56.203,59.706,61.945,63.915,63.332,62.066,61.239,68.441,57.638,57.152)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11211,11767,11949,1791,2200,2007,1949,2145,2097,2195,1956,2399,2201,2360,2165,2435,2091,2503,2212,2313,2530,2235,2310,2454,2546,2627,2603,2551,2517,2813,2369,2349)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[4]]<-list(file=file, name=name, value=value)
cat("-------------",3,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/01 R-Neurona 1_GFP-ROI7_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239,51.239)
Mean<-c(2.76,2.779,2.888,0.441,0.506,0.513,0.604,0.579,0.603,0.664,0.679,0.64,0.738,0.718,0.679,0.676,0.698,0.729,0.761,0.621,0.733,0.786,0.776,0.855,0.698,0.79,0.788,0.816,0.826,0.726,0.798,0.836)
StdDev<-c(7.712,7.485,7.613,2.199,2.103,2.054,1.93,1.797,1.766,1.915,2.197,1.764,2.053,1.913,1.834,1.784,1.878,2.021,2.303,1.757,2.07,2.411,2.352,2.555,2.058,2.226,2.113,2.217,2.218,1.906,2.132,2.119)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(89,78,86,63,52,43,38,25,22,24,41,29,24,19,17,22,25,37,40,29,32,50,47,53,32,35,21,29,24,22,31,21)
IntDen<-c(141.432,142.405,148.001,22.603,25.936,26.277,30.948,29.683,30.875,34.038,34.768,32.797,37.833,36.787,34.792,34.622,35.765,37.371,38.977,31.824,37.566,40.291,39.756,43.819,35.79,40.485,40.388,41.799,42.335,37.201,40.899,42.846)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(5813,5853,6083,929,1066,1080,1272,1220,1269,1399,1429,1348,1555,1512,1430,1423,1470,1536,1602,1308,1544,1656,1634,1801,1471,1664,1660,1718,1740,1529,1681,1761)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[5]]<-list(file=file, name=name, value=value)
cat("-------------",4,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/02 FRAP2 GFP neurona2_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707,29.707)
Mean<-c(4.026,4.437,4.333,0.857,0.906,0.977,1.004,1.002,1.002,1.076,0.906,1.023,0.867,0.977,1.041,0.987,0.993,0.876,0.925,0.877,0.869,0.969,0.94,0.961,0.988,0.97,0.996,1.004,1.035,0.875,0.975,0.965,1.049,0.908,1.183,1.117)
StdDev<-c(7.767,9.297,8.526,2.626,2.412,2.551,2.614,2.456,2.485,2.642,2.32,2.497,2.318,2.501,2.936,2.521,2.49,2.338,2.716,2.301,2.32,2.335,2.484,3.107,2.568,2.459,2.546,2.599,2.943,2.398,2.691,2.735,2.919,2.436,2.964,2.885)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(60,95,58,27,26,37,28,29,27,22,22,22,28,24,31,24,20,22,52,23,26,17,23,49,32,19,28,23,46,24,34,34,29,22,29,23)
IntDen<-c(119.607,131.821,128.731,25.449,26.909,29.026,29.829,29.756,29.78,31.97,26.909,30.388,25.766,29.026,30.924,29.318,29.488,26.033,27.493,26.058,25.814,28.783,27.931,28.539,29.342,28.807,29.586,29.829,30.753,25.985,28.953,28.661,31.167,26.982,35.133,33.186)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4916,5418,5291,1046,1106,1193,1226,1223,1224,1314,1106,1249,1059,1193,1271,1205,1212,1070,1130,1071,1061,1183,1148,1173,1206,1184,1216,1226,1264,1068,1190,1178,1281,1109,1444,1364)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[6]]<-list(file=file, name=name, value=value)
cat("-------------",5,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/02 FRAP2 GFP neurona2_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401,25.401)
Mean<-c(6.125,5.852,6.01,1.047,0.984,1.057,1.2,1.139,1.191,1.298,1.156,1.074,1.032,1.141,0.975,1.032,1.091,1.094,1.175,1.284,0.961,1.096,1.121,1.099,1.241,1.133,1.041,1.239,1.213,1.277,1.192,1.105,1.198,1.093,1.374,1.159)
StdDev<-c(11.032,10.181,10.651,2.882,2.624,2.669,2.904,2.692,3.304,3.327,2.957,2.783,2.396,2.987,2.276,2.792,2.898,2.642,2.78,3.32,2.488,2.814,3.015,2.611,2.976,2.901,2.521,2.955,3.031,3.15,3.308,2.834,3.024,2.847,3.189,2.73)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(76,83,93,32,30,24,21,25,67,44,47,35,15,47,18,33,30,19,22,39,24,31,39,24,21,33,23,20,26,25,41,25,23,27,28,22)
IntDen<-c(155.567,148.633,152.648,26.593,24.987,26.861,30.486,28.929,30.242,32.967,29.367,27.274,26.204,28.977,24.768,26.204,27.712,27.785,29.853,32.627,24.403,27.834,28.466,27.907,31.532,28.783,26.447,31.483,30.802,32.432,30.267,28.077,30.437,27.761,34.89,29.44)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(6394,6109,6274,1093,1027,1104,1253,1189,1243,1355,1207,1121,1077,1191,1018,1077,1139,1142,1227,1341,1003,1144,1170,1147,1296,1183,1087,1294,1266,1333,1244,1154,1251,1141,1434,1210)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[7]]<-list(file=file, name=name, value=value)
cat("-------------",5,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/03 FRAP3 GFP neurona3_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398,36.398)
Mean<-c(6.629,6.571,6.276,1.13,1.17,1.018,1.055,0.85,0.988,1.147,1.011,1.139,1.172,1.029,1.051,0.977,1.042,1.006,0.983,1.1,0.984,1.044,1.043,0.999,0.999,1.04,1.035,1.043,1.053,0.97,0.936,0.971,0.955,1.088,0.961,1.058,0.969,1.017,0.971,0.976)
StdDev<-c(12.937,13.085,12.021,3.267,3.2,2.789,2.661,2.026,2.371,2.988,2.658,2.914,3.538,2.614,2.751,2.502,2.67,2.746,2.638,2.897,2.538,2.835,2.865,2.911,2.911,2.776,2.652,4.266,3.468,2.501,2.717,2.708,2.795,3.052,2.67,2.864,2.639,2.967,2.588,2.662)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(129,113,115,41,28,34,24,19,23,37,27,33,82,24,27,33,25,36,25,39,26,32,50,51,51,34,25,120,78,22,57,39,26,31,35,32,33,53,24,33)
IntDen<-c(241.283,239.166,228.436,41.142,42.602,37.055,38.393,30.924,35.96,41.751,36.787,41.459,42.651,37.444,38.247,35.546,37.931,36.617,35.79,40.023,35.814,38.004,37.979,36.374,36.374,37.858,37.663,37.955,38.344,35.303,34.062,35.352,34.744,39.585,34.987,38.515,35.279,37.031,35.327,35.522)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(9917,9830,9389,1691,1751,1523,1578,1271,1478,1716,1512,1704,1753,1539,1572,1461,1559,1505,1471,1645,1472,1562,1561,1495,1495,1556,1548,1560,1576,1451,1400,1453,1428,1627,1438,1583,1450,1522,1452,1460)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[8]]<-list(file=file, name=name, value=value)
cat("-------------",6,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/03 FRAP3 GFP neurona3_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547)
Mean<-c(11.548,12.011,11.556,1.83,1.651,1.774,1.663,2.001,1.858,1.937,1.832,1.886,1.561,1.669,1.562,1.962,1.57,1.808,1.644,1.634,1.497,1.737,1.675,1.7,1.798,1.616,1.473,1.423,1.608,1.803,1.584,1.587,1.655,1.441,1.659,1.81,1.809,1.624,1.604,1.658)
StdDev<-c(22.088,23.309,22.987,4.924,4.39,4.554,4.741,4.661,4.668,4.689,5.028,4.51,3.989,4.272,3.704,5.247,4.158,4.453,4.031,4.413,3.721,4.523,4.823,4.507,4.93,4.347,3.756,4.002,4.424,4.54,4.217,4.057,4.529,3.908,4.203,4.494,4.624,4.082,4.524,4.1)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(159,177,167,49,41,47,67,37,60,38,70,36,37,38,28,76,41,50,40,52,30,51,91,57,52,46,40,51,46,44,43,37,70,36,40,44,46,46,50,45)
IntDen<-c(295.004,306.853,295.223,46.763,42.189,45.327,42.481,51.118,47.468,49.488,46.811,48.174,39.877,42.627,39.902,50.12,40.121,46.179,41.994,41.751,38.247,44.378,42.797,43.429,45.935,41.288,37.639,36.349,41.069,46.057,40.461,40.534,42.286,36.812,42.383,46.227,46.203,41.483,40.972,42.359)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(12125,12612,12134,1922,1734,1863,1746,2101,1951,2034,1924,1980,1639,1752,1640,2060,1649,1898,1726,1716,1572,1824,1759,1785,1888,1697,1547,1494,1688,1893,1663,1666,1738,1513,1742,1900,1899,1705,1684,1741)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[9]]<-list(file=file, name=name, value=value)
cat("-------------",7,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite"
name<-"GFP/GFP Secondary Neurite/03 FRAP3 GFP neurona3_ROI6 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644,25.644)
Mean<-c(22.394,22.342,21.758,3.079,3.02,3.009,2.968,2.919,3.021,2.77,2.664,2.813,2.847,3.003,2.661,3.027,2.898,2.786,2.888,2.888,2.762,2.912,2.796,2.702,2.772,2.772,2.798,3.022,2.913,2.818,2.738,2.626,2.625,2.972,2.671,2.484,2.75,2.443,2.576,2.531)
StdDev<-c(39.248,39.062,38.796,7.007,6.987,6.857,6.638,6.145,6.255,6.654,5.753,6.45,6.127,6.613,5.984,6.772,6.777,6.152,6.52,6.536,6.431,6.584,6.233,6.168,6.876,6.625,6.408,6.68,6.522,6.624,6.61,5.947,6.091,6.592,6.356,5.751,6.771,5.524,5.924,5.734)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(252,249,249,74,57,75,49,50,52,69,44,72,61,50,57,74,65,57,68,53,61,75,58,47,73,61,54,59,53,45,61,57,41,47,45,47,89,50,54,46)
IntDen<-c(574.266,572.928,557.965,78.952,77.443,77.175,76.105,74.864,77.467,71.044,68.319,72.139,73.015,77.005,68.246,77.613,74.304,71.434,74.061,74.061,70.825,74.669,71.701,69.292,71.093,71.093,71.75,77.492,74.694,72.261,70.217,67.346,67.322,76.202,68.49,63.696,70.533,62.65,66.057,64.913)
Median<-c(5,6,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(23603,23548,22933,3245,3183,3172,3128,3077,3184,2920,2808,2965,3001,3165,2805,3190,3054,2936,3044,3044,2911,3069,2947,2848,2922,2922,2949,3185,3070,2970,2886,2768,2767,3132,2815,2618,2899,2575,2715,2668)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[10]]<-list(file=file, name=name, value=value)
cat("-------------",8,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/01 R-Neurona 1_GFP-ROI10_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[11]]<-list(file=file, name=name, value=value)
cat("-------------",9,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/01 R-Neurona 1_GFP-ROI4_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[12]]<-list(file=file, name=name, value=value)
cat("------------",10,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/01 R-Neurona 1_GFP-ROI5_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[13]]<-list(file=file, name=name, value=value)
cat("------------",11,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/01 R-Neurona 1_GFP-ROI7_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[14]]<-list(file=file, name=name, value=value)
cat("------------",12,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/02 FRAP2 GFP neurona2_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911)
Mean<-c(1.106,1.091,1.018,0.772,0.852,0.806,0.931,0.841,0.856,0.789,1.001,0.818,0.801,0.835,0.86,0.759,0.661,0.671,0.799,0.766,0.794,0.844,0.744,0.668,0.805,0.749,0.743,0.968,0.782,0.745,0.703,0.706,0.662,0.77,0.803,0.799)
StdDev<-c(3.176,2.737,2.458,2.349,2.402,2.25,2.63,2.248,2.06,1.997,2.855,2.292,2.141,2.215,2.483,2.419,1.825,1.949,2.308,2.202,2.182,2.549,2.172,2.038,2.192,2.02,2.274,2.585,2.207,2.183,1.917,2.05,1.927,2.231,2.355,2.294)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(67,38,23,21,23,25,42,28,18,22,57,28,21,19,27,34,22,20,24,25,21,36,25,25,20,18,27,23,19,24,16,20,23,23,27,23)
IntDen<-c(39.731,39.196,36.568,27.712,30.583,28.953,33.43,30.218,30.729,28.32,35.96,29.367,28.783,29.999,30.899,27.274,23.722,24.087,28.685,27.517,28.515,30.315,26.715,23.99,28.904,26.909,26.666,34.768,28.077,26.739,25.23,25.352,23.771,27.663,28.831,28.685)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(1633,1611,1503,1139,1257,1190,1374,1242,1263,1164,1478,1207,1183,1233,1270,1121,975,990,1179,1131,1172,1246,1098,986,1188,1106,1096,1429,1154,1099,1037,1042,977,1137,1185,1179)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[15]]<-list(file=file, name=name, value=value)
cat("------------",13,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/02 FRAP2 GFP neurona2_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911,35.911)
Mean<-c(1.106,1.091,1.018,0.772,0.852,0.806,0.931,0.841,0.856,0.789,1.001,0.818,0.801,0.835,0.86,0.759,0.661,0.671,0.799,0.766,0.794,0.844,0.744,0.668,0.805,0.749,0.743,0.968,0.782,0.745,0.703,0.706,0.662,0.77,0.803,0.799)
StdDev<-c(3.176,2.737,2.458,2.349,2.402,2.25,2.63,2.248,2.06,1.997,2.855,2.292,2.141,2.215,2.483,2.419,1.825,1.949,2.308,2.202,2.182,2.549,2.172,2.038,2.192,2.02,2.274,2.585,2.207,2.183,1.917,2.05,1.927,2.231,2.355,2.294)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(67,38,23,21,23,25,42,28,18,22,57,28,21,19,27,34,22,20,24,25,21,36,25,25,20,18,27,23,19,24,16,20,23,23,27,23)
IntDen<-c(39.731,39.196,36.568,27.712,30.583,28.953,33.43,30.218,30.729,28.32,35.96,29.367,28.783,29.999,30.899,27.274,23.722,24.087,28.685,27.517,28.515,30.315,26.715,23.99,28.904,26.909,26.666,34.768,28.077,26.739,25.23,25.352,23.771,27.663,28.831,28.685)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(1633,1611,1503,1139,1257,1190,1374,1242,1263,1164,1478,1207,1183,1233,1270,1121,975,990,1179,1131,1172,1246,1098,986,1188,1106,1096,1429,1154,1099,1037,1042,977,1137,1185,1179)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[16]]<-list(file=file, name=name, value=value)
cat("------------",14,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/03 FRAP3 GFP neurona3_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[17]]<-list(file=file, name=name, value=value)
cat("------------",14,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/03 FRAP3 GFP neurona3_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[18]]<-list(file=file, name=name, value=value)
cat("------------",15,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Back"
name<-"GFP/GFP Secondary Neurite Back/03 FRAP3 GFP neurona3_ROI6 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[19]]<-list(file=file, name=name, value=value)
cat("------------",16,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/01 R-Neurona 1_GFP-ROI10_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[20]]<-list(file=file, name=name, value=value)
cat("------------",17,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/01 R-Neurona 1_GFP-ROI4_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[21]]<-list(file=file, name=name, value=value)
cat("------------",18,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/01 R-Neurona 1_GFP-ROI5_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[22]]<-list(file=file, name=name, value=value)
cat("------------",19,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/01 R-Neurona 1_GFP-ROI7_FRAP1 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[23]]<-list(file=file, name=name, value=value)
cat("------------",20,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/02 FRAP2 GFP neurona2_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607)
Mean<-c(5.372,4.976,5.095,4.62,4.777,4.605,4.569,4.226,3.969,3.957,3.819,3.88,3.585,3.677,3.269,3.237,3.348,3.382,3.393,3.199,3.569,3.39,3.419,3.435,3.146,3.429,2.955,3.176,3.072,3.161,2.962,3.003,3.092,2.95,3.238,2.886)
StdDev<-c(9.29,8.955,9.025,8.967,8.859,8.173,8.631,8.085,7.354,7.639,7.183,7.267,6.865,7.116,6.645,6.248,6.585,6.505,6.318,6.357,7.145,6.577,6.84,7.009,6.039,7.148,5.697,6.224,6.279,6.251,5.812,5.777,6.032,5.686,6.435,5.609)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(75,60,72,77,87,49,49,64,53,65,81,49,48,48,54,54,44,59,56,43,80,52,63,65,51,65,48,63,41,56,54,44,43,50,56,49)
IntDen<-c(164.424,152.307,155.957,141.407,146.2,140.945,139.85,129.339,121.481,121.116,116.882,118.756,109.729,112.552,100.07,99.073,102.479,103.501,103.841,97.905,109.243,103.768,104.644,105.131,96.299,104.961,90.46,97.199,94.012,96.737,90.654,91.92,94.645,90.289,99.121,88.343)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0)
RawIntDen<-c(6758,6260,6410,5812,6009,5793,5748,5316,4993,4978,4804,4881,4510,4626,4113,4072,4212,4254,4268,4024,4490,4265,4301,4321,3958,4314,3718,3995,3864,3976,3726,3778,3890,3711,4074,3631)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[24]]<-list(file=file, name=name, value=value)
cat("------------",21,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/02 FRAP2 GFP neurona2_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)
Area<-c(30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607,30.607)
Mean<-c(5.372,4.976,5.095,4.62,4.777,4.605,4.569,4.226,3.969,3.957,3.819,3.88,3.585,3.677,3.269,3.237,3.348,3.382,3.393,3.199,3.569,3.39,3.419,3.435,3.146,3.429,2.955,3.176,3.072,3.161,2.962,3.003,3.092,2.95,3.238,2.886)
StdDev<-c(9.29,8.955,9.025,8.967,8.859,8.173,8.631,8.085,7.354,7.639,7.183,7.267,6.865,7.116,6.645,6.248,6.585,6.505,6.318,6.357,7.145,6.577,6.84,7.009,6.039,7.148,5.697,6.224,6.279,6.251,5.812,5.777,6.032,5.686,6.435,5.609)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(75,60,72,77,87,49,49,64,53,65,81,49,48,48,54,54,44,59,56,43,80,52,63,65,51,65,48,63,41,56,54,44,43,50,56,49)
IntDen<-c(164.424,152.307,155.957,141.407,146.2,140.945,139.85,129.339,121.481,121.116,116.882,118.756,109.729,112.552,100.07,99.073,102.479,103.501,103.841,97.905,109.243,103.768,104.644,105.131,96.299,104.961,90.46,97.199,94.012,96.737,90.654,91.92,94.645,90.289,99.121,88.343)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0)
RawIntDen<-c(6758,6260,6410,5812,6009,5793,5748,5316,4993,4978,4804,4881,4510,4626,4113,4072,4212,4254,4268,4024,4490,4265,4301,4321,3958,4314,3718,3995,3864,3976,3726,3778,3890,3711,4074,3631)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[25]]<-list(file=file, name=name, value=value)
cat("------------",22,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/03 FRAP3 GFP neurona3_ROI4 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[26]]<-list(file=file, name=name, value=value)
cat("------------",23,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/03 FRAP3 GFP neurona3_ROI5 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[27]]<-list(file=file, name=name, value=value)
cat("------------",23,"% progress","------------\n")
file<-"GFP/GFP Secondary Neurite Control"
name<-"GFP/GFP Secondary Neurite Control/03 FRAP3 GFP neurona3_ROI6 secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[28]]<-list(file=file, name=name, value=value)
cat("------------",24,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/01 R-Neurona 1_GFP-ROI1_FRAP terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94,43.94)
Mean<-c(2.701,2.841,2.846,0.622,0.724,0.77,0.834,0.821,0.786,0.855,0.908,0.983,0.896,0.911,0.872,0.957,0.992,1.14,0.899,0.964,0.947,1.03,0.975,1.088,0.946,0.97,0.983,1.038,1.073,1.028,1.066,1.09)
StdDev<-c(5.898,6.44,6.068,2.354,2.708,2.246,2.447,2.275,2.245,2.308,2.193,2.483,2.3,2.362,2.188,2.504,2.839,3.068,2.255,2.574,2.334,2.981,2.61,2.667,2.392,2.48,2.449,2.476,2.597,2.566,2.639,2.721)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(50,62,51,28,53,24,27,22,33,28,21,28,21,31,26,38,57,52,34,49,25,64,33,27,21,28,24,23,21,24,23,36)
IntDen<-c(118.683,124.838,125.057,27.323,31.824,33.843,36.666,36.082,34.549,37.59,39.877,43.186,39.366,40.048,38.296,42.067,43.6,50.072,39.512,42.359,41.605,45.279,42.846,47.809,41.58,42.627,43.186,45.619,47.152,45.181,46.86,47.882)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4878,5131,5140,1123,1308,1391,1507,1483,1420,1545,1639,1775,1618,1646,1574,1729,1792,2058,1624,1741,1710,1861,1761,1965,1709,1752,1775,1875,1938,1857,1926,1968)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[29]]<-list(file=file, name=name, value=value)
cat("------------",25,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/01 R-Neurona 1_GFP-ROI2_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303,25.303)
Mean<-c(3.356,3.448,3.425,0.643,0.787,0.934,1.012,0.779,0.973,0.977,0.988,0.891,0.977,0.955,0.989,0.972,0.93,1.091,0.997,0.924,0.984,1.048,1.093,0.994,1.023,1.071,1.098,1.109,0.973,0.992,1.152,0.977)
StdDev<-c(7.04,7.476,7.442,2.352,2.443,2.762,3.19,2.031,2.486,2.524,2.855,2.406,2.546,2.47,2.379,2.634,2.396,3.265,2.986,2.441,2.615,2.792,2.643,2.508,2.798,2.585,2.909,2.776,2.467,2.368,2.895,2.618)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(48,74,81,30,20,27,48,18,23,22,36,32,26,35,22,33,21,61,40,23,24,28,24,19,41,19,41,27,21,19,22,40)
IntDen<-c(84.912,87.248,86.664,16.277,19.902,23.625,25.62,19.707,24.622,24.719,25.011,22.554,24.719,24.16,25.036,24.598,23.527,27.615,25.23,23.381,24.89,26.52,27.663,25.157,25.887,27.104,27.785,28.053,24.622,25.109,29.148,24.719)
Median<-c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(3490,3586,3562,669,818,971,1053,810,1012,1016,1028,927,1016,993,1029,1011,967,1135,1037,961,1023,1090,1137,1034,1064,1114,1142,1153,1012,1032,1198,1016)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[30]]<-list(file=file, name=name, value=value)
cat("------------",26,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/01 R-Neurona 1_GFP-ROI3_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931,27.931)
Mean<-c(8.984,9.895,9.723,1.44,1.553,1.579,1.78,1.739,1.57,1.625,1.66,1.71,1.913,1.635,1.771,1.764,1.849,1.753,1.816,1.735,1.942,1.928,1.97,1.725,1.772,1.875,1.955,1.848,1.686,1.851,2.01,1.817)
StdDev<-c(14.307,15.698,15.701,3.788,3.926,4.055,4.219,5.386,3.468,3.645,3.884,3.591,4.185,3.557,4.08,4.052,3.875,3.792,4.283,3.951,4.186,4.479,4.802,3.653,4.157,4.177,3.993,3.828,3.741,3.84,4.256,3.795)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(95,103,115,36,27,42,34,128,38,26,33,25,34,29,38,40,28,31,42,33,37,33,80,30,37,31,30,31,29,33,39,28)
IntDen<-c(250.942,276.391,271.574,40.218,43.381,44.111,49.707,48.563,43.843,45.4,46.373,47.76,53.429,45.668,49.463,49.269,51.653,48.952,50.728,48.466,54.232,53.843,55.035,48.174,49.488,52.383,54.597,51.604,47.079,51.702,56.154,50.753)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(10314,11360,11162,1653,1783,1813,2043,1996,1802,1866,1906,1963,2196,1877,2033,2025,2123,2012,2085,1992,2229,2213,2262,1980,2034,2153,2244,2121,1935,2125,2308,2086)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[31]]<-list(file=file, name=name, value=value)
cat("------------",27,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/03 FRAP3 GFP neurona3_ROI1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578,42.578)
Mean<-c(3.629,3.865,3.663,0.729,0.746,0.675,0.779,0.821,0.658,0.739,0.729,0.717,0.811,0.823,0.77,0.667,0.767,0.691,0.773,0.81,0.737,0.692,0.722,0.654,0.695,0.639,0.601,0.577,0.663,0.649,0.683,0.682,0.721,0.637,0.669,0.798,0.729,0.739,0.65,0.805)
StdDev<-c(8.505,9.212,8.76,2.253,2.269,1.938,2.312,2.368,2.037,2.112,2.312,1.945,2.252,2.363,2.466,1.812,2.175,2.108,2.114,2.425,2.103,1.978,2.065,1.715,2.184,1.902,1.691,1.68,1.993,2.127,2.038,2.223,2.212,1.903,1.969,2.227,2.168,2.327,2.262,3.08)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(93,103,79,20,25,26,29,32,36,27,39,21,36,29,52,22,27,41,20,36,26,24,22,16,33,23,22,20,29,22,26,32,31,20,19,20,30,29,37,64)
IntDen<-c(154.497,164.545,155.981,31.021,31.751,28.758,33.186,34.938,28.004,31.483,31.045,30.534,34.549,35.036,32.773,28.393,32.675,29.415,32.894,34.5,31.362,29.464,30.729,27.834,29.61,27.201,25.595,24.574,28.223,27.639,29.099,29.05,30.705,27.128,28.491,33.989,31.021,31.483,27.663,34.257)
Median<-c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(6350,6763,6411,1275,1305,1182,1364,1436,1151,1294,1276,1255,1420,1440,1347,1167,1343,1209,1352,1418,1289,1211,1263,1144,1217,1118,1052,1010,1160,1136,1196,1194,1262,1115,1171,1397,1275,1294,1137,1408)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[32]]<-list(file=file, name=name, value=value)
cat("------------",28,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/03 FRAP3 GFP neurona3_ROI2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875,20.875)
Mean<-c(5.226,4.773,4.647,0.872,0.697,0.825,0.836,0.833,0.659,0.873,0.878,0.829,0.913,0.639,0.892,0.81,0.802,0.869,0.737,0.887,0.817,0.894,0.787,0.801,0.874,0.86,0.784,0.777,0.861,0.836,0.844,0.804,0.812,0.864,0.765,0.829,0.848,0.684,0.762,0.728)
StdDev<-c(10.711,9.201,9.54,2.568,1.842,2.311,2.251,2.208,1.788,2.337,2.34,2.137,2.264,1.696,2.312,2.159,2.082,2.309,1.833,2.418,2.066,2.539,2.3,2.327,2.472,2.81,2.149,2.32,2.751,2.537,2.745,2.529,2.351,2.244,2.014,2.297,2.356,1.907,2.229,2.145)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(118,71,97,24,22,19,22,22,22,22,29,18,17,19,23,22,20,29,18,21,15,33,30,33,29,49,19,23,34,39,27,38,26,23,17,26,23,22,24,21)
IntDen<-c(109.097,99.632,97.005,18.199,14.549,17.226,17.445,17.396,13.747,18.223,18.321,17.299,19.051,13.333,18.613,16.909,16.739,18.15,15.377,18.515,17.055,18.661,16.423,16.715,18.248,17.956,16.374,16.228,17.98,17.445,17.615,16.788,16.958,18.029,15.961,17.299,17.712,14.282,15.912,15.206)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4484,4095,3987,748,598,708,717,715,565,749,753,711,783,548,765,695,688,746,632,761,701,767,675,687,750,738,673,667,739,717,724,690,697,741,656,711,728,587,654,625)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[33]]<-list(file=file, name=name, value=value)
cat("------------",29,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite"
name<-"GFP/GFP Tertiary Neurite/03 FRAP3 GFP neurona3_ROI3 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262,12.262)
Mean<-c(14.159,14.464,14.036,1.944,2.127,1.909,2.073,2.185,1.72,2.081,1.996,1.784,1.986,2.147,2.149,2.02,1.863,2.143,1.978,2.155,1.873,1.919,1.861,1.974,1.806,1.829,1.923,1.823,1.929,1.647,2.069,1.597,1.558,2.256,1.74,1.571,1.438,1.603,1.413,1.671)
StdDev<-c(18.794,18.242,18.668,4,4.426,4.253,4.481,4.336,3.971,4.306,4.147,4.2,4.006,4.46,4.75,4.035,4.023,4.444,4.639,4.334,5.03,4.089,3.916,4.216,3.918,3.803,4.088,4.312,4.417,3.925,4.483,3.505,3.458,4.791,3.87,3.562,3.448,3.606,3.235,3.636)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(127,95,101,29,28,34,27,30,37,30,34,44,28,36,42,25,24,32,39,29,62,30,28,30,27,21,25,37,31,34,36,23,26,43,33,27,30,29,28,24)
IntDen<-c(173.62,177.367,172.112,23.844,26.082,23.406,25.425,26.788,21.094,25.522,24.476,21.873,24.355,26.325,26.35,24.768,22.846,26.277,24.257,26.423,22.968,23.527,22.822,24.209,22.14,22.432,23.576,22.359,23.649,20.194,25.376,19.586,19.099,27.663,21.338,19.27,17.639,19.659,17.323,20.486)
Median<-c(7,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(7136,7290,7074,980,1072,962,1045,1101,867,1049,1006,899,1001,1082,1083,1018,939,1080,997,1086,944,967,938,995,910,922,969,919,972,830,1043,805,785,1137,877,792,725,808,712,842)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[34]]<-list(file=file, name=name, value=value)
cat("------------",30,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/01 R-Neurona 1_GFP-ROI1_FRAP terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[35]]<-list(file=file, name=name, value=value)
cat("------------",31,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/01 R-Neurona 1_GFP-ROI2_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[36]]<-list(file=file, name=name, value=value)
cat("------------",32,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/01 R-Neurona 1_GFP-ROI3_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756,39.756)
Mean<-c(0.365,0.337,0.321,0.051,0.133,0.195,0.236,0.266,0.305,0.321,0.322,0.32,0.33,0.323,0.405,0.328,0.37,0.321,0.3,0.278,0.351,0.3,0.293,0.293,0.303,0.357,0.315,0.302,0.304,0.316,0.329,0.333)
StdDev<-c(1.401,0.636,0.467,0.22,0.34,0.482,0.674,0.442,0.788,0.57,0.503,0.689,0.868,0.664,3.171,0.687,1.53,0.637,0.458,0.462,1.983,0.553,0.455,0.456,0.746,1.561,0.464,0.459,0.468,0.465,0.656,0.544)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(41,16,1,1,1,10,15,1,21,10,8,21,27,19,127,20,51,18,1,5,76,13,1,2,23,52,1,1,4,1,19,9)
IntDen<-c(14.525,13.382,12.749,2.019,5.304,7.761,9.391,10.559,12.116,12.749,12.798,12.725,13.114,12.822,16.082,13.041,14.695,12.773,11.922,11.07,13.941,11.922,11.654,11.63,12.043,14.185,12.506,12.019,12.068,12.554,13.09,13.236)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(597,550,524,83,218,319,386,434,498,524,526,523,539,527,661,536,604,525,490,455,573,490,479,478,495,583,514,494,496,516,538,544)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[37]]<-list(file=file, name=name, value=value)
cat("------------",32,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/03 FRAP3 GFP neurona3_ROI1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[38]]<-list(file=file, name=name, value=value)
cat("------------",33,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/03 FRAP3 GFP neurona3_ROI2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[39]]<-list(file=file, name=name, value=value)
cat("------------",34,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Back"
name<-"GFP/GFP Tertiary Neurite Back/03 FRAP3 GFP neurona3_ROI3 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849,179.849)
Mean<-c(0.326,0.318,0.321,0.198,0.249,0.297,0.328,0.332,0.324,0.32,0.326,0.324,0.346,0.351,0.343,0.334,0.35,0.332,0.316,0.325,0.327,0.325,0.316,0.313,0.289,0.292,0.287,0.319,0.27,0.261,0.253,0.271,0.268,0.285,0.292,0.298,0.283,0.279,0.281,0.251)
StdDev<-c(0.665,0.571,0.531,0.681,0.531,0.607,0.971,0.875,0.631,0.489,0.535,0.607,0.857,1.051,1.157,0.713,1.019,0.717,0.644,0.794,0.968,0.917,0.595,0.903,0.49,0.865,0.71,1.4,0.758,0.561,0.634,0.581,0.495,0.81,0.851,0.914,0.813,0.582,1.031,0.846)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(21,14,12,41,23,23,58,50,26,10,11,16,36,56,77,37,58,37,33,41,59,49,17,55,11,53,28,77,33,19,28,21,10,49,45,39,51,19,46,60)
IntDen<-c(58.636,57.127,57.736,35.595,44.695,53.356,59.025,59.633,58.271,57.468,58.709,58.32,62.164,63.161,61.75,60.12,62.942,59.779,56.908,58.393,58.83,58.441,56.811,56.349,51.994,52.432,51.677,57.419,48.49,46.957,45.522,48.685,48.198,51.264,52.578,53.526,50.899,50.218,50.583,45.181)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(2410,2348,2373,1463,1837,2193,2426,2451,2395,2362,2413,2397,2555,2596,2538,2471,2587,2457,2339,2400,2418,2402,2335,2316,2137,2155,2124,2360,1993,1930,1871,2001,1981,2107,2161,2200,2092,2064,2079,1857)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[40]]<-list(file=file, name=name, value=value)
cat("------------",35,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/01 R-Neurona 1_GFP-ROI1_FRAP terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[41]]<-list(file=file, name=name, value=value)
cat("------------",36,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/01 R-Neurona 1_GFP-ROI2_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[42]]<-list(file=file, name=name, value=value)
cat("------------",37,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/01 R-Neurona 1_GFP-ROI3_FRAP1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32)
Area<-c(70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558,70.558)
Mean<-c(3.869,3.83,3.746,2.929,3.118,3.089,3.392,3.369,3.304,3.304,3.289,3.319,3.288,3.366,3.199,3.356,3.344,3.112,3.208,3.244,3.241,3.341,3.19,3.078,3.138,2.999,2.967,2.995,3.001,2.822,2.622,2.808)
StdDev<-c(9.549,9.587,8.948,6.909,7.276,7.293,7.688,7.393,7.54,7.531,7.369,7.544,7.459,7.818,7.186,7.559,7.518,7.011,7.283,7.217,7.379,7.732,7.384,7.116,7.125,6.912,6.396,6.282,6.429,6.391,5.547,5.9)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(130,97,87,60,64,81,74,54,96,116,83,68,81,65,77,60,79,62,104,72,74,92,71,69,75,75,55,53,72,125,62,42)
IntDen<-c(273.009,270.26,264.299,206.636,219.994,217.974,239.312,237.682,233.156,233.132,232.037,234.154,231.989,237.463,225.687,236.806,235.954,219.58,226.32,228.899,228.655,235.711,225.103,217.171,221.429,211.624,209.313,211.332,211.77,199.143,185.031,198.121)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11221,11108,10863,8493,9042,8959,9836,9769,9583,9582,9537,9624,9535,9760,9276,9733,9698,9025,9302,9408,9398,9688,9252,8926,9101,8698,8603,8686,8704,8185,7605,8143)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[43]]<-list(file=file, name=name, value=value)
cat("------------",38,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/03 FRAP3 GFP neurona3_ROI1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[44]]<-list(file=file, name=name, value=value)
cat("------------",39,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/03 FRAP3 GFP neurona3_ROI2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[45]]<-list(file=file, name=name, value=value)
cat("------------",40,"% progress","------------\n")
file<-"GFP/GFP Tertiary Neurite Control"
name<-"GFP/GFP Tertiary Neurite Control/03 FRAP3 GFP neurona3_ROI3 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284,33.284)
Mean<-c(8.788,8.733,8.799,7.913,7.898,8.133,7.545,7.601,7.515,7.206,7.469,6.917,6.713,6.993,6.697,6.649,6.544,6.728,6.369,6.045,5.829,6.183,5.586,6.081,5.789,5.496,5.355,5.23,5.105,5.136,5.096,4.959,5.164,4.889,4.828,4.404,4.836,4.566,4.659,4.712)
StdDev<-c(14.788,14.356,14.791,12.822,13.056,14.018,13.039,12.9,12.2,12.39,12.631,11.115,11.703,11.734,11.766,11.599,10.933,12.309,11.372,10.633,10.515,10.963,9.973,10.861,10.977,10.708,9.952,9.336,9.162,9.642,9.651,9.275,9.484,8.739,9.13,8.364,9.642,8.311,8.868,9.07)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(90,98,93,72,95,135,104,109,73,83,90,80,81,71,118,77,71,80,128,65,72,85,89,73,86,111,82,63,74,58,76,66,62,55,58,59,103,50,59,68)
IntDen<-c(292.498,290.673,292.863,263.375,262.864,270.698,251.112,252.986,250.115,239.847,248.606,230.237,223.449,232.767,222.889,221.308,217.804,223.935,211.989,201.211,194.009,205.785,185.907,202.403,192.695,182.939,178.243,174.058,169.922,170.944,169.606,165.056,171.869,162.72,160.701,146.59,160.969,151.966,155.056,156.833)
Median<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0)
RawIntDen<-c(12022,11947,12037,10825,10804,11126,10321,10398,10280,9858,10218,9463,9184,9567,9161,9096,8952,9204,8713,8270,7974,8458,7641,8319,7920,7519,7326,7154,6984,7026,6971,6784,7064,6688,6605,6025,6616,6246,6373,6446)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[46]]<-list(file=file, name=name, value=value)
cat("------------",41,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite/01 R-FRAP1- LifeAct Neurona1-ROI5 Dendrita secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397,56.397)
Mean<-c(9.649,9.569,9.761,2.6,3.146,3.497,4.188,4.528,4.679,5.157,5.124,5.596,5.362,5.591,5.684,5.5,5.535,5.425,5.5,5.748,5.723,6.003,5.962,6.157,6.191,6.123,6.318,6.547,6.381,6.415,6.645,6.778,7.124,7.164,6.954)
StdDev<-c(18.084,17.934,18.402,6.065,7.043,7.561,8.998,9.503,9.804,10.704,10.19,10.811,10.719,11.042,10.958,10.235,10.463,10.603,10.212,10.749,10.794,11.338,11.137,11.571,11.552,10.941,11.299,11.757,11.305,11.869,12.116,12.272,12.628,12.802,12.478)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(119,127,131,59,50,53,103,76,87,94,76,72,86,88,78,80,71,80,74,99,75,88,78,134,77,97,84,77,76,143,105,81,119,94,98)
IntDen<-c(544.194,539.668,550.52,146.638,177.44,197.221,236.173,255.394,263.885,290.819,288.994,315.612,302.376,315.344,320.551,310.162,312.157,305.977,310.162,324.2,322.765,338.579,336.244,347.265,349.139,345.319,356.316,369.26,359.892,361.79,374.758,382.276,401.765,404.003,392.203)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1)
RawIntDen<-c(22367,22181,22627,6027,7293,8106,9707,10497,10846,11953,11878,12972,12428,12961,13175,12748,12830,12576,12748,13325,13266,13916,13820,14273,14350,14193,14645,15177,14792,14870,15403,15712,16513,16605,16120)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[47]]<-list(file=file, name=name, value=value)
cat("------------",41,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite/01 R-FRAP1- LifeAct Neurona1-ROI6 Dendrita Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685,38.685)
Mean<-c(11.276,10.825,10.775,2.999,3.921,4.431,4.467,5.315,5.326,5.863,5.764,6.133,5.738,6.193,6.165,6.135,6.161,6.111,6.267,6.23,6.421,6.294,6.626,6.386,6.267,6.476,6.695,6.992,6.653,6.602,6.788,6.814,7.214,7.295,7.179)
StdDev<-c(21.399,20.515,20.215,7.12,8.675,9.91,9.509,11.536,11.391,12.433,12.249,12.485,11.535,12.141,12.524,11.676,11.741,11.854,11.807,11.748,11.835,11.892,12.113,11.893,12.078,12.576,12.593,13.03,12.039,12.772,13.033,12.564,13.422,13.653,13.067)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(164,145,135,61,59,78,68,89,79,77,99,100,93,80,128,81,82,77,72,77,81,99,68,77,84,97,88,94,79,135,88,90,105,81,78)
IntDen<-c(436.216,418.747,416.85,116.006,151.675,171.431,172.817,205.615,206.028,226.806,222.962,237.268,221.989,239.58,238.485,237.341,238.339,236.417,242.426,241.015,248.411,243.472,256.343,247.049,242.426,250.528,258.995,270.479,257.365,255.394,262.596,263.618,279.068,282.206,277.705)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(17929,17211,17133,4768,6234,7046,7103,8451,8468,9322,9164,9752,9124,9847,9802,9755,9796,9717,9964,9906,10210,10007,10536,10154,9964,10297,10645,11117,10578,10497,10793,10835,11470,11599,11414)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[48]]<-list(file=file, name=name, value=value)
cat("------------",42,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite/03 R-FRAP3 neurona3 lifeact-ROI 8 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162,33.162)
Mean<-c(32.278,32.247,31.618,4.684,6.652,9.157,10.398,11.381,13.103,13.26,14.088,15.011,15.848,16.186,16.781,17.817,18.026,18.084,18.57,18.577,19.318,18.844,19.954,19.395,20.18,19.769,20.004,20.406,21.021,20.548,20.357,20.114,22.079)
StdDev<-c(61.512,60.165,60.432,10.575,13.741,18.362,20.424,21.753,24.59,24.749,26.062,27.171,28.875,29.667,30.523,32.464,33.09,32.272,32.64,32.335,34.527,33.784,35.963,34.346,36.05,36.847,36.925,38.753,40.62,40.035,38.965,38.806,42.159)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,85,117,118,128,181,164,157,178,177,224,194,220,215,228,234,228,216,230,224,227,219,213,250,242,255,255,255,247,246,255)
IntDen<-c(1070.408,1069.361,1048.535,155.324,220.602,303.665,344.832,377.41,434.538,439.72,467.189,497.796,525.557,536.749,556.481,590.859,597.769,599.691,615.822,616.041,640.639,624.921,661.709,643.193,669.202,655.578,663.363,676.696,697.109,681.416,675.09,667.013,732.169)
Median<-c(1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(43995,43952,43096,6384,9067,12481,14173,15512,17860,18073,19202,20460,21601,22061,22872,24285,24569,24648,25311,25320,26331,25685,27197,26436,27505,26945,27265,27813,28652,28007,27747,27415,30093)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[49]]<-list(file=file, name=name, value=value)
cat("------------",43,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite/03 R-FRAP3 neurona3 lifeact-ROI 9 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458,51.458)
Mean<-c(17.817,17.339,17.502,3.843,4.951,5.697,6.573,7.294,7.4,8.049,8.41,8.401,8.974,9.248,9.002,9.441,9.439,9.087,9.467,9.723,9.868,9.859,9.73,9.855,10.035,10.232,10.465,9.96,10.42,10.198,10.534,10.477,10.772)
StdDev<-c(43.701,42.303,44.037,10.982,12.922,14.108,16.631,18.585,18.475,19.421,19.674,19.549,21.014,20.911,21.053,22.091,22.243,21.134,22.477,22.917,22.317,23.715,22.946,23.198,23.717,24.489,25.207,23.813,25.467,25.304,26.096,25.783,27.446)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,121,97,106,133,158,156,148,135,143,187,144,181,167,189,160,183,182,139,198,143,182,171,169,179,165,184,187,186,235,240)
IntDen<-c(916.835,892.238,900.607,197.732,254.762,293.179,338.214,375.342,380.768,414.198,432.786,432.299,461.763,475.899,463.223,485.826,485.728,467.627,487.164,500.326,507.772,507.309,500.691,507.115,516.409,526.506,538.525,512.516,536.189,524.778,542.077,539.109,554.291)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(37683,36672,37016,8127,10471,12050,13901,15427,15650,17024,17788,17768,18979,19560,19039,19968,19964,19220,20023,20564,20870,20851,20579,20843,21225,21640,22134,21065,22038,21569,22280,22158,22782)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[50]]<-list(file=file, name=name, value=value)
cat("------------",44,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Back"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Back/01 R-FRAP1- LifeAct Neurona1-ROI5 Dendrita secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[51]]<-list(file=file, name=name, value=value)
cat("------------",45,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Back"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Back/01 R-FRAP1- LifeAct Neurona1-ROI6 Dendrita Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[52]]<-list(file=file, name=name, value=value)
cat("------------",46,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Back"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Back/03 R-FRAP3 neurona3 lifeact-ROI 8 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[53]]<-list(file=file, name=name, value=value)
cat("------------",47,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Back"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Back/03 R-FRAP3 neurona3 lifeact-ROI 9 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[54]]<-list(file=file, name=name, value=value)
cat("------------",48,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Control"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Control/01 R-FRAP1- LifeAct Neurona1-ROI5 Dendrita secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[55]]<-list(file=file, name=name, value=value)
cat("------------",49,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Control"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Control/01 R-FRAP1- LifeAct Neurona1-ROI6 Dendrita Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[56]]<-list(file=file, name=name, value=value)
cat("------------",50,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Control"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Control/03 R-FRAP3 neurona3 lifeact-ROI 8 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[57]]<-list(file=file, name=name, value=value)
cat("------------",50,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Dendrite Control"
name<-"LIFEACT GFP/LIFEACT GFP Dendrite Control/03 R-FRAP3 neurona3 lifeact-ROI 9 Dendrita.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[58]]<-list(file=file, name=name, value=value)
cat("------------",51,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Primary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Primary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 3 terminal.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399,85.399)
Mean<-c(6.704,6.705,6.538,1.311,1.78,2.099,2.497,2.626,2.725,3.038,2.989,3.142,3.23,3.515,3.409,3.453,3.425,3.509,3.52,3.536,3.647,3.703,3.532,3.426,3.664,3.731,3.644,3.604,3.355,3.59,3.536,3.569,3.428)
StdDev<-c(22.459,22.817,22.045,5.051,6.641,7.423,8.463,9.099,9.315,10.245,10.163,10.232,11.092,11.051,10.981,11.269,11.257,11.472,11.34,11.623,11.813,12.15,11.647,11.226,12.226,12.275,12.295,12.057,11.209,12.162,12.177,12.039,11.761)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(241,255,255,63,93,99,102,126,115,131,118,132,149,103,123,118,123,136,138,132,126,157,140,130,154,142,161,123,147,147,177,181,161)
IntDen<-c(572.539,572.612,558.378,111.919,152.039,179.241,213.254,224.276,232.718,259.433,255.297,268.338,275.856,300.137,291.135,294.906,292.449,299.7,300.624,301.938,311.451,316.268,301.597,292.595,312.935,318.604,311.232,307.777,286.513,306.609,301.938,304.809,292.765)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(23532,23535,22950,4600,6249,7367,8765,9218,9565,10663,10493,11029,11338,12336,11966,12121,12020,12318,12356,12410,12801,12999,12396,12026,12862,13095,12792,12650,11776,12602,12410,12528,12033)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[59]]<-list(file=file, name=name, value=value)
cat("------------",52,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Primary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Primary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 3 terminal.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[60]]<-list(file=file, name=name, value=value)
cat("------------",53,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Primary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Primary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 3 terminal.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[61]]<-list(file=file, name=name, value=value)
cat("------------",54,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/01 R-FRAP1- LifeAct Neurona1-ROI1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057,36.057)
Mean<-c(7.271,7.277,6.74,2.735,3.386,3.607,3.91,4.024,4.106,3.872,4.075,3.976,3.796,4.141,3.902,4.146,4.275,3.868,4.061,4.214,4.113,3.859,3.976,4.181,4.258,3.936,3.918,4.138,3.94,4.68,4.508,4.401,4.566,4.482,4.702)
StdDev<-c(16.443,16.972,15.366,7.208,8.288,8.985,9.619,10.095,10.494,9.396,9.998,9.395,9.337,9.507,9.404,9.68,10.118,8.978,9.238,9.705,9.016,8.915,8.91,9.142,9.906,8.843,8.726,9.209,9.2,9.799,9.564,9.621,10.091,9.996,9.973)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(123,138,116,63,62,80,75,79,90,66,82,77,101,74,74,85,110,81,71,121,65,74,65,63,127,69,74,64,94,65,84,76,81,83,71)
IntDen<-c(262.158,262.401,243.01,98.61,122.089,130.045,140.994,145.081,148.049,139.631,146.93,143.354,136.857,149.315,140.702,149.485,154.156,139.461,146.444,151.942,148.317,139.144,143.378,150.75,153.524,141.918,141.261,149.193,142.064,168.754,162.55,158.706,164.643,161.626,169.533)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(10775,10785,9988,4053,5018,5345,5795,5963,6085,5739,6039,5892,5625,6137,5783,6144,6336,5732,6019,6245,6096,5719,5893,6196,6310,5833,5806,6132,5839,6936,6681,6523,6767,6643,6968)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[62]]<-list(file=file, name=name, value=value)
cat("------------",55,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/02 R-FRAP1- LifeAct Neurona1-ROI 1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437,20.437)
Mean<-c(16.248,15.885,15.711,5.733,7.701,7.295,7.936,8.115,8.521,8.367,8.214,8.564,8.318,8.344,8.548,8.63,8.435,8.58,8.642,8.958,8.231,8.327,9.123,9.255,8.677,9.707,8.901,9.457,9.363,9.26,8.977,9.394,9.612,9.496,9.283,9.827,10.269,10.76,10.902,10.633)
StdDev<-c(34.124,34.765,32.629,13.934,17.593,16.994,18.027,18.198,18.798,18.049,17.88,18.641,18.85,18.515,19.093,19.01,18.078,18.972,19.392,19.508,17.697,17.793,20.212,19.825,18.817,20.631,19.197,20.076,19.671,19.7,19.938,20.252,20.266,19.808,19.684,20.847,21.959,22.116,22.519,21.214)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(195,235,198,100,159,121,118,123,129,141,163,139,139,138,149,141,121,150,152,167,126,131,155,139,124,144,182,118,161,192,166,158,150,148,154,157,146,156,155,130)
IntDen<-c(332.059,324.638,321.086,117.174,157.392,149.096,162.185,165.859,174.156,170.993,167.878,175.032,169.995,170.53,174.691,176.37,172.38,175.348,176.613,183.085,168.219,170.19,186.442,189.143,177.343,198.389,181.917,193.279,191.357,189.24,183.474,191.99,196.442,194.082,189.727,200.846,209.872,219.896,222.816,217.317)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,1,1,1,1,1)
RawIntDen<-c(13648,13343,13197,4816,6469,6128,6666,6817,7158,7028,6900,7194,6987,7009,7180,7249,7085,7207,7259,7525,6914,6995,7663,7774,7289,8154,7477,7944,7865,7778,7541,7891,8074,7977,7798,8255,8626,9038,9158,8932)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[63]]<-list(file=file, name=name, value=value)
cat("------------",56,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/02 R-FRAP1- LifeAct Neurona1-ROI 2 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552,23.552)
Mean<-c(13.023,13.501,13.061,5.909,6.027,6.73,6.885,6.945,6.289,6.996,6.759,7.413,7.424,7.255,7.646,7.427,7.346,7.786,7.791,7.722,8.046,7.84,7.409,8.009,8.221,7.968,7.648,8.207,8.135,8.077,8.354,8.723,8.432,8.388,8.384,8.348,9.023,9.507,9.275,9.343)
StdDev<-c(31.199,32.855,32.347,16.603,15.54,17.163,18.427,18.217,16.174,18.573,17.62,18.299,17.958,18.602,18.441,19.275,18.234,19.122,19.239,18.863,18.792,19.299,18.347,20.161,20.156,19.148,18.456,19.677,20.296,19.67,21.435,21.525,19.604,21.319,20.381,21.987,23.008,24.169,23.588,24.262)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(213,255,239,146,131,151,188,154,135,186,158,133,153,142,119,130,136,145,149,146,150,149,164,197,148,162,149,171,160,159,179,188,152,162,177,181,211,187,201,186)
IntDen<-c(306.707,317.972,307.607,139.169,141.942,158.511,162.161,163.572,148.122,164.764,159.193,174.594,174.837,170.871,180.068,174.91,173.012,183.377,183.498,181.868,189.508,184.642,174.496,188.632,193.62,187.659,180.117,193.279,191.6,190.238,196.758,205.444,198.583,197.561,197.464,196.612,212.5,223.911,218.437,220.042)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0)
RawIntDen<-c(12606,13069,12643,5720,5834,6515,6665,6723,6088,6772,6543,7176,7186,7023,7401,7189,7111,7537,7542,7475,7789,7589,7172,7753,7958,7713,7403,7944,7875,7819,8087,8444,8162,8120,8116,8081,8734,9203,8978,9044)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[64]]<-list(file=file, name=name, value=value)
cat("------------",57,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/02 R-FRAP1- LifeAct Neurona1-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011,45.011)
Mean<-c(14.801,14.965,14.968,3.369,4.751,5.791,6.277,6.896,7.272,7.632,7.865,8.271,8.381,8.506,8.676,9.002,9.153,9.009,8.915,9.263,8.788,9.229,9.355,9.494,9.683,9.647,9.297,9.809,9.79,9.237,10.147,9.868,10.185,10.156,10.769,10.761,10.616,11.023,11.311,11.179)
StdDev<-c(39.439,39.292,39.532,10.054,13.385,16.334,17.665,19.227,20.374,21.264,22.14,23.505,23.473,23.689,24.016,24.567,24.467,24.765,24.29,25.236,24.387,25.361,25.57,26.072,27.036,26.918,25.523,26.97,27.434,24.574,27.404,27.402,28.457,28.794,29.453,30.206,29.91,32.088,32.257,31.563)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,98,118,181,163,150,204,180,184,209,172,191,216,206,204,174,227,179,195,182,191,255,197,196,228,216,210,187,204,220,230,237,218,255,233,252,247,252)
IntDen<-c(666.21,673.606,673.704,151.626,213.838,260.674,282.522,310.381,327.314,343.543,354.029,372.277,377.24,382.885,390.5,405.171,411.983,405.487,401.254,416.923,395.561,415.39,421.059,427.312,435.851,434.221,418.455,441.52,440.644,415.779,456.727,444.148,458.454,457.116,484.706,484.366,477.845,496.166,509.134,503.173)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(27382,27686,27690,6232,8789,10714,11612,12757,13453,14120,14551,15301,15505,15737,16050,16653,16933,16666,16492,17136,16258,17073,17306,17563,17914,17847,17199,18147,18111,17089,18772,18255,18843,18788,19922,19908,19640,20393,20926,20681)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[65]]<-list(file=file, name=name, value=value)
cat("------------",58,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/02 R-FRAP1- LifeAct Neurona1-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765,35.765)
Mean<-c(23.603,23.885,24.689,4.442,6.469,7.865,9.278,10.312,11.11,11.573,12.621,13.291,13.394,14.256,14.854,14.724,15.29,15.876,15.469,15.724,15.904,16.276,15.911,16.608,16.369,16.414,16.729,16.573,16.336,16.597,17.193,17.341,17.718,17.522,18.07,17.827,18.11,19.079,19.116,19.367)
StdDev<-c(55.86,56.013,57.628,11.54,16.347,19.52,22.906,24.465,27.085,27.698,30.675,31.831,32.464,34.256,35.433,35.127,37.19,38.321,37.927,37.823,37.975,39.276,38.592,40.358,39.405,40.339,39.955,39.576,39.144,39.319,41.442,40.614,41.622,40.579,41.954,40.854,41.261,43.549,41.883,41.887)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,88,151,161,181,174,204,193,216,230,232,215,219,240,255,250,255,255,249,255,234,255,244,255,238,255,252,254,255,244,255,253,244,255,250,255,246,245)
IntDen<-c(844.161,854.258,883.016,158.876,231.38,281.282,331.815,368.797,397.337,413.93,451.398,475.364,479.038,509.864,531.274,526.603,546.87,567.794,553.245,562.369,568.816,582.125,569.059,593.998,585.434,587.064,598.304,592.733,584.266,593.608,614.922,620.226,633.68,626.698,646.283,637.573,647.695,682.365,683.679,692.681)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(34696,35111,36293,6530,9510,11561,13638,15158,16331,17013,18553,19538,19689,20956,21836,21644,22477,23337,22739,23114,23379,23926,23389,24414,24062,24129,24591,24362,24014,24398,25274,25492,26045,25758,26563,26205,26621,28046,28100,28470)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[66]]<-list(file=file, name=name, value=value)
cat("------------",59,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/02 R-FRAP1- LifeAct Neurona1-ROI 9 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612)
Mean<-c(16.149,16.606,16.839,3.237,4.764,5.81,6.073,6.848,6.98,7.32,8.048,7.906,7.934,8.786,8.56,8.994,9.231,9.19,9.12,9.327,9.298,9.71,9.625,9.739,9.732,9.435,9.691,9.911,10.382,10.185,10.345,10.79,10.911,10.701,11.26,10.87,11.343,11.332,11.344,11.304)
StdDev<-c(37.379,38.624,39.952,8.379,12.147,14.808,14.653,16.825,17.209,17.827,20.31,18.321,19.034,20.499,20.077,21.232,21.852,22.22,22.131,22.091,22.145,23.752,22.456,22.383,22.835,23.246,22.877,22.879,24.924,24.253,24.552,25.621,26.381,26.383,26.904,26.048,26.088,26.955,27.953,26.489)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(238,255,255,70,114,130,108,128,114,117,167,140,127,131,144,159,173,181,179,187,197,203,163,165,158,205,155,161,217,177,173,168,171,212,185,192,167,177,208,187)
IntDen<-c(462.055,475.145,481.811,92.625,136.298,166.224,173.766,195.931,199.727,209.434,230.261,226.222,227.001,251.38,244.908,257.341,264.129,262.961,260.942,266.854,266.051,277.827,275.394,278.654,278.459,269.968,277.291,283.569,297.048,291.403,296.001,308.726,312.181,306.171,322.181,311.013,324.541,324.249,324.589,323.421)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,1)
RawIntDen<-c(18991,19529,19803,3807,5602,6832,7142,8053,8209,8608,9464,9298,9330,10332,10066,10577,10856,10808,10725,10968,10935,11419,11319,11453,11445,11096,11397,11655,12209,11977,12166,12689,12831,12584,13242,12783,13339,13327,13341,13293)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[67]]<-list(file=file, name=name, value=value)
cat("------------",59,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 10 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806,48.806)
Mean<-c(41.804,41.532,40.948,3.523,4.624,5.82,6.439,6.933,8.107,8.552,9.016,9.504,9.981,10.423,11.366,11.897,12.097,12.425,13.29,13.179,13.553,14.356,14.444,14.541,15.453,15.613,15.957,16.788,16.733,17.701,17.046,17.814,18.211)
StdDev<-c(74.947,75.051,74.592,8.572,10.535,12.81,13.97,14.531,17.085,17.212,18.62,19.577,19.965,21.117,23.163,24.267,24.142,25.023,26.763,26.326,27.62,28.174,28.469,28.757,30.913,31.509,32.772,35.229,34.704,37.288,36.525,37.744,37.995)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,83,83,94,119,116,141,131,129,168,154,160,160,184,180,166,180,200,213,217,178,231,208,222,225,224,224,241,249,255,246)
IntDen<-c(2040.283,2027.047,1998.532,171.966,225.687,284.031,314.273,338.36,395.658,417.409,440.036,463.88,487.139,508.696,554.753,580.64,590.421,606.431,648.619,643.242,661.465,700.661,704.968,709.688,754.188,761.998,778.786,819.344,816.668,863.941,831.947,869.416,888.831)
Median<-c(1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(83858,83314,82142,7068,9276,11674,12917,13907,16262,17156,18086,19066,20022,20908,22801,23865,24267,24925,26659,26438,27187,28798,28975,29169,30998,31319,32009,33676,33566,35509,34194,35734,36532)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[68]]<-list(file=file, name=name, value=value)
cat("------------",60,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 11 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223,28.223)
Mean<-c(36.257,36.125,35.994,9.546,11.944,13.527,14.54,15.768,16.297,16.722,16.725,17.155,18.274,19.19,19.505,19.622,19.278,20.129,20.62,20.072,20.04,20.797,20.631,22.006,21.964,21.929,22.691,22.471,22.877,22.927,22.633,22.415,23.175)
StdDev<-c(67.439,67.131,67.34,22.644,26.993,29.061,29.562,32.273,32.853,33.124,33.031,33.01,34.136,35.833,37.301,37.67,36.058,38.134,38.341,37.78,38.385,39.053,39.416,42.51,42.209,41.713,44.45,44.455,46.138,46.463,46.219,46.849,48.093)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,199,181,250,198,191,255,201,225,188,193,200,218,217,216,243,200,237,253,233,219,226,241,239,247,255,255,255,253,255,255)
IntDen<-c(1023.28,1019.558,1015.859,269.408,337.095,381.765,410.353,445.024,459.938,471.933,472.03,484.171,515.752,541.591,550.495,553.804,544.072,568.11,581.954,566.505,565.58,586.966,582.271,621.077,619.885,618.912,640.42,634.191,645.651,647.062,638.765,632.61,654.069)
Median<-c(2,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(42058,41905,41753,11073,13855,15691,16866,18291,18904,19397,19401,19900,21198,22260,22626,22762,22362,23350,23919,23284,23246,24125,23932,25527,25478,25438,26322,26066,26537,26595,26254,26001,26883)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[69]]<-list(file=file, name=name, value=value)
cat("------------",61,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 4 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75,61.75)
Mean<-c(15.237,14.671,14.441,3.918,5.157,5.732,6.214,6.721,7.022,7.356,7.593,7.708,7.853,7.933,8.371,8.454,8.193,8.051,8.5,8.025,8.529,8.286,8.515,8.578,8.753,9.016,9.253,9.233,9.498,9.313,9.214,9.606,9.08)
StdDev<-c(42.863,41.431,40.883,13.072,15.839,17.041,18.354,19.391,19.856,20.638,20.864,21.27,21.164,21.289,22.698,23.05,21.759,21.797,22.342,21.232,21.744,21.991,23.111,22.934,23.325,24.206,25.299,25.077,26.253,25.741,25.836,27.78,25.932)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,141,144,157,167,160,158,215,164,197,174,149,197,195,165,161,160,174,193,165,194,175,188,180,207,222,190,208,209,222,195)
IntDen<-c(940.898,905.935,891.702,241.915,318.434,353.956,383.736,415.049,433.613,454.221,468.892,475.948,484.901,489.84,516.92,522.005,505.922,497.164,524.851,495.533,526.676,511.689,525.776,529.669,540.52,556.748,571.395,570.154,586.48,575.069,568.986,593.146,560.69)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(38672,37235,36650,9943,13088,14548,15772,17059,17822,18669,19272,19562,19930,20133,21246,21455,20794,20434,21572,20367,21647,21031,21610,21770,22216,22883,23485,23434,24105,23636,23386,24379,23045)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[70]]<-list(file=file, name=name, value=value)
cat("------------",62,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612,28.612)
Mean<-c(32.051,31.998,31.095,2.989,3.816,4.78,5.394,5.52,6.375,6.433,6.715,7.037,7.401,8.114,8.352,8.645,9.17,8.582,9.405,8.792,9.386,9.909,9.549,10.189,10.847,10.602,10.458,10.758,11.514,11.443,12.044,11.685,11.904)
StdDev<-c(61.294,60.538,58.706,7.747,9.201,10.671,11.778,11.615,13.179,14.104,13.316,13.903,14.807,15.164,16.172,16.088,17.341,16.908,17.976,17.054,17.769,18.45,18.231,19.377,19.65,19.9,19.748,21.229,23.059,22.444,23.376,22.855,24.378)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,64,75,87,85,87,98,148,100,94,141,102,144,107,120,144,147,134,120,144,182,166,141,149,118,176,205,139,146,151,157)
IntDen<-c(917.054,915.546,889.707,85.521,109.194,136.76,154.327,157.927,182.404,184.058,192.136,201.332,211.746,232.159,238.971,247.341,262.377,245.54,269.092,251.55,268.557,283.52,273.228,291.525,310.356,303.349,299.237,307.802,329.455,327.412,344.613,334.321,340.599)
Median<-c(1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(37692,37630,36568,3515,4488,5621,6343,6491,7497,7565,7897,8275,8703,9542,9822,10166,10784,10092,11060,10339,11038,11653,11230,11982,12756,12468,12299,12651,13541,13457,14164,13741,13999)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[71]]<-list(file=file, name=name, value=value)
cat("------------",63,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547,25.547)
Mean<-c(32.378,31.351,32.094,5.503,6.16,6.997,6.956,7.992,7.801,8.697,8.586,9.762,9.888,10.142,10.288,11.359,11.166,11.45,11.444,12.367,12.263,12.245,13.375,13.116,13.07,13.93,13.443,14.815,14.943,15.853,15.65,16.057,16.11)
StdDev<-c(61.474,58.731,60.97,14.334,16.111,16.678,16.302,19.285,18.869,20.094,20.459,21.356,21.773,22.748,21.593,24.672,23.931,24.927,25.286,26.656,26.362,25.817,29.323,28.575,29.134,29.88,29.507,32.85,31.783,35.549,35.407,35.084,36.27)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,127,204,145,135,155,214,168,198,188,231,223,175,199,213,238,191,210,221,236,255,247,246,229,255,255,255,255,255,255,255)
IntDen<-c(827.154,800.926,819.904,140.58,157.368,178.754,177.708,204.179,199.289,222.183,219.337,249.385,252.596,259.092,262.815,290.186,285.247,292.498,292.352,315.928,313.276,312.814,341.693,335.076,333.883,355.854,343.421,378.481,381.741,405.001,399.794,410.207,411.57)
Median<-c(3,3,3,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(33997,32919,33699,5778,6468,7347,7304,8392,8191,9132,9015,10250,10382,10649,10802,11927,11724,12022,12016,12985,12876,12857,14044,13772,13723,14626,14115,15556,15690,16646,16432,16860,16916)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[72]]<-list(file=file, name=name, value=value)
cat("------------",64,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 7 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852,59.852)
Mean<-c(20.925,20.515,20.724,3.897,4.469,5.225,5.767,6.065,6.589,6.772,7.032,7.221,7.575,7.876,8.162,8.285,8.386,8.967,8.821,9.197,9.348,9.618,9.849,10.168,10.524,10.347,10.415,10.87,11.467,11.473,11.636,11.783,11.88)
StdDev<-c(52.802,52.029,52.238,10.944,12.401,13.984,14.809,15.229,16.993,16.771,17.49,17.688,18.524,19.153,19.578,20.52,20.255,21.402,21.428,22.154,22.637,23.451,24.281,24.408,25.172,24.809,25.268,26.781,28.519,28.705,29.56,30.075,31.124)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,80,113,119,145,129,178,124,174,185,157,188,157,157,163,142,171,183,153,191,214,211,182,211,186,185,249,205,241,227,237)
IntDen<-c(1252.398,1227.897,1240.403,233.254,267.462,312.716,345.197,363.031,394.368,405.317,420.888,432.202,453.369,471.374,488.526,495.85,501.908,536.724,527.941,550.471,559.473,575.677,589.497,608.596,629.885,619.277,623.389,650.614,686.331,686.696,696.452,705.236,711.075)
Median<-c(1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,1,1,1,1,0,0,1)
RawIntDen<-c(51475,50468,50982,9587,10993,12853,14188,14921,16209,16659,17299,17764,18634,19374,20079,20380,20629,22060,21699,22625,22995,23661,24229,25014,25889,25453,25622,26741,28209,28224,28625,28986,29226)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[73]]<-list(file=file, name=name, value=value)
cat("------------",65,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/01 R-FRAP1- LifeAct Neurona1-ROI1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[74]]<-list(file=file, name=name, value=value)
cat("------------",66,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/02 R-FRAP1- LifeAct Neurona1-ROI 1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103)
Mean<-c(0.147,0.139,0.147,0.04,0.069,0.1,0.112,0.131,0.141,0.154,0.181,0.189,0.197,0.215,0.229,0.237,0.239,0.246,0.239,0.245,0.241,0.246,0.255,0.254,0.262,0.276,0.265,0.267,0.266,0.274,0.269,0.269,0.275,0.271,0.268,0.263,0.289,0.293,0.288,0.304)
StdDev<-c(0.743,0.65,0.672,0.542,0.568,0.943,0.736,0.612,0.739,0.64,0.712,0.689,0.649,0.774,0.699,0.836,0.86,0.786,0.736,0.692,0.728,0.78,0.748,0.65,0.578,0.711,0.65,0.766,0.611,0.697,0.745,0.738,0.859,0.845,0.78,0.73,0.807,0.89,0.701,0.918)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(45,38,51,44,44,86,65,36,50,33,42,66,43,55,45,67,72,50,41,31,38,50,58,42,22,45,40,54,35,48,61,45,66,68,74,39,44,61,43,77)
IntDen<-c(111.676,105.398,111.359,30.218,52.748,75.91,84.693,99.267,106.883,117.101,137.612,143.792,149.387,163.523,173.791,179.533,181.309,186.734,181.065,186.004,182.744,186.807,193.255,192.89,198.681,209.313,201.405,202.646,201.819,208.315,204.52,203.912,208.607,206.077,203.595,199.362,219.75,222.11,218.607,230.553)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4590,4332,4577,1242,2168,3120,3481,4080,4393,4813,5656,5910,6140,6721,7143,7379,7452,7675,7442,7645,7511,7678,7943,7928,8166,8603,8278,8329,8295,8562,8406,8381,8574,8470,8368,8194,9032,9129,8985,9476)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[75]]<-list(file=file, name=name, value=value)
cat("------------",67,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/02 R-FRAP1- LifeAct Neurona1-ROI 2 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103)
Mean<-c(0.147,0.139,0.147,0.04,0.069,0.1,0.112,0.131,0.141,0.154,0.181,0.189,0.197,0.215,0.229,0.237,0.239,0.246,0.239,0.245,0.241,0.246,0.255,0.254,0.262,0.276,0.265,0.267,0.266,0.274,0.269,0.269,0.275,0.271,0.268,0.263,0.289,0.293,0.288,0.304)
StdDev<-c(0.743,0.65,0.672,0.542,0.568,0.943,0.736,0.612,0.739,0.64,0.712,0.689,0.649,0.774,0.699,0.836,0.86,0.786,0.736,0.692,0.728,0.78,0.748,0.65,0.578,0.711,0.65,0.766,0.611,0.697,0.745,0.738,0.859,0.845,0.78,0.73,0.807,0.89,0.701,0.918)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(45,38,51,44,44,86,65,36,50,33,42,66,43,55,45,67,72,50,41,31,38,50,58,42,22,45,40,54,35,48,61,45,66,68,74,39,44,61,43,77)
IntDen<-c(111.676,105.398,111.359,30.218,52.748,75.91,84.693,99.267,106.883,117.101,137.612,143.792,149.387,163.523,173.791,179.533,181.309,186.734,181.065,186.004,182.744,186.807,193.255,192.89,198.681,209.313,201.405,202.646,201.819,208.315,204.52,203.912,208.607,206.077,203.595,199.362,219.75,222.11,218.607,230.553)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4590,4332,4577,1242,2168,3120,3481,4080,4393,4813,5656,5910,6140,6721,7143,7379,7452,7675,7442,7645,7511,7678,7943,7928,8166,8603,8278,8329,8295,8562,8406,8381,8574,8470,8368,8194,9032,9129,8985,9476)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[76]]<-list(file=file, name=name, value=value)
cat("------------",68,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/02 R-FRAP1- LifeAct Neurona1-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103)
Mean<-c(0.147,0.139,0.147,0.04,0.069,0.1,0.112,0.131,0.141,0.154,0.181,0.189,0.197,0.215,0.229,0.237,0.239,0.246,0.239,0.245,0.241,0.246,0.255,0.254,0.262,0.276,0.265,0.267,0.266,0.274,0.269,0.269,0.275,0.271,0.268,0.263,0.289,0.293,0.288,0.304)
StdDev<-c(0.743,0.65,0.672,0.542,0.568,0.943,0.736,0.612,0.739,0.64,0.712,0.689,0.649,0.774,0.699,0.836,0.86,0.786,0.736,0.692,0.728,0.78,0.748,0.65,0.578,0.711,0.65,0.766,0.611,0.697,0.745,0.738,0.859,0.845,0.78,0.73,0.807,0.89,0.701,0.918)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(45,38,51,44,44,86,65,36,50,33,42,66,43,55,45,67,72,50,41,31,38,50,58,42,22,45,40,54,35,48,61,45,66,68,74,39,44,61,43,77)
IntDen<-c(111.676,105.398,111.359,30.218,52.748,75.91,84.693,99.267,106.883,117.101,137.612,143.792,149.387,163.523,173.791,179.533,181.309,186.734,181.065,186.004,182.744,186.807,193.255,192.89,198.681,209.313,201.405,202.646,201.819,208.315,204.52,203.912,208.607,206.077,203.595,199.362,219.75,222.11,218.607,230.553)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4590,4332,4577,1242,2168,3120,3481,4080,4393,4813,5656,5910,6140,6721,7143,7379,7452,7675,7442,7645,7511,7678,7943,7928,8166,8603,8278,8329,8295,8562,8406,8381,8574,8470,8368,8194,9032,9129,8985,9476)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[77]]<-list(file=file, name=name, value=value)
cat("------------",68,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/02 R-FRAP1- LifeAct Neurona1-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103)
Mean<-c(0.147,0.139,0.147,0.04,0.069,0.1,0.112,0.131,0.141,0.154,0.181,0.189,0.197,0.215,0.229,0.237,0.239,0.246,0.239,0.245,0.241,0.246,0.255,0.254,0.262,0.276,0.265,0.267,0.266,0.274,0.269,0.269,0.275,0.271,0.268,0.263,0.289,0.293,0.288,0.304)
StdDev<-c(0.743,0.65,0.672,0.542,0.568,0.943,0.736,0.612,0.739,0.64,0.712,0.689,0.649,0.774,0.699,0.836,0.86,0.786,0.736,0.692,0.728,0.78,0.748,0.65,0.578,0.711,0.65,0.766,0.611,0.697,0.745,0.738,0.859,0.845,0.78,0.73,0.807,0.89,0.701,0.918)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(45,38,51,44,44,86,65,36,50,33,42,66,43,55,45,67,72,50,41,31,38,50,58,42,22,45,40,54,35,48,61,45,66,68,74,39,44,61,43,77)
IntDen<-c(111.676,105.398,111.359,30.218,52.748,75.91,84.693,99.267,106.883,117.101,137.612,143.792,149.387,163.523,173.791,179.533,181.309,186.734,181.065,186.004,182.744,186.807,193.255,192.89,198.681,209.313,201.405,202.646,201.819,208.315,204.52,203.912,208.607,206.077,203.595,199.362,219.75,222.11,218.607,230.553)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4590,4332,4577,1242,2168,3120,3481,4080,4393,4813,5656,5910,6140,6721,7143,7379,7452,7675,7442,7645,7511,7678,7943,7928,8166,8603,8278,8329,8295,8562,8406,8381,8574,8470,8368,8194,9032,9129,8985,9476)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[78]]<-list(file=file, name=name, value=value)
cat("------------",69,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/02 R-FRAP1- LifeAct Neurona1-ROI 9 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103,759.103)
Mean<-c(0.147,0.139,0.147,0.04,0.069,0.1,0.112,0.131,0.141,0.154,0.181,0.189,0.197,0.215,0.229,0.237,0.239,0.246,0.239,0.245,0.241,0.246,0.255,0.254,0.262,0.276,0.265,0.267,0.266,0.274,0.269,0.269,0.275,0.271,0.268,0.263,0.289,0.293,0.288,0.304)
StdDev<-c(0.743,0.65,0.672,0.542,0.568,0.943,0.736,0.612,0.739,0.64,0.712,0.689,0.649,0.774,0.699,0.836,0.86,0.786,0.736,0.692,0.728,0.78,0.748,0.65,0.578,0.711,0.65,0.766,0.611,0.697,0.745,0.738,0.859,0.845,0.78,0.73,0.807,0.89,0.701,0.918)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(45,38,51,44,44,86,65,36,50,33,42,66,43,55,45,67,72,50,41,31,38,50,58,42,22,45,40,54,35,48,61,45,66,68,74,39,44,61,43,77)
IntDen<-c(111.676,105.398,111.359,30.218,52.748,75.91,84.693,99.267,106.883,117.101,137.612,143.792,149.387,163.523,173.791,179.533,181.309,186.734,181.065,186.004,182.744,186.807,193.255,192.89,198.681,209.313,201.405,202.646,201.819,208.315,204.52,203.912,208.607,206.077,203.595,199.362,219.75,222.11,218.607,230.553)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(4590,4332,4577,1242,2168,3120,3481,4080,4393,4813,5656,5910,6140,6721,7143,7379,7452,7675,7442,7645,7511,7678,7943,7928,8166,8603,8278,8329,8295,8562,8406,8381,8574,8470,8368,8194,9032,9129,8985,9476)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[79]]<-list(file=file, name=name, value=value)
cat("------------",70,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 10 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[80]]<-list(file=file, name=name, value=value)
cat("------------",71,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 11 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[81]]<-list(file=file, name=name, value=value)
cat("------------",72,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 4 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[82]]<-list(file=file, name=name, value=value)
cat("------------",73,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[83]]<-list(file=file, name=name, value=value)
cat("------------",74,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[84]]<-list(file=file, name=name, value=value)
cat("------------",75,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 7 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[85]]<-list(file=file, name=name, value=value)
cat("------------",76,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/01 R-FRAP1- LifeAct Neurona1-ROI1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[86]]<-list(file=file, name=name, value=value)
cat("------------",77,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/02 R-FRAP1- LifeAct Neurona1-ROI 1 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768)
Mean<-c(43.423,42.758,43.721,43.32,43.298,43.214,42.979,42.364,42.742,42.32,42.076,42.136,41.343,41.402,41.164,41.621,41.621,41.217,40.501,40.23,40.658,39.824,39.768,40.15,39.742,40.049,39.237,39.447,39.224,40.472,39.765,40.534,41.059,41.302,41.514,41.451,41.783,42.396,42.87,42.532)
StdDev<-c(77.839,77.06,77.827,77.97,77.474,77.516,77.462,76.448,76.739,76.007,76.174,75.835,75.132,75.848,75.201,75.536,75.536,74.699,73.95,73.968,74.211,73.879,73.9,74.034,73.442,74.245,72.838,72.919,72.558,74.147,73.605,73.628,74.455,74.947,74.833,74.625,75.425,75.695,75.985,75.518)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255)
IntDen<-c(1943.935,1914.155,1957.293,1939.312,1938.364,1934.592,1924.057,1896.54,1913.474,1894.569,1883.621,1886.321,1850.848,1853.475,1842.819,1863.281,1863.281,1845.179,1813.136,1800.995,1820.167,1782.821,1780.315,1797.419,1779.147,1792.918,1756.544,1765.935,1755.96,1811.822,1780.169,1814.62,1838.099,1848.999,1858.488,1855.665,1870.531,1897.951,1919.191,1904.058)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(79898,78674,80447,79708,79669,79514,79081,77950,78646,77869,77419,77530,76072,76180,75742,76583,76583,75839,74522,74023,74811,73276,73173,73876,73125,73691,72196,72582,72172,74468,73167,74583,75548,75996,76386,76270,76881,78008,78881,78259)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[87]]<-list(file=file, name=name, value=value)
cat("------------",77,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/02 R-FRAP1- LifeAct Neurona1-ROI 2 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768)
Mean<-c(43.423,42.758,43.721,43.32,43.298,43.214,42.979,42.364,42.742,42.32,42.076,42.136,41.343,41.402,41.164,41.621,41.621,41.217,40.501,40.23,40.658,39.824,39.768,40.15,39.742,40.049,39.237,39.447,39.224,40.472,39.765,40.534,41.059,41.302,41.514,41.451,41.783,42.396,42.87,42.532)
StdDev<-c(77.839,77.06,77.827,77.97,77.474,77.516,77.462,76.448,76.739,76.007,76.174,75.835,75.132,75.848,75.201,75.536,75.536,74.699,73.95,73.968,74.211,73.879,73.9,74.034,73.442,74.245,72.838,72.919,72.558,74.147,73.605,73.628,74.455,74.947,74.833,74.625,75.425,75.695,75.985,75.518)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255)
IntDen<-c(1943.935,1914.155,1957.293,1939.312,1938.364,1934.592,1924.057,1896.54,1913.474,1894.569,1883.621,1886.321,1850.848,1853.475,1842.819,1863.281,1863.281,1845.179,1813.136,1800.995,1820.167,1782.821,1780.315,1797.419,1779.147,1792.918,1756.544,1765.935,1755.96,1811.822,1780.169,1814.62,1838.099,1848.999,1858.488,1855.665,1870.531,1897.951,1919.191,1904.058)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(79898,78674,80447,79708,79669,79514,79081,77950,78646,77869,77419,77530,76072,76180,75742,76583,76583,75839,74522,74023,74811,73276,73173,73876,73125,73691,72196,72582,72172,74468,73167,74583,75548,75996,76386,76270,76881,78008,78881,78259)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[88]]<-list(file=file, name=name, value=value)
cat("------------",78,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/02 R-FRAP1- LifeAct Neurona1-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768)
Mean<-c(43.423,42.758,43.721,43.32,43.298,43.214,42.979,42.364,42.742,42.32,42.076,42.136,41.343,41.402,41.164,41.621,41.621,41.217,40.501,40.23,40.658,39.824,39.768,40.15,39.742,40.049,39.237,39.447,39.224,40.472,39.765,40.534,41.059,41.302,41.514,41.451,41.783,42.396,42.87,42.532)
StdDev<-c(77.839,77.06,77.827,77.97,77.474,77.516,77.462,76.448,76.739,76.007,76.174,75.835,75.132,75.848,75.201,75.536,75.536,74.699,73.95,73.968,74.211,73.879,73.9,74.034,73.442,74.245,72.838,72.919,72.558,74.147,73.605,73.628,74.455,74.947,74.833,74.625,75.425,75.695,75.985,75.518)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255)
IntDen<-c(1943.935,1914.155,1957.293,1939.312,1938.364,1934.592,1924.057,1896.54,1913.474,1894.569,1883.621,1886.321,1850.848,1853.475,1842.819,1863.281,1863.281,1845.179,1813.136,1800.995,1820.167,1782.821,1780.315,1797.419,1779.147,1792.918,1756.544,1765.935,1755.96,1811.822,1780.169,1814.62,1838.099,1848.999,1858.488,1855.665,1870.531,1897.951,1919.191,1904.058)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(79898,78674,80447,79708,79669,79514,79081,77950,78646,77869,77419,77530,76072,76180,75742,76583,76583,75839,74522,74023,74811,73276,73173,73876,73125,73691,72196,72582,72172,74468,73167,74583,75548,75996,76386,76270,76881,78008,78881,78259)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[89]]<-list(file=file, name=name, value=value)
cat("------------",79,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/02 R-FRAP1- LifeAct Neurona1-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768)
Mean<-c(43.423,42.758,43.721,43.32,43.298,43.214,42.979,42.364,42.742,42.32,42.076,42.136,41.343,41.402,41.164,41.621,41.621,41.217,40.501,40.23,40.658,39.824,39.768,40.15,39.742,40.049,39.237,39.447,39.224,40.472,39.765,40.534,41.059,41.302,41.514,41.451,41.783,42.396,42.87,42.532)
StdDev<-c(77.839,77.06,77.827,77.97,77.474,77.516,77.462,76.448,76.739,76.007,76.174,75.835,75.132,75.848,75.201,75.536,75.536,74.699,73.95,73.968,74.211,73.879,73.9,74.034,73.442,74.245,72.838,72.919,72.558,74.147,73.605,73.628,74.455,74.947,74.833,74.625,75.425,75.695,75.985,75.518)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255)
IntDen<-c(1943.935,1914.155,1957.293,1939.312,1938.364,1934.592,1924.057,1896.54,1913.474,1894.569,1883.621,1886.321,1850.848,1853.475,1842.819,1863.281,1863.281,1845.179,1813.136,1800.995,1820.167,1782.821,1780.315,1797.419,1779.147,1792.918,1756.544,1765.935,1755.96,1811.822,1780.169,1814.62,1838.099,1848.999,1858.488,1855.665,1870.531,1897.951,1919.191,1904.058)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(79898,78674,80447,79708,79669,79514,79081,77950,78646,77869,77419,77530,76072,76180,75742,76583,76583,75839,74522,74023,74811,73276,73173,73876,73125,73691,72196,72582,72172,74468,73167,74583,75548,75996,76386,76270,76881,78008,78881,78259)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[90]]<-list(file=file, name=name, value=value)
cat("------------",80,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/02 R-FRAP1- LifeAct Neurona1-ROI 9 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40)
Area<-c(44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768,44.768)
Mean<-c(43.423,42.758,43.721,43.32,43.298,43.214,42.979,42.364,42.742,42.32,42.076,42.136,41.343,41.402,41.164,41.621,41.621,41.217,40.501,40.23,40.658,39.824,39.768,40.15,39.742,40.049,39.237,39.447,39.224,40.472,39.765,40.534,41.059,41.302,41.514,41.451,41.783,42.396,42.87,42.532)
StdDev<-c(77.839,77.06,77.827,77.97,77.474,77.516,77.462,76.448,76.739,76.007,76.174,75.835,75.132,75.848,75.201,75.536,75.536,74.699,73.95,73.968,74.211,73.879,73.9,74.034,73.442,74.245,72.838,72.919,72.558,74.147,73.605,73.628,74.455,74.947,74.833,74.625,75.425,75.695,75.985,75.518)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255)
IntDen<-c(1943.935,1914.155,1957.293,1939.312,1938.364,1934.592,1924.057,1896.54,1913.474,1894.569,1883.621,1886.321,1850.848,1853.475,1842.819,1863.281,1863.281,1845.179,1813.136,1800.995,1820.167,1782.821,1780.315,1797.419,1779.147,1792.918,1756.544,1765.935,1755.96,1811.822,1780.169,1814.62,1838.099,1848.999,1858.488,1855.665,1870.531,1897.951,1919.191,1904.058)
Median<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(79898,78674,80447,79708,79669,79514,79081,77950,78646,77869,77419,77530,76072,76180,75742,76583,76583,75839,74522,74023,74811,73276,73173,73876,73125,73691,72196,72582,72172,74468,73167,74583,75548,75996,76386,76270,76881,78008,78881,78259)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[91]]<-list(file=file, name=name, value=value)
cat("------------",81,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 10 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[92]]<-list(file=file, name=name, value=value)
cat("------------",82,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 11 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[93]]<-list(file=file, name=name, value=value)
cat("------------",83,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 4 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[94]]<-list(file=file, name=name, value=value)
cat("------------",84,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 5 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[95]]<-list(file=file, name=name, value=value)
cat("------------",85,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 6 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[96]]<-list(file=file, name=name, value=value)
cat("------------",86,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Secondary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 7 Secundaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[97]]<-list(file=file, name=name, value=value)
cat("------------",86,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite/01 R-FRAP1- LifeAct Neurona1-ROI2 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98,27.98)
Mean<-c(17.249,16.382,16.748,3.852,4.557,5.392,6.005,6.182,6.813,7.233,7.543,7.45,7.601,7.873,7.759,7.91,7.877,7.636,8.196,7.916,7.728,7.961,8.012,8.67,8.261,7.811,7.852,8.297,8.558,8.665,8.507,8.968,9.023,8.994,8.711)
StdDev<-c(27.651,26.646,27.141,8.114,9.029,10.489,11.393,11.438,13.363,13.619,13.208,13.194,13.295,14.005,13.055,13.749,13.258,12.408,13.786,12.927,12.941,12.819,12.852,13.691,12.832,12.857,12.755,13.263,14.091,13.665,13.964,14.001,14.212,14.527,13.974)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(171,160,173,57,57,85,76,68,99,90,89,78,80,124,80,144,71,61,88,95,90,96,99,93,77,82,79,86,83,74,76,100,83,90,111)
IntDen<-c(482.614,458.357,468.6,107.783,127.49,150.872,168.024,172.963,190.627,202.379,211.04,208.437,212.67,220.286,217.098,221.308,220.407,213.644,229.312,221.478,216.223,222.743,224.179,242.572,231.137,218.558,219.702,232.135,239.458,242.451,238.022,250.917,252.45,251.647,243.74)
Median<-c(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,1,1,1,1)
RawIntDen<-c(19836,18839,19260,4430,5240,6201,6906,7109,7835,8318,8674,8567,8741,9054,8923,9096,9059,8781,9425,9103,8887,9155,9214,9970,9500,8983,9030,9541,9842,9965,9783,10313,10376,10343,10018)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[98]]<-list(file=file, name=name, value=value)
cat("------------",87,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite/01 R-FRAP1- LifeAct Neurona1-ROI3 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736,27.736)
Mean<-c(10.335,10.302,10.797,5.314,6.172,6.184,6.475,6.611,6.968,7.015,6.725,6.589,6.495,5.932,6.705,6.089,6.479,6.172,6.288,6.524,6.095,6.636,6.596,6.777,6.549,6.254,6.684,6.76,6.554,6.869,6.575,7.125,6.75,7.094,6.886)
StdDev<-c(19.133,18.948,20.096,11.307,12.843,12.308,12.894,13.714,14.969,14.329,14.216,13.626,12.897,12.258,13.014,11.882,12.775,12.187,12.032,12.541,11.807,11.928,12.399,12.918,12.047,11.628,12.484,12.14,12.027,12.308,12.082,13.221,12.433,12.888,12.699)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(126,109,122,93,96,98,86,80,152,88,117,80,115,92,85,88,82,121,81,89,79,82,76,115,79,73,88,74,72,69,72,145,80,72,84)
IntDen<-c(286.659,285.734,299.481,147.392,171.187,171.528,179.606,183.352,193.279,194.569,186.515,182.744,180.141,164.521,185.98,168.876,179.703,171.187,174.399,180.944,169.046,184.058,182.963,187.975,181.649,173.45,185.396,187.489,181.771,190.53,182.379,197.634,187.221,196.758,190.992)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(11782,11744,12309,6058,7036,7050,7382,7536,7944,7997,7666,7511,7404,6762,7644,6941,7386,7036,7168,7437,6948,7565,7520,7726,7466,7129,7620,7706,7471,7831,7496,8123,7695,8087,7850)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[99]]<-list(file=file, name=name, value=value)
cat("------------",88,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite/01 R-FRAP1- LifeAct Neurona1-ROI4 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342,39.342)
Mean<-c(14.881,14.349,14.233,4.935,5.642,5.764,6.785,7.025,6.845,7.208,7.049,7.061,7.44,6.706,7.142,6.808,6.982,6.868,7.02,7.006,7.092,7.143,7.335,7.152,7.263,7.565,7.331,7.956,7.623,7.635,7.881,8.375,7.944,8.245,8.125)
StdDev<-c(30.18,28.219,28.605,11.369,12.8,12.464,14.288,14.807,15.088,15.214,14.295,15.064,15.002,14.399,14.306,13.655,13.623,13.39,13.474,13.825,13.514,13.814,14.004,13.973,13.386,14.367,13.56,14.603,14.762,14.534,14.777,15.43,15.391,15.557,15.577)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(230,167,182,87,102,89,114,110,120,102,97,111,110,114,96,142,88,91,103,98,105,125,103,105,76,93,80,84,133,99,113,105,121,104,99)
IntDen<-c(585.434,564.51,559.935,194.155,221.965,226.782,266.951,276.367,269.311,283.569,277.316,277.778,292.717,263.812,280.99,267.851,274.688,270.211,276.197,275.637,278.995,281.014,288.581,281.379,285.734,297.607,288.435,313.008,299.894,300.356,310.064,329.48,312.546,324.37,319.65)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0)
RawIntDen<-c(24062,23202,23014,7980,9123,9321,10972,11359,11069,11655,11398,11417,12031,10843,11549,11009,11290,11106,11352,11329,11467,11550,11861,11565,11744,12232,11855,12865,12326,12345,12744,13542,12846,13332,13138)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[100]]<-list(file=file, name=name, value=value)
cat("------------",89,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817,24.817)
Mean<-c(12.037,11.698,11.685,3.409,4.429,5.147,5.673,6.17,6.38,6.476,6.616,7.348,7.159,7.562,7.525,7.56,7.292,7.044,8.384,7.765,7.484,7.389,7.839,7.684,7.981,7.483,7.974,7.64,7.579,7.461,7.324,7.368,7.254)
StdDev<-c(26.542,25.758,26.103,9.239,11.325,12.085,13.243,13.579,13.935,14.583,14.277,14.962,15.302,16.094,16.037,15.909,15.749,14.928,18.147,17.002,15.92,15.438,16.024,17.574,17.138,16.647,17.585,17.671,16.744,17.092,17.37,16.643,16.617)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(217,206,239,87,116,141,98,121,129,131,125,105,129,152,130,136,161,115,159,160,146,139,102,167,127,186,116,209,156,142,152,160,148)
IntDen<-c(298.726,290.308,289.992,84.596,109.924,127.734,140.775,153.11,158.341,160.725,164.18,182.355,177.659,187.659,186.759,187.61,180.968,174.813,208.072,192.695,185.737,183.377,194.544,190.7,198.072,185.713,197.878,189.605,188.097,185.153,181.747,182.842,180.019)
Median<-c(1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(12278,11932,11919,3477,4518,5250,5786,6293,6508,6606,6748,7495,7302,7713,7676,7711,7438,7185,8552,7920,7634,7537,7996,7838,8141,7633,8133,7793,7731,7610,7470,7515,7399)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[101]]<-list(file=file, name=name, value=value)
cat("------------",90,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite/03 R-FRAP3 neurona3 lifeact-ROI 2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104,37.104)
Mean<-c(19.248,19.315,18.675,4.99,5.904,7.178,8.563,8.892,9.292,9.9,10.106,10.675,10.807,10.955,11.176,11.468,11.914,11.962,11.748,12.275,11.929,11.787,11.693,11.955,12.365,12.454,11.991,12.25,12.67,11.748,12.561,11.797,12.652)
StdDev<-c(38.942,39.761,37.812,12.316,13.525,16.527,18.747,18.675,19.345,20.329,20.789,21.894,21.718,21.968,22.901,22.863,24.548,24.366,23.476,25.412,23.641,23.325,24.45,24.189,25.528,25.029,24.744,25.74,25.876,24.632,25.454,24.517,26.225)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,246,245,100,84,120,115,141,128,154,163,166,156,122,151,187,167,143,144,175,161,129,184,153,201,150,156,191,154,175,182,161,172)
IntDen<-c(714.165,716.671,692.9,185.129,219.069,266.343,317.704,329.942,344.759,367.313,374.977,396.096,400.962,406.461,414.684,425.511,442.056,443.832,435.9,455.462,442.615,437.336,433.856,443.588,458.77,462.079,444.927,454.537,470.084,435.9,466.045,437.701,469.427)
Median<-c(1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(29353,29456,28479,7609,9004,10947,13058,13561,14170,15097,15412,16280,16480,16706,17044,17489,18169,18242,17916,18720,18192,17975,17832,18232,18856,18992,18287,18682,19321,17916,19155,17990,19294)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[102]]<-list(file=file, name=name, value=value)
cat("------------",91,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back/01 R-FRAP1- LifeAct Neurona1-ROI2 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[103]]<-list(file=file, name=name, value=value)
cat("------------",92,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back/01 R-FRAP1- LifeAct Neurona1-ROI3 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[104]]<-list(file=file, name=name, value=value)
cat("------------",93,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back/01 R-FRAP1- LifeAct Neurona1-ROI4 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735,1184.735)
Mean<-c(0.017,0.025,0.019,0.078,0.038,0.025,0.017,0.018,0.02,0.022,0.03,0.026,0.035,0.041,0.041,0.042,0.049,0.052,0.061,0.059,0.063,0.084,0.089,0.109,0.114,0.116,0.135,0.141,0.15,0.151,0.167,0.179,0.189,0.195,0.202)
StdDev<-c(0.418,0.712,0.525,0.639,0.54,0.656,0.476,0.566,0.459,0.452,0.651,0.587,0.635,0.613,0.689,0.571,0.678,0.743,0.689,0.561,0.499,0.665,0.522,0.722,0.587,0.573,0.778,0.636,0.641,0.651,0.751,0.78,0.692,0.655,0.789)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(35,59,50,62,44,70,60,51,36,36,66,70,63,60,84,49,75,81,71,42,44,57,42,57,43,57,77,51,62,47,52,95,47,37,76)
IntDen<-c(20.072,29.123,22.116,92.601,45.449,29.95,19.926,21.094,23.722,25.571,35.546,31.021,42.018,48.806,48.271,49.828,57.979,61.993,71.993,69.463,74.061,99.097,105.35,129.291,134.814,137.855,160.409,166.881,177.489,179.046,197.732,212.232,223.814,230.894,238.777)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(825,1197,909,3806,1868,1231,819,867,975,1051,1461,1275,1727,2006,1984,2048,2383,2548,2959,2855,3044,4073,4330,5314,5541,5666,6593,6859,7295,7359,8127,8723,9199,9490,9814)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[105]]<-list(file=file, name=name, value=value)
cat("------------",94,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[106]]<-list(file=file, name=name, value=value)
cat("------------",95,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Back/03 R-FRAP3 neurona3 lifeact-ROI 2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494,2063.494)
Mean<-c(0.331,0.331,0.336,0.057,0.115,0.146,0.178,0.205,0.21,0.244,0.254,0.28,0.281,0.294,0.298,0.303,0.31,0.312,0.317,0.322,0.322,0.322,0.332,0.331,0.332,0.335,0.338,0.336,0.337,0.337,0.332,0.335,0.333)
StdDev<-c(0.74,0.674,0.794,0.656,0.669,0.592,0.721,0.745,0.62,0.842,0.74,0.792,0.715,0.741,0.759,0.755,0.742,0.79,0.71,0.737,0.788,0.779,0.769,0.784,0.7,0.71,0.774,0.809,0.786,0.75,0.678,0.723,0.709)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(51,41,69,77,58,45,61,60,52,79,69,84,52,76,91,61,56,59,72,44,57,65,62,70,68,60,56,59,62,65,55,51,45)
IntDen<-c(682.657,683.703,692.34,117.637,236.782,300.94,367.046,424.027,434.027,503.879,524.73,578.329,579.789,606.212,614.727,625.603,639.617,642.926,654.191,663.655,664.993,664.555,685.893,683.314,685.528,691.221,697.669,693.289,696.16,695.333,685.139,690.516,687.28)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
RawIntDen<-c(28058,28101,28456,4835,9732,12369,15086,17428,17839,20710,21567,23770,23830,24916,25266,25713,26289,26425,26888,27277,27332,27314,28191,28085,28176,28410,28675,28495,28613,28579,28160,28381,28248)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[107]]<-list(file=file, name=name, value=value)
cat("------------",95,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control/01 R-FRAP1- LifeAct Neurona1-ROI2 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[108]]<-list(file=file, name=name, value=value)
cat("------------",96,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control/01 R-FRAP1- LifeAct Neurona1-ROI3 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[109]]<-list(file=file, name=name, value=value)
cat("------------",97,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control/01 R-FRAP1- LifeAct Neurona1-ROI4 Terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35)
Area<-c(59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171,59.171)
Mean<-c(17.826,16.635,17.061,17.726,17.413,17.521,16.791,17.755,17.424,17.124,16.493,16.616,15.873,15.474,15.3,14.781,14.608,14.609,14.504,14.605,14.073,14.77,14.475,14.415,14.172,14.261,14.701,14.512,14.527,14.421,14.544,14.342,15.135,15.17,15.29)
StdDev<-c(33.588,31.322,32.765,34.102,33.843,33.218,32.796,34.153,34.188,32.815,31.292,31.59,29.465,28.489,27.805,26.878,26.408,26.493,25.86,26.273,24.772,25.78,25.741,25.844,24.896,25.543,26.261,25.649,25.958,25.904,26.011,25.628,26.952,27.631,27.671)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(220,200,244,201,223,191,198,205,212,233,193,224,186,211,174,173,192,195,165,164,162,140,181,165,156,151,147,156,183,163,177,178,189,186,173)
IntDen<-c(1054.788,984.327,1009.509,1048.875,1030.336,1036.71,993.524,1050.603,1031.017,1013.256,975.909,983.208,939.195,915.619,905.303,874.598,864.355,864.452,858.224,864.185,832.701,873.941,856.472,852.969,838.565,843.845,869.854,858.662,859.586,853.285,860.56,848.638,895.546,897.639,904.743)
Median<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1)
RawIntDen<-c(43353,40457,41492,43110,42348,42610,40835,43181,42376,41646,40111,40411,38602,37633,37209,35947,35526,35530,35274,35519,34225,35920,35202,35058,34466,34683,35752,35292,35330,35071,35370,34880,36808,36894,37186)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[110]]<-list(file=file, name=name, value=value)
cat("------------",98,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 1 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[111]]<-list(file=file, name=name, value=value)
cat("------------",99,"% progress","------------\n")
file<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control"
name<-"LIFEACT GFP/LIFEACT GFP Tertiary Neurite Control/03 R-FRAP3 neurona3 lifeact-ROI 2 terciaria.csv"
X<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33)
Area<-c(42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237,42.237)
Mean<-c(30.787,30.561,29.76,26.097,26.163,25.086,25.226,24.695,24.23,24.469,24.552,23.714,23.036,23.593,22.77,22.847,22.235,22.764,21.885,22.202,22.109,22.067,21.428,21.24,21.454,21.046,20.589,20.546,20.588,20.916,20.812,20.465,20.04)
StdDev<-c(63.5,63.04,61.339,53.99,54.718,50.96,51.159,50.161,49.428,48.457,48.708,45.985,45.358,46.098,44.692,45.35,43.49,44.397,42.368,43.373,43.207,43.491,42.094,41.013,41.837,41.628,41.266,41.49,42.27,42.807,42.406,41.999,41.606)
Min<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
Max<-c(255,255,255,255,255,255,255,255,255,250,251,240,234,254,242,247,242,249,235,232,221,240,221,232,227,231,211,253,246,222,235,255,212)
IntDen<-c(1300.352,1290.815,1256.996,1102.28,1105.054,1059.556,1065.493,1043.06,1023.402,1033.523,1037.027,1001.626,972.965,996.517,961.725,964.985,939.146,961.506,924.378,937.759,933.842,932.066,905.06,897.104,906.179,888.904,869.635,867.786,869.562,883.43,879.026,864.404,846.448)
Median<-c(1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
RawIntDen<-c(53446,53054,51664,45305,45419,43549,43793,42871,42063,42479,42623,41168,39990,40958,39528,39662,38600,39519,37993,38543,38382,38309,37199,36872,37245,36535,35743,35667,35740,36310,36129,35528,34790)
value<-data.frame(X,Area,Mean,StdDev,Min,Max,IntDen,Median,RawIntDen)
lista[[112]]<-list(file=file, name=name, value=value)
itz::csv.printer(lista, basis="data" )
test.fraping()
cat("-----------",100,"% progress","------------\n")
cat("---------- Download Completed ---------\n")
}

#' Example script for FRAP analysis
#'
#' This function provides an example script for FRAP (Fluorescence Recovery After Photobleaching) analysis using the 'fraping' package.
#'
#' @details The `test.fraping` function demonstrates how to perform FRAP analysis on a set of data using the 'fraping' package. It includes the necessary steps to load the required libraries, set plot parameters, create data objects, add directories, and generate plots.
#'
#' @examples
#' \dontrun{
#'   # Run the example script
#'   test.fraping()
#' }
#'
#' @export
test.fraping<-function(){
  write(paste(# Load required libraries\n",
"library(itz) # Load the 'itz' library for statistical analysis\n",
"library(fraping) # Load the 'fraping' library for FRAP analysis\n",
"\n",
"# Set default plot values\n",
"Plot(\"\\\\default.values\") # Set default plot values to the predefined values\n",
"Plot(\"col.background\", \"#eeeeee\") # Set the background color for plots to light gray\n",
"Plot(\"col.backlines\", \"#ffffff\") # Set the color of the background grid lines to white\n",
"\n",
"# Define fit objects\n",
"Exp <- newFit(\"Exponential\", pexp, \"rate\", list(c(0, 1))) # Create an exponential fit object with a rate parameter between 0 and 1\n",
"Gam <- newFit(\"Gamma\", pgamma, c(\"shape\", \"rate\"), list(c(0, 5), c(0, 5))) # Create a gamma fit object with shape and rate parameters between 0 and 5\n",
"Wei <- newFit(\"Weibull\", pweibull, c(\"shape\", \"scale\"), list(c(0, 5), c(0, 2000))) # Create a Weibull fit object with shape and scale parameters\n",
"\n",
"# Create FRAP objects\n",
"A <- newDataFrap(\"Neurites\", 80) # Create a new FRAP object named \"Neurites\" with 80 time points\n",
"B <- newDataFrap(\"Secondary.Neurites\", 80) # Create a new FRAP object named \"Secondary.Neurites\" with 80 time points\n",
"C <- newDataFrap(\"Dendrites\", 80) # Create a new FRAP object named \"Dendrites\" with 80 time points\n",
"\n",
"# Add directories to FRAP objects\n",
"A$addDir(\n",
"  480, 4, \"Area\", \"IntDen\",\n",
"  \"data/GFP/GFP Secondary Neurite\",\n",
"  \"data/GFP/GFP Secondary Neurite Control\",\n",
"  \"data/GFP/GFP Secondary Neurite Back\"\n",
") # Add directories containing FRAP data for \"Neurites\" FRAP object\n",
"\n",
"B$addDir(\n",
"  480, 4, \"Area\", \"IntDen\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Control\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Back\"\n",
") # Add directories containing FRAP data for \"Secondary.Neurites\" FRAP object\n",
"\n",
"C$addDir(\n",
"  480, 4, \"Area\", \"IntDen\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Dendrite\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Dendrite Control\",\n",
"  \"data/LIFEACT GFP/LIFEACT GFP Dendrite Back\"\n",
") # Add directories containing FRAP data for \"Dendrites\" FRAP object\n",
"\n",
"# Set colors for FRAP objects\n",
"A$setColor(\"#05753D\") # Set the color of the \"Neurites\" FRAP object to green\n",
"B$setColor(\"#0000A6\") # Set the color of the \"Secondary.Neurites\" FRAP object to blue\n",
"C$setColor(\"#E014F7\") # Set the color of the \"Dendrites\" FRAP object to purple\n",
"\n",
"# Create printer object for exporting plots\n",
"pnt <- printer(\"img/test fraping\") # Create a printer object for exporting plots to a specified directory\n",
"pnt$OFF() # Disable plot export for now (use pnt$ON() to enable export)\n",
"\n",
"# Export plots to PDF files\n",
"\n",
"# Plot 001\n",
"pnt$PDF(\"001.pdf\", PLOTS = {\n",
"  plotRecover(B, C, plot.lines = TRUE, ylim = c(0.2, 1.2)) # Plot FRAP recovery curves for \"Secondary.Neurites\" and \"Dendrites\" with connecting lines\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites\", \"Dendrites\"), col.lin = c(B$getColor(), C$getColor())) # Add a legend with color-coded labels for the FRAP objects\n",
"})\n",
"\n",
"# Plot 002\n",
"pnt$PDF(\"002.pdf\", PLOTS = {\n",
"  plotRecover(A, plot.lines = TRUE, ylim = c(0.1, 1.2)) # Plot FRAP recovery curve for \"Neurites\" with connecting lines\n",
"  mlegend(\"bottomright\", \"Neurites\", col.lin = A$getColor()) # Add a legend with a color-coded label for the FRAP object\n",
"})\n",
"\n",
"# Plot 003\n",
"pnt$PDF(\"003.pdf\", PLOTS = {\n",
"  col <- c(\"darkgray\", \"red\", \"green\", \"blue\") # Define colors for different plot elements\n",
"  plotFit(B, B, B, B, fit = l(NULL, Exp, Gam, Wei), index = 11, plot.lines = c(FALSE, TRUE, TRUE, TRUE), plot.points = c(TRUE, FALSE, FALSE, FALSE), lwd.points = 2, lwd.lines = 2, lty.lines = 2, col = col, ylim = c(0.2, 0.8), xdigits = 0) # Plot multiple fits and data points for \"Secondary.Neurites\" with different line styles and colors\n",
"  mlegend(\"bottomright\", c(\"F. recovering\", \"Exponential fit\", \"Gamma fit\", \"Weibull fit\"), col.lin = col, lin = c(FALSE, TRUE, TRUE, TRUE), lwd.lin = 2, lty.lin = 2) # Add a legend with labels and corresponding colors for the fits and data points\n",
"})\n",
"\n",
"# Plot 004\n",
"pnt$PDF(\"004.pdf\", PLOTS = {\n",
"  plotFit(B, C, fit = Wei, plot.lines = TRUE, ylim = c(0.2, 1), xdigits = 0) # Plot Weibull fits for \"Secondary.Neurites\" and \"Dendrites\" with connecting lines\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites\", \"Dendrites\"), col.lin = c(B$getColor(), C$getColor())) # Add a legend with color-coded labels for the FRAP objects\n",
"})\n",
"\n",
"# Plot 005\n",
"pnt$PDF(\"005.pdf\", PLOTS = {\n",
"  plotFit(B, C, fit = Wei, plot.shadow = TRUE, plot.mean = TRUE, lwd.mean = 2, alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0) # Plot Weibull fits for \"Secondary.Neurites\" and \"Dendrites\" with shaded regions and mean curves\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites mean\", \"Dendrites mean\"), col.lin = c(B$getColor(), C$getColor()), lwd.lin = 2) # Add a legend with color-coded labels for the mean curves\n",
"})\n",
"\n",
"# Plot 006\n",
"pnt$PDF(\"006.pdf\", PLOTS = {\n",
"  compareMean(B, C, fit = Wei, lwd.lines = 2, lty.lines = c(2, 1), ylim = c(0, .2), col.lines = c(\"red\", \"#C65153\"), xdigits = 0, ydigits = 4) # Compare means of \"Secondary.Neurites\" and \"Dendrites\" using Weibull fits and display significance levels\n",
"  mlegend(\"topright\", c(\"Significance value = 5%\", \"P-value\"), col.lin = c(\"red\", \"#C65153\"), lwd.lin = 2, lty.lin = c(2, 1)) # Add a legend with labels and corresponding colors for significance levels\n",
"})\n",
"\n",
"# Plot 007\n",
"pnt$PDF(\"007.pdf\", PLOTS = {\n",
"  seed <- c(41876, 31522) # Set random seed values for simulation\n",
"  compareMean(B, C, fit = Wei, lwd.lines = 2, lty.lines = c(2, 1), ylim = c(0, .2), col.lines = c(\"red\", \"#C65153\"), xdigits = 0, ydigits = 4) # Compare means of \"Secondary.Neurites\" and \"Dendrites\" using Weibull fits and display significance levels\n",
"  compareMean(B, C, fit = Wei, lwd.lines = 2, col.lines = \"#79189F\", simulated = TRUE, seed = seed, new.plot = FALSE) # Compare means using simulated data and overlay on the same plot\n",
"  mlegend(\"topright\", c(\"Significance value = 5%\", \"Non-simulated p-value\", \"Simulated p-value\"), col.lin = c(\"red\", \"#C65153\", \"#79189F\"), lwd.lin = 2, lty.lin = c(2, 1, 1)) # Add a legend with labels and corresponding colors for significance levels\n",
"})\n",
"\n",
"# Plot 008\n",
"pnt$PDF(\"008.pdf\", PLOTS = {\n",
"  plotFit(B, C, fit = Wei, plot.lines = TRUE, plot.mean = TRUE, lwd.mean = 2, alp.lines = .2, simulated = TRUE, seed = seed, xdigits = 0, ylim = c(0.2, 1), displacement = TRUE) # Plot Weibull fits with mean curves for \"Secondary.Neurites\" and \"Dendrites\" with displacement and overlay simulated data\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites mean\", \"Secondary Neurites sim\", \"Dendrites mean\", \"Dendrites sim\"), col.lin = c(B$getColor(), B$getColor(), C$getColor(), C$getColor()), lwd.lin = c(2, 1), alp.lin = c(1, 0.2)) # Add a legend with color-coded labels for the mean curves and simulated curves\n",
"})\n",
"\n",
"# Plot 009\n",
"pnt$PDF(\"009.pdf\", prop = 1, width = 9 * 3 / 4, PLOTS = {\n",
"  compareParam(B, C, fit = Wei, param = \"MF\", lwd.lines = 2, lty.lines = c(2, 1), col.lines = c(\"red\", \"#C65153\"), ydigits = 4, xdigits = 4) # Compare parameter \"MF\" of Weibull fits for \"Secondary.Neurites\" and \"Dendrites\" and display Q-Q plots\n",
"  mlegend(\"bottomright\", c(\"Reference line\", \"Q-Q line\"), col.lin = c(\"red\", \"#C65153\"), lwd.lin = 2, lty.lin = c(2, 1)) # Add a legend with labels and corresponding colors for reference line and Q-Q line\n",
"})\n",
"\n",
"# Plot 010\n",
"pnt$PDF(\"010.pdf\", prop = 1, width = 9 * 3 / 4, PLOTS = {\n",
"  compareParam(B, C, fit = Wei, param = \"MF\", lwd.lines = 2, lty.lines = c(2, 1), col.lines = c(\"red\", \"#C65153\"), ydigits = 4, xdigits = 4) # Compare parameter \"MF\" of Weibull fits for \"Secondary.Neurites\" and \"Dendrites\" and display Q-Q plots\n",
"  compareParam(B, C, fit = Wei, param = \"MF\", lwd.lines = 2, col.lines = \"#79189F\", simulated = TRUE, seed = seed, new.plot = FALSE) # Compare parameter \"MF\" using simulated data and overlay on the same plot\n",
"  mlegend(\"bottomright\", c(\"Reference line\", \"Non-simulated Q-Q line\", \"Simulated Q-Q line\"), col.lin = c(\"red\", \"#C65153\", \"#79189F\"), lwd.lin = 2, lty.lin = c(2, 1, 1)) # Add a legend with labels and corresponding colors for reference line and Q-Q lines\n",
"})\n",
"\n",
"# Plot 011\n",
"pnt$PDF(\"011.pdf\", prop = 1, PLOTS = {\n",
"  compareFit(B, fit = l(Exp, Gam, Wei), col.lines = c(\"red\", \"yellow\"), lty.lines = c(2, 1), lwd.lines = 2, lwd.mean = 2) # Compare different fits (Exponential, Gamma, Weibull) for \"Secondary.Neurites\" and display mean curves\n",
"})\n",
"\n",
"# Plot 012\n",
"pnt$PDF(\"012.pdf\", PLOTS = {\n",
"  D <- newDataFrap(\"noName\", 80) # Create a new FRAP object with no name and 80 time points\n",
"  D$addDir(\n",
"    480, 4, \"Area\", \"IntDen\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Control\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Back\"\n",
"  ) # Add directories containing FRAP data for the new FRAP object\n",
"\n",
"  D$addDir(\n",
"    480, 4, \"Area\", \"IntDen\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Dendrite\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Dendrite Control\",\n",
"    \"data/LIFEACT GFP/LIFEACT GFP Dendrite Back\"\n",
"  ) # Add directories containing FRAP data for the new FRAP object\n",
"\n",
"  colMin <- color(\"blue\") # Set the minimum color for plotting\n",
"  colMax <- color(\"red\") # Set the maximum color for plotting\n",
"  area <- D$area() # Get the area values from the FRAP object\n",
"  areaMin <- min(area) # Get the minimum area value\n",
"  areaMax <- max(area) # Get the maximum area value\n",
"  r <- reparam(area, c(areaMin, areaMax), c(0, 1)) # Rescale the area values to a range of 0 to 1\n",
"  D$setColor(colMin$combine(colMax, r)) # Assign colors to the FRAP object based on the rescaled area values\n",
"  nScale <- 1000 # Set the number of color scales\n",
"  sqScale <- seq(areaMin, areaMax, (areaMax - areaMin) / (nScale - 1)) # Generate a sequence of area values for color scale\n",
"  colScale <- colMin$sequence(colMax, nScale) # Generate a color scale based on the minimum and maximum colors\n",
"  xfig <- c(0.75, 0.9215) # Set x-axis figure coordinates\n",
"  yfig <- c(0.0451, 0.6) # Set y-axis figure coordinates\n",
"\n",
"  plotRecover(D, type = \"R\", plot.lines = TRUE, area = FALSE, stand = FALSE, AB = TRUE, xdigits = 0, ydigits = 0) # Plot FRAP recovery curves for the new FRAP object with connecting lines\n",
"  subgraphic(\n",
"    {\n",
"      mplot(xaxis = FALSE, yaxis = FALSE, col.background = \"#E6E6E6\", alp.background = 0.8, lwd.backlines = 0, alp.border = 1, col.border = \"#BEBEBE\") # Add a subgraph with custom background color and border\n",
"    },\n",
"    xfig = xfig,\n",
"    yfig = yfig,\n",
"    xmar = c(2, 0),\n",
"    ymar = c(4.5, 1)\n",
"  )\n",
"  subgraphic(\n",
"    {\n",
"      mplot(ylim = c(min(area), max(area)), ydigits = 0, lwd.backlines = 0, yaxis.pos = FALSE, main = \"Area Size\", alp.background = 0, xaxis = FALSE, cex.axis = 1, cex.main = .8) # Add a subgraph with custom y-axis limits and labels\n",
"      mabline(h = sqScale, col.abline = colScale, alp.abline = 0.8) # Add horizontal lines with color scale\n",
"    },\n",
"    xfig = xfig,\n",
"    yfig = yfig\n",
"  )\n",
"})\n",
"\n",
"# Plot 013\n",
"pnt$PDF(\"013.pdf\", PLOTS = {\n",
"  xfig <- c(0.75, 0.9215) # Set x-axis figure coordinates\n",
"  yfig <- c(0.058, 0.6) # Set y-axis figure coordinates\n",
"  plotRecover(D, type = \"R\", plot.lines = TRUE, area = TRUE, stand = FALSE, AB = TRUE, xdigits = 0, ydigits = 0) # Plot FRAP recovery curves for the new FRAP object with connecting lines and area plot\n",
"  subgraphic(\n",
"    {\n",
"      mplot(xaxis = FALSE, yaxis = FALSE, col.background = \"#E6E6E6\", alp.background = 0.8, lwd.backlines = 0, alp.border = 1, col.border = \"#BEBEBE\") # Add a subgraph with custom background color and border\n",
"    },\n",
"    xfig = xfig,\n",
"    yfig = yfig,\n",
"    xmar = c(2, 0),\n",
"    ymar = c(4.5, 1)\n",
"  )\n",
"  subgraphic(\n",
"    {\n",
"      mplot(ylim = c(min(area), max(area)), ydigits = 0, lwd.backlines = 0, yaxis.pos = FALSE, main = \"Area Size\", alp.background = 0, xaxis = FALSE, cex.axis = 1, cex.main = .8) # Add a subgraph with custom y-axis limits and labels\n",
"      mabline(h = sqScale, col.abline = colScale, alp.abline = 0.8) # Add horizontal lines with color scale\n",
"    },\n",
"    xfig = xfig,\n",
"    yfig = yfig\n",
"  )\n",
"})\n",
"\n",
"cls <- color.rainbow(3) # Generate a color palette with 3 colors\n",
"\n",
"# Plot 014\n",
"pnt$PDF(\"014.pdf\", PLOTS = {\n",
"  plotFit(B, B, fit = l(NULL, Exp), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = 1992) # Plot exponential fit for \"Secondary.Neurites\" with mean curve, simulated data, and shading\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites mean\", \"Exponential simulation\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\n",
"})\n",
"\n",
"# Plot 015\n",
"pnt$PDF(\"015.pdf\", PLOTS = {\n",
"  plotFit(B, B, fit = l(NULL, Gam), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = 1992) # Plot gamma fit for \"Secondary.Neurites\" with mean curve, simulated data, and shading\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites mean\", \"Gamma simulation\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\n",
"})\n",
"\n",
"# Plot 016\n",
"pnt$PDF(\"016.pdf\", PLOTS = {\n",
"  plotFit(B, B, fit = l(NULL, Wei), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = seed) # Plot Weibull fit for \"Secondary.Neurites\" with mean curve, simulated data, and shading\n",
"  mlegend(\"bottomright\", c(\"Secondary Neurites mean\", \"Weibull simulation\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\n",
"})\n", sep=""), file="test.fraping.R")

write(paste("{",
" \"cells\": [",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"library(itz) # Load the 'itz' library for statistical analysis\\n\",",
"    \"library(fraping) # Load the 'fraping' library for FRAP analysis\\n\",",
"    \"\\n\",",
"    \"# Set default plot values\\n\",",
"    \"Plot(\\\"\\\\\\\\default.values\\\") # Set default plot values to the predefined values\\n\",",
"    \"# Set the background color for plots to light gray\\n\",",
"    \"Plot(\\\"col.background\\\", \\\"#eeeeee\\\")\\n\",",
"    \"# Set the color of the background grid lines to white\\n\",",
"    \"Plot(\\\"col.backlines\\\", \\\"#ffffff\\\")\\n\",",
"    \"\\n\",",
"    \"# Define fit objects\\n\",",
"    \"Exp <- newFit(\\\"Exponential\\\", pexp, \\\"rate\\\", list(c(0, 1))) # Create an exponential fit object with a rate parameter between 0 and 1\\n\",",
"    \"Gam <- newFit(\\\"Gamma\\\", pgamma, c(\\\"shape\\\", \\\"rate\\\"), list(c(0, 5), c(0, 5))) # Create a gamma fit object with shape and rate parameters between 0 and 5\\n\",",
"    \"Wei <- newFit(\\\"Weibull\\\", pweibull, c(\\\"shape\\\", \\\"scale\\\"), list(c(0, 5), c(0, 2000))) # Create a Weibull fit object with shape and scale parameters\\n\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"markdown\",",
"   \"metadata\": {},",
"   \"source\": [",
"    \"Create data frap objects\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Create FRAP objects\\n\",",
"    \"A <- newDataFrap(\\\"Neurites\\\", 80) # Create a new FRAP object named \\\"Neurites\\\" with 80 time points\\n\",",
"    \"B <- newDataFrap(\\\"Secondary.Neurites\\\", 80) # Create a new FRAP object named \\\"Secondary.Neurites\\\" with 80 time points\\n\",",
"    \"C <- newDataFrap(\\\"Dendrites\\\", 80) # Create a new FRAP object named \\\"Dendrites\\\" with 80 time points\\n\",",
"    \"\\n\",",
"    \"\\n\",",
"    \"# Add directories to FRAP objects\\n\",",
"    \"A$addDir(\\n\",",
"    \"  480, 4, \\\"Area\\\", \\\"IntDen\\\",\\n\",",
"    \"  \\\"data/GFP/GFP Secondary Neurite\\\",\\n\",",
"    \"  \\\"data/GFP/GFP Secondary Neurite Control\\\",\\n\",",
"    \"  \\\"data/GFP/GFP Secondary Neurite Back\\\"\\n\",",
"    \") # Add directories containing FRAP data for \\\"Neurites\\\" FRAP object\\n\",",
"    \"\\n\",",
"    \"B$addDir(\\n\",",
"    \"  480, 4, \\\"Area\\\", \\\"IntDen\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Control\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Back\\\"\\n\",",
"    \") # Add directories containing FRAP data for \\\"Secondary.Neurites\\\" FRAP object\\n\",",
"    \"\\n\",",
"    \"C$addDir(\\n\",",
"    \"  480, 4, \\\"Area\\\", \\\"IntDen\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite Control\\\",\\n\",",
"    \"  \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite Back\\\"\\n\",",
"    \") # Add directories containing FRAP data for \\\"Dendrites\\\" FRAP object\\n\",",
"    \"\\n\",",
"    \"# Set colors for FRAP objects\\n\",",
"    \"A$setColor(\\\"#05753D\\\") # Set the color of the \\\"Neurites\\\" FRAP object to green\\n\",",
"    \"B$setColor(\\\"#0000A6\\\") # Set the color of the \\\"Secondary.Neurites\\\" FRAP object to blue\\n\",",
"    \"C$setColor(\\\"#E014F7\\\") # Set the color of the \\\"Dendrites\\\" FRAP object to purple\\n\",",
"    \"\\n\",",
"    \"\\n\",",
"    \"# Create printer object for exporting plots\\n\",",
"    \"pnt <- printer(\\\"img/test fraping\\\") # Create a printer object for exporting plots to a specified directory\\n\",",
"    \"pnt$OFF() # Disable plot export for now (use pnt$ON() to enable export)\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"markdown\",",
"   \"metadata\": {},",
"   \"source\": [",
"    \"Exporting plots\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 001\\n\",",
"    \"pnt$PDF(\\\"001.pdf\\\", PLOTS = {\\n\",",
"    \"  plotRecover(B, C, plot.lines = TRUE, ylim = c(0.2, 1.2)) # Plot FRAP recovery curves for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" with connecting lines\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites\\\", \\\"Dendrites\\\"), col.lin = c(B$getColor(), C$getColor())) # Add a legend with color-coded labels for the FRAP objects\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 002\\n\",",
"    \"pnt$PDF(\\\"002.pdf\\\", PLOTS = {\\n\",",
"    \"  plotRecover(A, plot.lines = TRUE, ylim = c(0.1, 1.2)) # Plot FRAP recovery curve for \\\"Neurites\\\" with connecting lines\\n\",",
"    \"  mlegend(\\\"bottomright\\\", \\\"Neurites\\\", col.lin = A$getColor()) # Add a legend with a color-coded label for the FRAP object\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 003\\n\",",
"    \"pnt$PDF(\\\"003.pdf\\\", PLOTS = {\\n\",",
"    \"  col <- c(\\\"darkgray\\\", \\\"red\\\", \\\"green\\\", \\\"blue\\\") # Define colors for different plot elements\\n\",",
"    \"  plotFit(B, B, B, B, fit = l(NULL, Exp, Gam, Wei), index = 11, plot.lines = c(FALSE, TRUE, TRUE, TRUE), plot.points = c(TRUE, FALSE, FALSE, FALSE), lwd.points = 2, lwd.lines = 2, lty.lines = 2, col = col, ylim = c(0.2, 0.8), xdigits = 0) # Plot multiple fits and data points for \\\"Secondary.Neurites\\\" with different line styles and colors\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"F. recovering\\\", \\\"Exponential fit\\\", \\\"Gamma fit\\\", \\\"Weibull fit\\\"), col.lin = col, lin = c(FALSE, TRUE, TRUE, TRUE), lwd.lin = 2, lty.lin = 2) # Add a legend with labels and corresponding colors for the fits and data points\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 004\\n\",",
"    \"pnt$PDF(\\\"004.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, C, fit = Wei, plot.lines = TRUE, ylim = c(0.2, 1), xdigits = 0) # Plot Weibull fits for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" with connecting lines\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites\\\", \\\"Dendrites\\\"), col.lin = c(B$getColor(), C$getColor())) # Add a legend with color-coded labels for the FRAP objects\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 005\\n\",",
"    \"pnt$PDF(\\\"005.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, C, fit = Wei, plot.shadow = TRUE, plot.mean = TRUE, lwd.mean = 2, alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0) # Plot Weibull fits for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" with shaded regions and mean curves\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites mean\\\", \\\"Dendrites mean\\\"), col.lin = c(B$getColor(), C$getColor()), lwd.lin = 2) # Add a legend with color-coded labels for the mean curves\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 006\\n\",",
"    \"pnt$PDF(\\\"006.pdf\\\", PLOTS = {\\n\",",
"    \"  compareMean(B, C, fit = Wei, lwd.lines = 2, lty.lines = c(2, 1), ylim = c(0, .2), col.lines = c(\\\"red\\\", \\\"#C65153\\\"), xdigits = 0, ydigits = 4) # Compare means of \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" using Weibull fits and display significance levels\\n\",",
"    \"  mlegend(\\\"topright\\\", c(\\\"Significance value = 5%\\\", \\\"P-value\\\"), col.lin = c(\\\"red\\\", \\\"#C65153\\\"), lwd.lin = 2, lty.lin = c(2, 1)) # Add a legend with labels and corresponding colors for significance levels\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 007\\n\",",
"    \"pnt$PDF(\\\"007.pdf\\\", PLOTS = {\\n\",",
"    \"  seed <- c(41876, 31522) # Set random seed values for simulation\\n\",",
"    \"  compareMean(B, C, fit = Wei, lwd.lines = 2, lty.lines = c(2, 1), ylim = c(0, .2), col.lines = c(\\\"red\\\", \\\"#C65153\\\"), xdigits = 0, ydigits = 4) # Compare means of \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" using Weibull fits and display significance levels\\n\",",
"    \"  compareMean(B, C, fit = Wei, lwd.lines = 2, col.lines = \\\"#79189F\\\", simulated = TRUE, seed = seed, new.plot = FALSE) # Compare means using simulated data and overlay on the same plot\\n\",",
"    \"  mlegend(\\\"topright\\\", c(\\\"Significance value = 5%\\\", \\\"Non-simulated p-value\\\", \\\"Simulated p-value\\\"), col.lin = c(\\\"red\\\", \\\"#C65153\\\", \\\"#79189F\\\"), lwd.lin = 2, lty.lin = c(2, 1, 1)) # Add a legend with labels and corresponding colors for significance levels\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 008\\n\",",
"    \"pnt$PDF(\\\"008.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, C, fit = Wei, plot.lines = TRUE, plot.mean = TRUE, lwd.mean = 2, alp.lines = .2, simulated = TRUE, seed = seed, xdigits = 0, ylim = c(0.2, 1), displacement = TRUE) # Plot Weibull fits with mean curves for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" with displacement and overlay simulated data\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites mean\\\", \\\"Secondary Neurites sim\\\", \\\"Dendrites mean\\\", \\\"Dendrites sim\\\"), col.lin = c(B$getColor(), B$getColor(), C$getColor(), C$getColor()), lwd.lin = c(2, 1), alp.lin = c(1, 0.2)) # Add a legend with color-coded labels for the mean curves and simulated curves\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 009\\n\",",
"    \"pnt$PDF(\\\"009.pdf\\\", prop = 1, width = 9 * 3 / 4, PLOTS = {\\n\",",
"    \"  compareParam(B, C, fit = Wei, param = \\\"MF\\\", lwd.lines = 2, lty.lines = c(2, 1), col.lines = c(\\\"red\\\", \\\"#C65153\\\"), ydigits = 4, xdigits = 4) # Compare parameter \\\"MF\\\" of Weibull fits for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" and display Q-Q plots\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Reference line\\\", \\\"Q-Q line\\\"), col.lin = c(\\\"red\\\", \\\"#C65153\\\"), lwd.lin = 2, lty.lin = c(2, 1)) # Add a legend with labels and corresponding colors for reference line and Q-Q line\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 010\\n\",",
"    \"pnt$PDF(\\\"010.pdf\\\", prop = 1, width = 9 * 3 / 4, PLOTS = {\\n\",",
"    \"  compareParam(B, C, fit = Wei, param = \\\"MF\\\", lwd.lines = 2, lty.lines = c(2, 1), col.lines = c(\\\"red\\\", \\\"#C65153\\\"), ydigits = 4, xdigits = 4) # Compare parameter \\\"MF\\\" of Weibull fits for \\\"Secondary.Neurites\\\" and \\\"Dendrites\\\" and display Q-Q plots\\n\",",
"    \"  compareParam(B, C, fit = Wei, param = \\\"MF\\\", lwd.lines = 2, col.lines = \\\"#79189F\\\", simulated = TRUE, seed = seed, new.plot = FALSE) # Compare parameter \\\"MF\\\" using simulated data and overlay on the same plot\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Reference line\\\", \\\"Non-simulated Q-Q line\\\", \\\"Simulated Q-Q line\\\"), col.lin = c(\\\"red\\\", \\\"#C65153\\\", \\\"#79189F\\\"), lwd.lin = 2, lty.lin = c(2, 1, 1)) # Add a legend with labels and corresponding colors for reference line and Q-Q lines\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 011\\n\",",
"    \"pnt$PDF(\\\"011.pdf\\\", prop = 1, PLOTS = {\\n\",",
"    \"  compareFit(B, fit = l(Exp, Gam, Wei), col.lines = c(\\\"red\\\", \\\"yellow\\\"), lty.lines = c(2, 1), lwd.lines = 2, lwd.mean = 2) # Compare different fits (Exponential, Gamma, Weibull) for \\\"Secondary.Neurites\\\" and display mean curves\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 012\\n\",",
"    \"pnt$PDF(\\\"012.pdf\\\", PLOTS = {\\n\",",
"    \"  D <- newDataFrap(\\\"noName\\\", 80) # Create a new FRAP object with no name and 80 time points\\n\",",
"    \"  D$addDir(\\n\",",
"    \"    480, 4, \\\"Area\\\", \\\"IntDen\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Control\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Secondary Neurite Back\\\"\\n\",",
"    \"  ) # Add directories containing FRAP data for the new FRAP object\\n\",",
"    \"\\n\",",
"    \"  D$addDir(\\n\",",
"    \"    480, 4, \\\"Area\\\", \\\"IntDen\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite Control\\\",\\n\",",
"    \"    \\\"data/LIFEACT GFP/LIFEACT GFP Dendrite Back\\\"\\n\",",
"    \"  ) # Add directories containing FRAP data for the new FRAP object\\n\",",
"    \"\\n\",",
"    \"  colMin <- color(\\\"blue\\\") # Set the minimum color for plotting\\n\",",
"    \"  colMax <- color(\\\"red\\\") # Set the maximum color for plotting\\n\",",
"    \"  area <- D$area() # Get the area values from the FRAP object\\n\",",
"    \"  areaMin <- min(area) # Get the minimum area value\\n\",",
"    \"  areaMax <- max(area) # Get the maximum area value\\n\",",
"    \"  r <- reparam(area, c(areaMin, areaMax), c(0, 1)) # Rescale the area values to a range of 0 to 1\\n\",",
"    \"  D$setColor(colMin$combine(colMax, r)) # Assign colors to the FRAP object based on the rescaled area values\\n\",",
"    \"  nScale <- 1000 # Set the number of color scales\\n\",",
"    \"  sqScale <- seq(areaMin, areaMax, (areaMax - areaMin) / (nScale - 1)) # Generate a sequence of area values for color scale\\n\",",
"    \"  colScale <- colMin$sequence(colMax, nScale) # Generate a color scale based on the minimum and maximum colors\\n\",",
"    \"  xfig <- c(0.75, 0.9215) # Set x-axis figure coordinates\\n\",",
"    \"  yfig <- c(0.0451, 0.6) # Set y-axis figure coordinates\\n\",",
"    \"\\n\",",
"    \"  plotRecover(D, type = \\\"R\\\", plot.lines = TRUE, area = FALSE, stand = FALSE, AB = TRUE, xdigits = 0, ydigits = 0) # Plot FRAP recovery curves for the new FRAP object with connecting lines\\n\",",
"    \"  subgraphic(\\n\",",
"    \"    {\\n\",",
"    \"      mplot(xaxis = FALSE, yaxis = FALSE, col.background = \\\"#E6E6E6\\\", alp.background = 0.8, lwd.backlines = 0, alp.border = 1, col.border = \\\"#BEBEBE\\\") # Add a subgraph with custom background color and border\\n\",",
"    \"    },\\n\",",
"    \"    xfig = xfig,\\n\",",
"    \"    yfig = yfig,\\n\",",
"    \"    xmar = c(2, 0),\\n\",",
"    \"    ymar = c(4.5, 1)\\n\",",
"    \"  )\\n\",",
"    \"  subgraphic(\\n\",",
"    \"    {\\n\",",
"    \"      mplot(ylim = c(min(area), max(area)), ydigits = 0, lwd.backlines = 0, yaxis.pos = FALSE, main = \\\"Area Size\\\", alp.background = 0, xaxis = FALSE, cex.axis = 1, cex.main = .8) # Add a subgraph with custom y-axis limits and labels\\n\",",
"    \"      mabline(h = sqScale, col.abline = colScale, alp.abline = 0.8) # Add horizontal lines with color scale\\n\",",
"    \"    },\\n\",",
"    \"    xfig = xfig,\\n\",",
"    \"    yfig = yfig\\n\",",
"    \"  )\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 013\\n\",",
"    \"pnt$PDF(\\\"013.pdf\\\", PLOTS = {\\n\",",
"    \"  xfig <- c(0.75, 0.9215) # Set x-axis figure coordinates\\n\",",
"    \"  yfig <- c(0.058, 0.6) # Set y-axis figure coordinates\\n\",",
"    \"  plotRecover(D, type = \\\"R\\\", plot.lines = TRUE, area = TRUE, stand = FALSE, AB = TRUE, xdigits = 0, ydigits = 0) # Plot FRAP recovery curves for the new FRAP object with connecting lines and area plot\\n\",",
"    \"  subgraphic(\\n\",",
"    \"    {\\n\",",
"    \"      mplot(xaxis = FALSE, yaxis = FALSE, col.background = \\\"#E6E6E6\\\", alp.background = 0.8, lwd.backlines = 0, alp.border = 1, col.border = \\\"#BEBEBE\\\") # Add a subgraph with custom background color and border\\n\",",
"    \"    },\\n\",",
"    \"    xfig = xfig,\\n\",",
"    \"    yfig = yfig,\\n\",",
"    \"    xmar = c(2, 0),\\n\",",
"    \"    ymar = c(4.5, 1)\\n\",",
"    \"  )\\n\",",
"    \"  subgraphic(\\n\",",
"    \"    {\\n\",",
"    \"      mplot(ylim = c(min(area), max(area)), ydigits = 0, lwd.backlines = 0, yaxis.pos = FALSE, main = \\\"Area Size\\\", alp.background = 0, xaxis = FALSE, cex.axis = 1, cex.main = .8) # Add a subgraph with custom y-axis limits and labels\\n\",",
"    \"      mabline(h = sqScale, col.abline = colScale, alp.abline = 0.8) # Add horizontal lines with color scale\\n\",",
"    \"    },\\n\",",
"    \"    xfig = xfig,\\n\",",
"    \"    yfig = yfig\\n\",",
"    \"  )\\n\",",
"    \"})\\n\",",
"    \"\\n\",",
"    \"cls <- color.rainbow(3) # Generate a color palette with 3 colors\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 014\\n\",",
"    \"pnt$PDF(\\\"014.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, B, fit = l(NULL, Exp), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = 1992) # Plot exponential fit for \\\"Secondary.Neurites\\\" with mean curve, simulated data, and shading\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites mean\\\", \\\"Exponential simulation\\\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 015\\n\",",
"    \"pnt$PDF(\\\"015.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, B, fit = l(NULL, Gam), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = 1992) # Plot gamma fit for \\\"Secondary.Neurites\\\" with mean curve, simulated data, and shading\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites mean\\\", \\\"Gamma simulation\\\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\\n\",",
"    \"})\"",
"   ]",
"  },",
"  {",
"   \"cell_type\": \"code\",",
"   \"execution_count\": null,",
"   \"metadata\": {",
"    \"vscode\": {",
"     \"languageId\": \"r\"",
"    }",
"   },",
"   \"outputs\": [],",
"   \"source\": [",
"    \"# Plot 016\\n\",",
"    \"pnt$PDF(\\\"016.pdf\\\", PLOTS = {\\n\",",
"    \"  plotFit(B, B, fit = l(NULL, Wei), col = l(nasd(), cls$getCol(this = TRUE)), plot.lines = c(FALSE, TRUE), plot.mean = c(TRUE, FALSE), lwd.mean = 2, plot.shadow = c(TRUE, FALSE), alp.shadow = .2, ylim = c(0.2, 1), xdigits = 0, simulated = TRUE, seed = seed) # Plot Weibull fit for \\\"Secondary.Neurites\\\" with mean curve, simulated data, and shading\\n\",",
"    \"  mlegend(\\\"bottomright\\\", c(\\\"Secondary Neurites mean\\\", \\\"Weibull simulation\\\"), lwd.lin = c(2, 1), col.lin = c(B$getColor(), cls$getCol())) # Add a legend with color-coded labels for the mean curve and simulated data\\n\",",
"    \"})\"",
"   ]",
"  }",
" ],",
" \"metadata\": {",
"  \"kernelspec\": {",
"   \"display_name\": \"R\",",
"   \"language\": \"R\",",
"   \"name\": \"ir\"",
"  },",
"  \"language_info\": {",
"   \"name\": \"R\"",
"  }",
" },",
" \"nbformat\": 4,",
" \"nbformat_minor\": 2",
"}", sep=""), file="test.fraping.ipynb")
}