R/FBNLeukaemiaStudy.R
In clsdavid/FBNNet2_public: Fundamental Boolean Model and Networks
Defines functions
leukeamia_study_with_differential_output
reproduceSchmidtStudy
differentiallyExpressionStudy
## data folder D:/Dropbox/Dropbox/FBNNet/FBNNet2/output/Experiments_data/leukeamia/differential_cutOffInduction_1_majority_dot_6
## files2<-'D:/Dropbox/Dropbox/FBNNet/ChildhoodLeukeamiaDataFile/GSE2677_RAW'
## files3<-'D:\\Dropbox\\Dropbox\\FBNNet\\FBNNet2\\study\\leukeamia_data'
## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE13670_RAW\\GSE13670_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE20489_RAW\\GSE20489_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE42088_RAW\\GSE42088_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE54992_RAW\\GSE54992_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome
## Data\\GSE57194_RAW\\GSE57194_RAW')
## targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
## "T-ALL-25")
## targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43")
## targetsamples <- c("T-ALL-2", "T-ALL-20","T-ALL-25")
## targetsamples <- c("B-ALL-Adult", "B-ALL-IV_EtOH_40", "B-ALL-IV_GC_40", "C-Line-C7R1dim_high_GC",
## "C-Line-CEMC1_ratGR_GC", "HD1-STS-1", "HD2-RPK-1", "R-Line-C7R1dim_low_GC",
## "R-Line-CEMC1_GC", "R-Line-PreB_EtOH", "R-Line-PreB_GC", "S-Line-C7H2_GC",
## "S-Line-PreB_GC" )
#'@export
differentiallyExpressionStudy <- function(cellDirectory,
sortedtimeseries = NULL,
useGCRMA = FALSE,
cutOffInduction = 0.7,
cutOffRepression = 0.7,
majority = 7,
targetsamples = c("B-ALL-13",
"B-ALL-17",
"B-ALL-24",
"B-ALL-31",
"B-ALL-32",
"B-ALL-33",
"B-ALL-37",
"B-ALL-38",
"B-ALL-40",
"B-ALL-43",
"T-ALL-2",
"T-ALL-20",
"T-ALL-25")) {
# read affy files and normalized by RMA
if (is.null(sortedtimeseries)) {
sortedtimeseries2 <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
}
sortedtimeseries <- sortedtimeseries2[targetsamples]
cond <- sapply(sortedtimeseries, function(entry) !is.null(entry))
sortedtimeseries <- sortedtimeseries[cond]
print("###############################################our differentially experiments ###########################3")
diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
majority = majority)
commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID ##6to0
commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID##6to0
commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID ##24to0
commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID##24to0
commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID##24to6
commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID##24to6
print("############################")
print("6To0 induced via FBNNET bioinformatics:")
Genes6To0induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet1i))
deduplicated6To0induced_fbnnet <- unique(Genes6To0induced_fbnnet)
print(deduplicated6To0induced_fbnnet)
print(paste("In total =", length(commonGeneSet1i), " unqiue =", length(deduplicated6To0induced_fbnnet), sep = ""))
print("############################")
print("6To0 repressed via FBNNET bioinformatics:")
Genes6To0repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet1r))
deduplicated6To0repressed_fbnnet <- unique(Genes6To0repressed_fbnnet)
print(deduplicated6To0repressed_fbnnet)
print(paste("In total =", length(commonGeneSet1r), " unqiue =", length(deduplicated6To0repressed_fbnnet), sep = ""))
print("############################")
print("24To0 induced via FBNNET bioinformatics:")
Genes24To0induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet2i))
deduplicated24To0induced_fbnnet <- unique(Genes24To0induced_fbnnet)
print(deduplicated24To0induced_fbnnet)
print(paste("In total =", length(commonGeneSet2i), " unqiue =", length(deduplicated24To0induced_fbnnet), sep = ""))
print("############################")
print("24To0 repressed via FBNNET bioinformatics:")
Genes24To0repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet2r))
deduplicated24To0repressed_fbnnet <- unique(Genes24To0repressed_fbnnet)
print(deduplicated24To0repressed_fbnnet)
print(paste("In total =", length(commonGeneSet2r), " unqiue =", length(deduplicated24To0repressed_fbnnet), sep = ""))
print("############################")
print("24To6 induced via FBNNET bioinformatics:")
Genes24To6induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet3i))
deduplicated24To6induced_fbnnet <- unique(Genes24To6induced_fbnnet)
print(deduplicated24To6induced_fbnnet)
print(paste("In total =", length(commonGeneSet3i), " unqiue =", length(deduplicated24To6induced_fbnnet), sep = ""))
print("############################")
print("24To6 repressed via FBNNET bioinformatics:")
Genes24To6repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet3r))
deduplicated24To6repressed_fbnnet <- unique(Genes24To6repressed_fbnnet)
print(deduplicated24To6repressed_fbnnet)
print(paste("In total =", length(commonGeneSet3r), " unqiue =", length(deduplicated24To6repressed_fbnnet), sep = ""))
############################### output ################################################################################ todo: add the difference as well, plus the primise genes for
############################### further mining
finalSet <- unique(c(deduplicated6To0induced_fbnnet,
deduplicated6To0repressed_fbnnet,
deduplicated24To0induced_fbnnet,
deduplicated24To0repressed_fbnnet,
deduplicated24To6induced_fbnnet,
deduplicated24To6repressed_fbnnet))
print(paste("Total unique genes cross all time points are:", length(finalSet), sep = ""))
print(finalSet)
finalProbesetSet <- unique(c(commonGeneSet1i,
commonGeneSet1r,
commonGeneSet2i,
commonGeneSet2r,
commonGeneSet3i,
commonGeneSet3r))
filtered_timeseries <- lapply(sortedtimeseries2, function(subdata) subdata[rownames(subdata) %in% finalProbesetSet, ])
reproduceSchmidtStudy_res <- list()
reproduceSchmidtStudy_res[["CombinedGeneSet"]] <- finalSet
reproduceSchmidtStudy_res[["FBNNetGenes6To0induced"]] <- deduplicated6To0induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes6To0repressed"]] <- deduplicated6To0repressed_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To0induced"]] <- deduplicated24To0induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To0repressed"]] <- deduplicated24To0repressed_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To6Or8induced"]] <- deduplicated24To6induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To6or8repressed"]] <- deduplicated24To6repressed_fbnnet
reproduceSchmidtStudy_res[["timeseries_original"]] <- sortedtimeseries2
reproduceSchmidtStudy_res[["filtered_timeseries"]] <- filtered_timeseries
reproduceSchmidtStudy_res[["total_ProbesetSet"]] <- finalProbesetSet
#save(reproduceSchmidtStudy_res, file = "temp/reproduceSchmidtStudy_temp.Rdata")
reproduceSchmidtStudy_res
}
## data folder D:/Dropbox/Dropbox/FBNNet/FBNNet2/output/Experiments_data/leukeamia/differential_cutOffInduction_1_majority_dot_6
## files2<-'D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW'
## files3<-'D:\\Dropbox\\Dropbox\\FBNNet\\FBNNet2\\study\\leukeamia_data'
## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE13670_RAW\\GSE13670_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE20489_RAW\\GSE20489_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE42088_RAW\\GSE42088_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE54992_RAW\\GSE54992_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome
## Data\\GSE57194_RAW\\GSE57194_RAW')
#'@export
reproduceSchmidtStudy <- function(cellDirectory,
sortedtimeseries = NULL,
useGCRMA = FALSE,
cutOffInduction = 0.7,
cutOffRepression = 0.7,
majority = 7,
targetsamples = c("B-ALL-13",
"B-ALL-17",
"B-ALL-24",
"B-ALL-31",
"B-ALL-32",
"B-ALL-33",
"B-ALL-37",
"B-ALL-38",
"B-ALL-40",
"B-ALL-43",
"T-ALL-2",
"T-ALL-20",
"T-ALL-25")) {
# read affy files and normalized by RMA
if (is.null(sortedtimeseries)) {
sortedtimeseries2 <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
}
sortedtimeseries <- sortedtimeseries2[targetsamples]
cond <- sapply(sortedtimeseries, function(entry) !is.null(entry))
sortedtimeseries <- sortedtimeseries[cond]
#originSchmidtEvalues <- read.delim("output/Schmidt2006/GSE2677_E-Values-Patients.csv", header = TRUE, sep = ",")
# testMat <- originSchmidtEvalues[, 5:7]
# rownames(testMat) <- originSchmidtEvalues[, 1]
# colnames(testMat) <- c(0, 8, 24)
# try to reproduce the result of the paper 'Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia', written by
# Stefan Schmidt schmidtMvalues = read.table('study/Schmidt2006/GSE2677_M-Values-Patients.xls', header = TRUE)
schmidtMvalues <- read.delim("study/Schmidt2006/GSE2677_M-Values-Patients.csv", header = TRUE, sep = ",")
schmidtMvalues6To0 <- schmidtMvalues[, c(1, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28)]
schmidtMvalues24To0 <- schmidtMvalues[, c(1, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29)]
schmidtMvalues24To6Or8 <- read.delim("study/Schmidt2006/GSE2677_M-Values-Patients-6-24.csv", header = TRUE, sep = ",")
schmidtMvalues24To6Or8 <- schmidtMvalues24To6Or8[, c(1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)]
# remove the id column
schmidtMvalues6To0_temp <- sapply(schmidtMvalues6To0[, -1], as.numeric)
schmidtMvalues24To0_temp <- sapply(schmidtMvalues24To0[, -1], as.numeric)
schmidtMvalues24To6Or8_temp <- sapply(schmidtMvalues24To6Or8[, -1], as.numeric)
rownames(schmidtMvalues6To0_temp) <- schmidtMvalues6To0[, 1]
rownames(schmidtMvalues24To0_temp) <- schmidtMvalues24To0[, 1]
rownames(schmidtMvalues24To6Or8_temp) <- schmidtMvalues24To6Or8[, 1]
schmidtMvalues6To0 <- schmidtMvalues6To0_temp
schmidtMvalues24To0 <- schmidtMvalues24To0_temp
schmidtMvalues24To6Or8 <- schmidtMvalues24To6Or8_temp
# experiment with the criteria of logRatio=0.7(+-), majority 6 of 13
cat("Experiment with the criteria of cutOffInduction=", cutOffInduction, " & cutOffRepression=", cutOffRepression, " (+-), majority=", majority, " of 13")
print(paste("Experiment with cutOffInduction=", cutOffInduction, " and the majority of ", majority, sep = ""))
# induced 0 -> 6
newRownames <- c()
newMatinduced <- do.call(rbind, lapply(1:nrow(schmidtMvalues6To0), function(rowindex, logRatio, majority, schmidtMvalues6To0) {
# get row vector
rowname <- rownames(schmidtMvalues6To0)[rowindex]
rowvector <- schmidtMvalues6To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues6To0)[rowindex], schmidtMvalues6To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues6To0))
print("6To0 induced:")
Probeset6To0induced <- newMatinduced[, 1]
if (is.null(Probeset6To0induced))
Probeset6To0induced <- c()
Genes6To0induced <- unlist(mapProbesetNames(Probeset6To0induced))
deduplicated6To0induced <- unique(Genes6To0induced)
print(deduplicated6To0induced)
print(paste("In total =", length(Probeset6To0induced), " unqiue =", length(deduplicated6To0induced), sep = ""))
# repressed
newRownames <- c()
newMatrepressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues6To0), function(rowindex, logRatio, majority, schmidtMvalues6To0) {
# get row vector
rowvector <- schmidtMvalues6To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues6To0)[rowindex], schmidtMvalues6To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues6To0))
print(paste("Experiment with cutOffRepression=", cutOffRepression, " and the majority of ", majority, " out of 13", sep = ""))
print("6To0 repressed:")
Probeset6To0repressed <- newMatrepressed[, 1]
if (is.null(Probeset6To0repressed))
Probeset6To0repressed <- c()
Genes6To0repressed <- unlist(mapProbesetNames(Probeset6To0repressed))
deduplicated6To0repressed <- unique(Genes6To0repressed)
print(deduplicated6To0repressed)
print(paste("In total =", length(Probeset6To0repressed), " unqiue =", length(deduplicated6To0repressed), sep = ""))
# 24-0 induced
newRownames <- c()
newMat2induced <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To0), function(rowindex, logRatio, majority, schmidtMvalues24To0) {
# get row vector
rowvector <- schmidtMvalues24To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To0)[rowindex], schmidtMvalues24To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues24To0))
print("24To0 induced:")
Probeset24To0induced <- newMat2induced[, 1]
if (is.null(Probeset24To0induced))
Probeset24To0induced <- c()
Genes24To0induced <- unlist(mapProbesetNames(Probeset24To0induced))
deduplicated24To0induced <- unique(Genes24To0induced)
print(deduplicated24To0induced)
print(paste("In total =", length(Probeset24To0induced), " unqiue =", length(deduplicated24To0induced), sep = ""))
# repressed
newRownames <- c()
newMat2repressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To0), function(rowindex, logRatio, majority, schmidtMvalues24To0) {
# get row vector
rowvector <- schmidtMvalues24To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To0)[rowindex], schmidtMvalues24To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues24To0))
print("24To0 repressed:")
Probeset24To0repressed <- newMat2repressed[, 1]
if (is.null(Probeset24To0repressed))
Probeset24To0repressed <- c()
Genes24To0repressed <- unlist(mapProbesetNames(Probeset24To0repressed))
deduplicated24To0repressed <- unique(Genes24To0repressed)
print(deduplicated24To0repressed)
print(paste("In total =", length(Probeset24To0repressed), " unqiue =", length(deduplicated24To0repressed), sep = ""))
# 24To6/8 schmidtMvalues24To6Or8 induced
newRownames <- c()
newMat3induced <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To6Or8), function(rowindex, logRatio, majority, schmidtMvalues24To6Or8) {
# get row vector
rowvector <- schmidtMvalues24To6Or8[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To6Or8)[rowindex], schmidtMvalues24To6Or8[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues24To6Or8))
print("24To6/8 induced:")
Probeset24To6Or8induced <- newMat3induced[, 1]
if (is.null(Probeset24To6Or8induced))
Probeset24To6Or8induced <- c()
Genes24To6Or8induced <- unlist(mapProbesetNames(Probeset24To6Or8induced))
deduplicated24To6Or8induced <- unique(Genes24To6Or8induced)
print(deduplicated24To6Or8induced)
print(paste("In total =", length(Probeset24To6Or8induced), " unqiue =", length(deduplicated24To6Or8induced), sep = ""))
# repressed
newRownames <- c()
newMat3repressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To6Or8), function(rowindex, logRatio, majority, schmidtMvalues24To6Or8) {
# get row vector
rowvector <- schmidtMvalues24To6Or8[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To6Or8)[rowindex], schmidtMvalues24To6Or8[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues24To6Or8))
print("24To6/8 repressed:")
Probeset24To6or8repressed <- newMat3repressed[, 1]
if (is.null(Probeset24To6or8repressed))
Probeset24To6or8repressed <- c()
Genes24To6or8repressed <- unlist(mapProbesetNames(Probeset24To6or8repressed))
deduplicated24To6or8repressed <- unique(Genes24To6or8repressed)
print(deduplicated24To6or8repressed)
print(paste("In total =", length(Probeset24To6or8repressed), " unqiue =", length(deduplicated24To6or8repressed), sep = ""))
############################### output ################################################################################ todo: add the difference as well, plus the primise genes for
############################### further mining
finalSet <- unique(c(deduplicated6To0induced,
deduplicated6To0repressed,
deduplicated24To0induced,
deduplicated24To0repressed,
deduplicated24To6Or8induced,
deduplicated24To6or8repressed))
print(paste("Total unique genes cross all time points are:", length(finalSet), sep = ""))
print(finalSet)
finalProbesetSet <- unique(c(Probeset6To0induced, Probeset6To0repressed, Probeset24To0induced, Probeset24To0repressed, Probeset24To6Or8induced, Probeset24To6or8repressed))
filtered_timeseries <- lapply(sortedtimeseries2, function(subdata) subdata[rownames(subdata) %in% finalProbesetSet, ])
reproduceSchmidtStudy_res <- list()
reproduceSchmidtStudy_res[["CombinedGeneSet"]] <- finalSet
reproduceSchmidtStudy_res[["SchmidtGenes6To0induced"]] <- deduplicated6To0induced
reproduceSchmidtStudy_res[["SchmidtGenes6To0repressed"]] <- deduplicated6To0repressed
reproduceSchmidtStudy_res[["SchmidtGenes24To0induced"]] <- deduplicated24To0induced
reproduceSchmidtStudy_res[["SchmidtGenes24To0repressed"]] <- deduplicated24To0repressed
reproduceSchmidtStudy_res[["SchmidtGenes24To6Or8induced"]] <- deduplicated24To6Or8induced
reproduceSchmidtStudy_res[["SchmidtGenes24To6or8repressed"]] <- deduplicated24To6or8repressed
reproduceSchmidtStudy_res[["timeseries_original"]] <- sortedtimeseries2
reproduceSchmidtStudy_res[["filtered_timeseries"]] <- filtered_timeseries
reproduceSchmidtStudy_res[["total_ProbesetSet"]] <- finalProbesetSet
reproduceSchmidtStudy_res[["Schmidt6To0inducedMAT"]] <- newMatinduced
reproduceSchmidtStudy_res[["Schmidt6To0repressedMAT"]] <- newMatrepressed
reproduceSchmidtStudy_res[["Schmidt24To0inducedMAT"]] <- newMat2induced
reproduceSchmidtStudy_res[["Schmidt24To0repressedMAT"]] <- newMat2repressed
reproduceSchmidtStudy_res[["Schmidt24To6Or8inducedMAT"]] <- newMat3induced
reproduceSchmidtStudy_res[["Schmidt24To6or8repressedMAT"]] <- newMat3repressed
#save(reproduceSchmidtStudy_res, file = "temp/reproduceSchmidtStudy_temp.Rdata")
reproduceSchmidtStudy_res
}
#'@export
leukeamia_study_with_differential_output <- function(schmidts_realanlysis_output,
method = c("average", "kmeans", "edgeDetector", "scanStatistic"),
maxK = 4,
temporal = 2,
target_genes = c()) {
sortedtimeseries_leukaemia <- schmidts_realanlysis_output$filtered_timeseries
## shouldn't have duplicate row names
convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(sortedtimeseries_leukaemia)$convert_data
finalSet <- unique(schmidts_realanlysis_output$CombinedGeneSet)
if( length(target_genes) > 0) {
finalSet = target_genes
}
# remove duplicate
## the number of genes less than or equal to combined gene set
convertedgenedagta <- lapply(convertedgenedagta, function(subdata)subdata[which(rownames(subdata) %in% finalSet), ])
print(rownames(convertedgenedagta[[1]]))
# timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
if (method == "average") {
timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
} else {
require(BoolNet)
timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
}
# membexp=3 more fussy
genes <- rownames(timeseries_RMA_LogRatio_0.7[[1]])
# build all cubes for all clusters
cubeLeukaemia_RMA_LogRatio_temp <- constructFBNCube(genes, genes, timeseries_RMA_LogRatio_0.7, maxK, temporal, TRUE)
gc()
#.rs.restartR()
save(cubeLeukaemia_RMA_LogRatio_temp, file = "temp/cubeLeukaemia_RMA_LogRatio_temp.Rdata")
totalNetworks_RMA_LogRatio_0.7 <- mineFBNNetwork(cubeLeukaemia_RMA_LogRatio_temp, useParallel = TRUE)
leukeamia_study_with_schmidts_output_res <- list(schmidts_realanlysis_output = schmidts_realanlysis_output, cube = cubeLeukaemia_RMA_LogRatio_temp, network = totalNetworks_RMA_LogRatio_0.7,
timeseries = timeseries_RMA_LogRatio_0.7)
#save(leukeamia_study_with_schmidts_output_res, file = "temp/leukeamia_study_with_schmidts_output_temp.Rdata")
leukeamia_study_with_schmidts_output_res
}
#'
#' #'@export
#' leukeamia_study_with_schmidts_output_cluster <- function(schmidts_realanlysis_output,
#' method = c("average", "kmeans", "edgeDetector", "scanStatistic"),
#' maxK = 4,
#' temporal = 2,
#' minElementInCluster = 10,
#' maxElementInCluster = 20) {
#' sortedtimeseries_leukaemia <- schmidts_realanlysis_output$filtered_timeseries
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(sortedtimeseries_leukaemia)$convert_data
#'
#' finalSet <- unique(schmidts_realanlysis_output$CombinedGeneSet)
#' print(finalSet)
#'
#' # remove duplicate
#' convertedgenedagta <- lapply(convertedgenedagta, function(subdata) subdata[which(rownames(subdata) %in% finalSet), ])
#'
#' print(rownames(convertedgenedagta[[1]]))
#'
#' # timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' totalNetworks_RMA_LogRatio_0.7 <- generateFBMNetwork(timeseries_data = timeseries_RMA_LogRatio_0.7,
#' maxK = 4,
#' max_deep_temporal = 2,
#' useParallel = TRUE,
#' maxGenesForSingleCube = 10,
#' parallel_on_group = TRUE)
#'
#' #getClusteredTimeseries_RMA_LogRatio_0.7 <- dividedDataIntoSubgroups(timeseries_RMA_LogRatio_0.7, maxElementInCluster)
#' # build all cubes for all clusters build all cubes for all clusters
#' #cubeLeukaemia_RMA_LogRatio_temp <- constructFBNCubeAndNetworkInClusters_combine(getClusteredTimeseries_RMA_LogRatio_0.7, maxK = maxK, temporal = temporal, useParallel = TRUE)
#' # merge all networks? seperate by clusters?
#' #networks_RMA_LogRatio_0.7 <- mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_temp)
#' #totalNetworks_RMA_LogRatio_0.7 <- filterNetworkConnections(networks_RMA_LogRatio_0.7)
#'
#' #save(cubeLeukaemia_RMA_LogRatio_temp, file = "temp/cubeLeukaemia_RMA_LogRatio_temp_cluster.Rdata")
#' leukeamia_study_with_schmidts_output_res <- list(schmidts_realanlysis_output = schmidts_realanlysis_output, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' #save(leukeamia_study_with_schmidts_output_res, file = "temp/leukeamia_study_with_schmidts_output_temp.Rdata")
#' leukeamia_study_with_schmidts_output_res
#' }
#'
#' ## files2<-'D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW'
#' ## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE13670_RAW\\GSE13670_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE20489_RAW\\GSE20489_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE42088_RAW\\GSE42088_RAW'，'D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE54992_RAW\\GSE54992_RAW'，'D:\\Dropbox\\Dropbox\\FBNNet\\Genome Data\\GSE57194_RAW\\GSE57194_RAW')
#' #'@export
#' leukeamia_study_cluster <- function(cellDirectory,
#' cutOffInduction = 0.7,
#' cutOffRepression = 0.7,
#' majority = 0.5,
#' sortedtimeseries = NULL,
#' useGCRMA = FALSE,
#' method = c("average", "kmeans", "edgeDetector", "scanStatistic")) {
#' # read affy files and normalized by RMA
#' if (is.null(sortedtimeseries)) {
#' sortedtimeseries <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
#' }
#'
#' targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
#' "T-ALL-25")
#' sortedtimeseries_leukaemia <- sortedtimeseries[targetsamples]
#' cond <- sapply(sortedtimeseries_leukaemia, function(entry) !is.null(entry))
#' sortedtimeseries_leukaemia <- sortedtimeseries_leukaemia[cond]
#'
#' # Generate cube 0.7 = 2.0 ^ 0.7
#' probesets <- rownames(sortedtimeseries_leukaemia[[1]])
#' probesetGeneNameMappings <- mapProbesetNames(probesets)
#'
#' diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries_leukaemia, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
#' majority = majority, probesetGeneNameMappings = probesetGeneNameMappings)
#'
#' commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID
#' commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID
#' commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID
#' commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID
#' commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID
#' commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID
#'
#' finalSet <- unique(c(commonGeneSet1i, commonGeneSet1r, commonGeneSet2i, commonGeneSet2r, commonGeneSet3i, commonGeneSet3r))
#' print(finalSet)
#'
#' # remove duplicate
#' subsetgenedata <- lapply(sortedtimeseries, function(subdata) subdata[rownames(subdata) %in% finalSet, ])
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(subsetgenedata)$convert_data
#' print(rownames(convertedgenedagta[[1]]))
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' # membexp=3 more fussy
#' getClusteredTimeseries_RMA_LogRatio_0.7 <- clusterdDiscreteData(convertedgenedagta,timeseries_RMA_LogRatio_0.7, 30)
#' # build all cubes for all clusters
#' cubeLeukaemia_RMA_LogRatio_0.7 <- constructFBNCubeAndNetworkInClusters(getClusteredTimeseries_RMA_LogRatio_0.7, 4, 1, TRUE)
#' # merge all networks? seperate by clusters?
#' networks_RMA_LogRatio_0.7 <- mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_0.7)
#' totalNetworks_RMA_LogRatio_0.7 <- filterNetworkConnections(networks_RMA_LogRatio_0.7)
#'
#' diffgenes <- list(Induction_0_to_6_or_8 = commonGeneSet1i, Repression_0_to_6_or_8 = commonGeneSet1r, Induction_0_to_24 = commonGeneSet2i, Repression_0_to_24 = commonGeneSet2r,
#' Induction_6_or_8_to_24 = commonGeneSet3i, Repression_6_or_8_to_24 = commonGeneSet3r)
#' list(diff_genes = diffgenes, cube = timeseries_RMA_LogRatio_0.7, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' }
#'
#' # use boolnet
#' #'@export
#' leukeamia_study <- function(cellDirectory,
#' cutOffInduction = 1,
#' cutOffRepression = 1,
#' majority = 0.5,
#' sortedtimeseries = NULL,
#' useGCRMA = FALSE,
#' method = c("average",
#' "kmeans", "edgeDetector", "scanStatistic")) {
#' require(BoolNet)
#' # read affy files and normalized by RMA
#' if (is.null(sortedtimeseries)) {
#' sortedtimeseries <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
#' }
#'
#' targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
#' "T-ALL-25")
#' sortedtimeseries_leukaemia <- sortedtimeseries[targetsamples]
#' cond <- sapply(sortedtimeseries_leukaemia, function(entry) !is.null(entry))
#' sortedtimeseries_leukaemia <- sortedtimeseries_leukaemia[cond]
#'
#' # Generate cube 0.7 = 2.0 ^ 0.7
#' cutOffInduction = cutOffInduction
#' cutOffRepression = cutOffRepression
#' majority <- majority
#' probesets <- rownames(sortedtimeseries_leukaemia[[1]])
#' probesetGeneNameMappings <- mapProbesetNames(probesets)
#'
#' diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries_leukaemia, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
#' majority = majority, probesetGeneNameMappings = probesetGeneNameMappings)
#'
#' commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID
#' commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID
#' commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID
#' commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID
#' commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID
#' commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID
#'
#' finalSet <- unique(c(commonGeneSet1i, commonGeneSet1r, commonGeneSet2i, commonGeneSet2r, commonGeneSet3i, commonGeneSet3r))
#' print(finalSet)
#'
#' # remove duplicate
#' subsetgenedata <- lapply(sortedtimeseries, function(subdata) subdata[rownames(subdata) %in% finalSet, ])
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(subsetgenedata)$convert_data
#' print(rownames(convertedgenedagta[[1]]))
#'
#' # timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' # membexp=3 more fussy
#' genes <- rownames(timeseries_RMA_LogRatio_0.7[[1]])
#' # build all cubes for all clusters
#' cubeLeukaemia_RMA_LogRatio_0.7 <- constructFBNCube(genes, genes, timeseries_RMA_LogRatio_0.7, 4, 2, TRUE, TRUE)
#' totalNetworks_RMA_LogRatio_0.7 <- mineFBNNetwork(cubeLeukaemia_RMA_LogRatio_0.7)
#'
#' diffgenes <- list(Induction_0_to_6_or_8 = commonGeneSet1i, Repression_0_to_6_or_8 = commonGeneSet1r, Induction_0_to_24 = commonGeneSet2i, Repression_0_to_24 = commonGeneSet2r,
#' Induction_6_or_8_to_24 = commonGeneSet3i, Repression_6_or_8_to_24 = commonGeneSet3r)
#' list(diff_genes = diffgenes, cube = timeseries_RMA_LogRatio_0.7, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' # merge all networks? seperate by clusters? networks_RMA_LogRatio_0.7<<-mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_0.7)
#' # totalNetworks_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$TotalNetwork totalGenes_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$TotalNetwork$genes
#' # individualNetworks_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$IndividualNetwork
#' }
clsdavid/FBNNet2_public documentation built on April 20, 2023, 4:36 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions leukeamia_study_with_differential_output reproduceSchmidtStudy differentiallyExpressionStudy
## data folder D:/Dropbox/Dropbox/FBNNet/FBNNet2/output/Experiments_data/leukeamia/differential_cutOffInduction_1_majority_dot_6
## files2<-'D:/Dropbox/Dropbox/FBNNet/ChildhoodLeukeamiaDataFile/GSE2677_RAW'
## files3<-'D:\\Dropbox\\Dropbox\\FBNNet\\FBNNet2\\study\\leukeamia_data'
## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE13670_RAW\\GSE13670_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE20489_RAW\\GSE20489_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE42088_RAW\\GSE42088_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE54992_RAW\\GSE54992_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome
## Data\\GSE57194_RAW\\GSE57194_RAW')
## targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
## "T-ALL-25")
## targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43")
## targetsamples <- c("T-ALL-2", "T-ALL-20","T-ALL-25")
## targetsamples <- c("B-ALL-Adult", "B-ALL-IV_EtOH_40", "B-ALL-IV_GC_40", "C-Line-C7R1dim_high_GC",
## "C-Line-CEMC1_ratGR_GC", "HD1-STS-1", "HD2-RPK-1", "R-Line-C7R1dim_low_GC",
## "R-Line-CEMC1_GC", "R-Line-PreB_EtOH", "R-Line-PreB_GC", "S-Line-C7H2_GC",
## "S-Line-PreB_GC" )
#'@export
differentiallyExpressionStudy <- function(cellDirectory,
sortedtimeseries = NULL,
useGCRMA = FALSE,
cutOffInduction = 0.7,
cutOffRepression = 0.7,
majority = 7,
targetsamples = c("B-ALL-13",
"B-ALL-17",
"B-ALL-24",
"B-ALL-31",
"B-ALL-32",
"B-ALL-33",
"B-ALL-37",
"B-ALL-38",
"B-ALL-40",
"B-ALL-43",
"T-ALL-2",
"T-ALL-20",
"T-ALL-25")) {
# read affy files and normalized by RMA
if (is.null(sortedtimeseries)) {
sortedtimeseries2 <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
}
sortedtimeseries <- sortedtimeseries2[targetsamples]
cond <- sapply(sortedtimeseries, function(entry) !is.null(entry))
sortedtimeseries <- sortedtimeseries[cond]
print("###############################################our differentially experiments ###########################3")
diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
majority = majority)
commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID ##6to0
commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID##6to0
commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID ##24to0
commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID##24to0
commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID##24to6
commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID##24to6
print("############################")
print("6To0 induced via FBNNET bioinformatics:")
Genes6To0induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet1i))
deduplicated6To0induced_fbnnet <- unique(Genes6To0induced_fbnnet)
print(deduplicated6To0induced_fbnnet)
print(paste("In total =", length(commonGeneSet1i), " unqiue =", length(deduplicated6To0induced_fbnnet), sep = ""))
print("############################")
print("6To0 repressed via FBNNET bioinformatics:")
Genes6To0repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet1r))
deduplicated6To0repressed_fbnnet <- unique(Genes6To0repressed_fbnnet)
print(deduplicated6To0repressed_fbnnet)
print(paste("In total =", length(commonGeneSet1r), " unqiue =", length(deduplicated6To0repressed_fbnnet), sep = ""))
print("############################")
print("24To0 induced via FBNNET bioinformatics:")
Genes24To0induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet2i))
deduplicated24To0induced_fbnnet <- unique(Genes24To0induced_fbnnet)
print(deduplicated24To0induced_fbnnet)
print(paste("In total =", length(commonGeneSet2i), " unqiue =", length(deduplicated24To0induced_fbnnet), sep = ""))
print("############################")
print("24To0 repressed via FBNNET bioinformatics:")
Genes24To0repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet2r))
deduplicated24To0repressed_fbnnet <- unique(Genes24To0repressed_fbnnet)
print(deduplicated24To0repressed_fbnnet)
print(paste("In total =", length(commonGeneSet2r), " unqiue =", length(deduplicated24To0repressed_fbnnet), sep = ""))
print("############################")
print("24To6 induced via FBNNET bioinformatics:")
Genes24To6induced_fbnnet <- unlist(mapProbesetNames(commonGeneSet3i))
deduplicated24To6induced_fbnnet <- unique(Genes24To6induced_fbnnet)
print(deduplicated24To6induced_fbnnet)
print(paste("In total =", length(commonGeneSet3i), " unqiue =", length(deduplicated24To6induced_fbnnet), sep = ""))
print("############################")
print("24To6 repressed via FBNNET bioinformatics:")
Genes24To6repressed_fbnnet <- unlist(mapProbesetNames(commonGeneSet3r))
deduplicated24To6repressed_fbnnet <- unique(Genes24To6repressed_fbnnet)
print(deduplicated24To6repressed_fbnnet)
print(paste("In total =", length(commonGeneSet3r), " unqiue =", length(deduplicated24To6repressed_fbnnet), sep = ""))
############################### output ################################################################################ todo: add the difference as well, plus the primise genes for
############################### further mining
finalSet <- unique(c(deduplicated6To0induced_fbnnet,
deduplicated6To0repressed_fbnnet,
deduplicated24To0induced_fbnnet,
deduplicated24To0repressed_fbnnet,
deduplicated24To6induced_fbnnet,
deduplicated24To6repressed_fbnnet))
print(paste("Total unique genes cross all time points are:", length(finalSet), sep = ""))
print(finalSet)
finalProbesetSet <- unique(c(commonGeneSet1i,
commonGeneSet1r,
commonGeneSet2i,
commonGeneSet2r,
commonGeneSet3i,
commonGeneSet3r))
filtered_timeseries <- lapply(sortedtimeseries2, function(subdata) subdata[rownames(subdata) %in% finalProbesetSet, ])
reproduceSchmidtStudy_res <- list()
reproduceSchmidtStudy_res[["CombinedGeneSet"]] <- finalSet
reproduceSchmidtStudy_res[["FBNNetGenes6To0induced"]] <- deduplicated6To0induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes6To0repressed"]] <- deduplicated6To0repressed_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To0induced"]] <- deduplicated24To0induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To0repressed"]] <- deduplicated24To0repressed_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To6Or8induced"]] <- deduplicated24To6induced_fbnnet
reproduceSchmidtStudy_res[["FBNNetGenes24To6or8repressed"]] <- deduplicated24To6repressed_fbnnet
reproduceSchmidtStudy_res[["timeseries_original"]] <- sortedtimeseries2
reproduceSchmidtStudy_res[["filtered_timeseries"]] <- filtered_timeseries
reproduceSchmidtStudy_res[["total_ProbesetSet"]] <- finalProbesetSet
#save(reproduceSchmidtStudy_res, file = "temp/reproduceSchmidtStudy_temp.Rdata")
reproduceSchmidtStudy_res
}
## data folder D:/Dropbox/Dropbox/FBNNet/FBNNet2/output/Experiments_data/leukeamia/differential_cutOffInduction_1_majority_dot_6
## files2<-'D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW'
## files3<-'D:\\Dropbox\\Dropbox\\FBNNet\\FBNNet2\\study\\leukeamia_data'
## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE13670_RAW\\GSE13670_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE20489_RAW\\GSE20489_RAW','G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE42088_RAW\\GSE42088_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome_Data\\GSE54992_RAW\\GSE54992_RAW'，'G:\\Dropbox\\Dropbox\\FBNNet\\Genome
## Data\\GSE57194_RAW\\GSE57194_RAW')
#'@export
reproduceSchmidtStudy <- function(cellDirectory,
sortedtimeseries = NULL,
useGCRMA = FALSE,
cutOffInduction = 0.7,
cutOffRepression = 0.7,
majority = 7,
targetsamples = c("B-ALL-13",
"B-ALL-17",
"B-ALL-24",
"B-ALL-31",
"B-ALL-32",
"B-ALL-33",
"B-ALL-37",
"B-ALL-38",
"B-ALL-40",
"B-ALL-43",
"T-ALL-2",
"T-ALL-20",
"T-ALL-25")) {
# read affy files and normalized by RMA
if (is.null(sortedtimeseries)) {
sortedtimeseries2 <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
}
sortedtimeseries <- sortedtimeseries2[targetsamples]
cond <- sapply(sortedtimeseries, function(entry) !is.null(entry))
sortedtimeseries <- sortedtimeseries[cond]
#originSchmidtEvalues <- read.delim("output/Schmidt2006/GSE2677_E-Values-Patients.csv", header = TRUE, sep = ",")
# testMat <- originSchmidtEvalues[, 5:7]
# rownames(testMat) <- originSchmidtEvalues[, 1]
# colnames(testMat) <- c(0, 8, 24)
# try to reproduce the result of the paper 'Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia', written by
# Stefan Schmidt schmidtMvalues = read.table('study/Schmidt2006/GSE2677_M-Values-Patients.xls', header = TRUE)
schmidtMvalues <- read.delim("study/Schmidt2006/GSE2677_M-Values-Patients.csv", header = TRUE, sep = ",")
schmidtMvalues6To0 <- schmidtMvalues[, c(1, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28)]
schmidtMvalues24To0 <- schmidtMvalues[, c(1, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29)]
schmidtMvalues24To6Or8 <- read.delim("study/Schmidt2006/GSE2677_M-Values-Patients-6-24.csv", header = TRUE, sep = ",")
schmidtMvalues24To6Or8 <- schmidtMvalues24To6Or8[, c(1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)]
# remove the id column
schmidtMvalues6To0_temp <- sapply(schmidtMvalues6To0[, -1], as.numeric)
schmidtMvalues24To0_temp <- sapply(schmidtMvalues24To0[, -1], as.numeric)
schmidtMvalues24To6Or8_temp <- sapply(schmidtMvalues24To6Or8[, -1], as.numeric)
rownames(schmidtMvalues6To0_temp) <- schmidtMvalues6To0[, 1]
rownames(schmidtMvalues24To0_temp) <- schmidtMvalues24To0[, 1]
rownames(schmidtMvalues24To6Or8_temp) <- schmidtMvalues24To6Or8[, 1]
schmidtMvalues6To0 <- schmidtMvalues6To0_temp
schmidtMvalues24To0 <- schmidtMvalues24To0_temp
schmidtMvalues24To6Or8 <- schmidtMvalues24To6Or8_temp
# experiment with the criteria of logRatio=0.7(+-), majority 6 of 13
cat("Experiment with the criteria of cutOffInduction=", cutOffInduction, " & cutOffRepression=", cutOffRepression, " (+-), majority=", majority, " of 13")
print(paste("Experiment with cutOffInduction=", cutOffInduction, " and the majority of ", majority, sep = ""))
# induced 0 -> 6
newRownames <- c()
newMatinduced <- do.call(rbind, lapply(1:nrow(schmidtMvalues6To0), function(rowindex, logRatio, majority, schmidtMvalues6To0) {
# get row vector
rowname <- rownames(schmidtMvalues6To0)[rowindex]
rowvector <- schmidtMvalues6To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues6To0)[rowindex], schmidtMvalues6To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues6To0))
print("6To0 induced:")
Probeset6To0induced <- newMatinduced[, 1]
if (is.null(Probeset6To0induced))
Probeset6To0induced <- c()
Genes6To0induced <- unlist(mapProbesetNames(Probeset6To0induced))
deduplicated6To0induced <- unique(Genes6To0induced)
print(deduplicated6To0induced)
print(paste("In total =", length(Probeset6To0induced), " unqiue =", length(deduplicated6To0induced), sep = ""))
# repressed
newRownames <- c()
newMatrepressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues6To0), function(rowindex, logRatio, majority, schmidtMvalues6To0) {
# get row vector
rowvector <- schmidtMvalues6To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues6To0)[rowindex], schmidtMvalues6To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues6To0))
print(paste("Experiment with cutOffRepression=", cutOffRepression, " and the majority of ", majority, " out of 13", sep = ""))
print("6To0 repressed:")
Probeset6To0repressed <- newMatrepressed[, 1]
if (is.null(Probeset6To0repressed))
Probeset6To0repressed <- c()
Genes6To0repressed <- unlist(mapProbesetNames(Probeset6To0repressed))
deduplicated6To0repressed <- unique(Genes6To0repressed)
print(deduplicated6To0repressed)
print(paste("In total =", length(Probeset6To0repressed), " unqiue =", length(deduplicated6To0repressed), sep = ""))
# 24-0 induced
newRownames <- c()
newMat2induced <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To0), function(rowindex, logRatio, majority, schmidtMvalues24To0) {
# get row vector
rowvector <- schmidtMvalues24To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To0)[rowindex], schmidtMvalues24To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues24To0))
print("24To0 induced:")
Probeset24To0induced <- newMat2induced[, 1]
if (is.null(Probeset24To0induced))
Probeset24To0induced <- c()
Genes24To0induced <- unlist(mapProbesetNames(Probeset24To0induced))
deduplicated24To0induced <- unique(Genes24To0induced)
print(deduplicated24To0induced)
print(paste("In total =", length(Probeset24To0induced), " unqiue =", length(deduplicated24To0induced), sep = ""))
# repressed
newRownames <- c()
newMat2repressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To0), function(rowindex, logRatio, majority, schmidtMvalues24To0) {
# get row vector
rowvector <- schmidtMvalues24To0[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To0)[rowindex], schmidtMvalues24To0[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues24To0))
print("24To0 repressed:")
Probeset24To0repressed <- newMat2repressed[, 1]
if (is.null(Probeset24To0repressed))
Probeset24To0repressed <- c()
Genes24To0repressed <- unlist(mapProbesetNames(Probeset24To0repressed))
deduplicated24To0repressed <- unique(Genes24To0repressed)
print(deduplicated24To0repressed)
print(paste("In total =", length(Probeset24To0repressed), " unqiue =", length(deduplicated24To0repressed), sep = ""))
# 24To6/8 schmidtMvalues24To6Or8 induced
newRownames <- c()
newMat3induced <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To6Or8), function(rowindex, logRatio, majority, schmidtMvalues24To6Or8) {
# get row vector
rowvector <- schmidtMvalues24To6Or8[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) >= logRatio))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To6Or8)[rowindex], schmidtMvalues24To6Or8[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffInduction, majority, schmidtMvalues24To6Or8))
print("24To6/8 induced:")
Probeset24To6Or8induced <- newMat3induced[, 1]
if (is.null(Probeset24To6Or8induced))
Probeset24To6Or8induced <- c()
Genes24To6Or8induced <- unlist(mapProbesetNames(Probeset24To6Or8induced))
deduplicated24To6Or8induced <- unique(Genes24To6Or8induced)
print(deduplicated24To6Or8induced)
print(paste("In total =", length(Probeset24To6Or8induced), " unqiue =", length(deduplicated24To6Or8induced), sep = ""))
# repressed
newRownames <- c()
newMat3repressed <- do.call(rbind, lapply(1:nrow(schmidtMvalues24To6Or8), function(rowindex, logRatio, majority, schmidtMvalues24To6Or8) {
# get row vector
rowvector <- schmidtMvalues24To6Or8[rowindex, ]
totalSamples <- length(rowvector)
numOfMatchCriteria <- length(which(as.numeric(rowvector) <= (-1 * logRatio)))
if (numOfMatchCriteria >= majority) {
return(c(rownames(schmidtMvalues24To6Or8)[rowindex], schmidtMvalues24To6Or8[rowindex, ], majority = majority))
}else {
return (NULL)
}
}, cutOffRepression, majority, schmidtMvalues24To6Or8))
print("24To6/8 repressed:")
Probeset24To6or8repressed <- newMat3repressed[, 1]
if (is.null(Probeset24To6or8repressed))
Probeset24To6or8repressed <- c()
Genes24To6or8repressed <- unlist(mapProbesetNames(Probeset24To6or8repressed))
deduplicated24To6or8repressed <- unique(Genes24To6or8repressed)
print(deduplicated24To6or8repressed)
print(paste("In total =", length(Probeset24To6or8repressed), " unqiue =", length(deduplicated24To6or8repressed), sep = ""))
############################### output ################################################################################ todo: add the difference as well, plus the primise genes for
############################### further mining
finalSet <- unique(c(deduplicated6To0induced,
deduplicated6To0repressed,
deduplicated24To0induced,
deduplicated24To0repressed,
deduplicated24To6Or8induced,
deduplicated24To6or8repressed))
print(paste("Total unique genes cross all time points are:", length(finalSet), sep = ""))
print(finalSet)
finalProbesetSet <- unique(c(Probeset6To0induced, Probeset6To0repressed, Probeset24To0induced, Probeset24To0repressed, Probeset24To6Or8induced, Probeset24To6or8repressed))
filtered_timeseries <- lapply(sortedtimeseries2, function(subdata) subdata[rownames(subdata) %in% finalProbesetSet, ])
reproduceSchmidtStudy_res <- list()
reproduceSchmidtStudy_res[["CombinedGeneSet"]] <- finalSet
reproduceSchmidtStudy_res[["SchmidtGenes6To0induced"]] <- deduplicated6To0induced
reproduceSchmidtStudy_res[["SchmidtGenes6To0repressed"]] <- deduplicated6To0repressed
reproduceSchmidtStudy_res[["SchmidtGenes24To0induced"]] <- deduplicated24To0induced
reproduceSchmidtStudy_res[["SchmidtGenes24To0repressed"]] <- deduplicated24To0repressed
reproduceSchmidtStudy_res[["SchmidtGenes24To6Or8induced"]] <- deduplicated24To6Or8induced
reproduceSchmidtStudy_res[["SchmidtGenes24To6or8repressed"]] <- deduplicated24To6or8repressed
reproduceSchmidtStudy_res[["timeseries_original"]] <- sortedtimeseries2
reproduceSchmidtStudy_res[["filtered_timeseries"]] <- filtered_timeseries
reproduceSchmidtStudy_res[["total_ProbesetSet"]] <- finalProbesetSet
reproduceSchmidtStudy_res[["Schmidt6To0inducedMAT"]] <- newMatinduced
reproduceSchmidtStudy_res[["Schmidt6To0repressedMAT"]] <- newMatrepressed
reproduceSchmidtStudy_res[["Schmidt24To0inducedMAT"]] <- newMat2induced
reproduceSchmidtStudy_res[["Schmidt24To0repressedMAT"]] <- newMat2repressed
reproduceSchmidtStudy_res[["Schmidt24To6Or8inducedMAT"]] <- newMat3induced
reproduceSchmidtStudy_res[["Schmidt24To6or8repressedMAT"]] <- newMat3repressed
#save(reproduceSchmidtStudy_res, file = "temp/reproduceSchmidtStudy_temp.Rdata")
reproduceSchmidtStudy_res
}
#'@export
leukeamia_study_with_differential_output <- function(schmidts_realanlysis_output,
method = c("average", "kmeans", "edgeDetector", "scanStatistic"),
maxK = 4,
temporal = 2,
target_genes = c()) {
sortedtimeseries_leukaemia <- schmidts_realanlysis_output$filtered_timeseries
## shouldn't have duplicate row names
convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(sortedtimeseries_leukaemia)$convert_data
finalSet <- unique(schmidts_realanlysis_output$CombinedGeneSet)
if( length(target_genes) > 0) {
finalSet = target_genes
}
# remove duplicate
## the number of genes less than or equal to combined gene set
convertedgenedagta <- lapply(convertedgenedagta, function(subdata)subdata[which(rownames(subdata) %in% finalSet), ])
print(rownames(convertedgenedagta[[1]]))
# timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
if (method == "average") {
timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
} else {
require(BoolNet)
timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
}
# membexp=3 more fussy
genes <- rownames(timeseries_RMA_LogRatio_0.7[[1]])
# build all cubes for all clusters
cubeLeukaemia_RMA_LogRatio_temp <- constructFBNCube(genes, genes, timeseries_RMA_LogRatio_0.7, maxK, temporal, TRUE)
gc()
#.rs.restartR()
save(cubeLeukaemia_RMA_LogRatio_temp, file = "temp/cubeLeukaemia_RMA_LogRatio_temp.Rdata")
totalNetworks_RMA_LogRatio_0.7 <- mineFBNNetwork(cubeLeukaemia_RMA_LogRatio_temp, useParallel = TRUE)
leukeamia_study_with_schmidts_output_res <- list(schmidts_realanlysis_output = schmidts_realanlysis_output, cube = cubeLeukaemia_RMA_LogRatio_temp, network = totalNetworks_RMA_LogRatio_0.7,
timeseries = timeseries_RMA_LogRatio_0.7)
#save(leukeamia_study_with_schmidts_output_res, file = "temp/leukeamia_study_with_schmidts_output_temp.Rdata")
leukeamia_study_with_schmidts_output_res
}
#'
#' #'@export
#' leukeamia_study_with_schmidts_output_cluster <- function(schmidts_realanlysis_output,
#' method = c("average", "kmeans", "edgeDetector", "scanStatistic"),
#' maxK = 4,
#' temporal = 2,
#' minElementInCluster = 10,
#' maxElementInCluster = 20) {
#' sortedtimeseries_leukaemia <- schmidts_realanlysis_output$filtered_timeseries
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(sortedtimeseries_leukaemia)$convert_data
#'
#' finalSet <- unique(schmidts_realanlysis_output$CombinedGeneSet)
#' print(finalSet)
#'
#' # remove duplicate
#' convertedgenedagta <- lapply(convertedgenedagta, function(subdata) subdata[which(rownames(subdata) %in% finalSet), ])
#'
#' print(rownames(convertedgenedagta[[1]]))
#'
#' # timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' totalNetworks_RMA_LogRatio_0.7 <- generateFBMNetwork(timeseries_data = timeseries_RMA_LogRatio_0.7,
#' maxK = 4,
#' max_deep_temporal = 2,
#' useParallel = TRUE,
#' maxGenesForSingleCube = 10,
#' parallel_on_group = TRUE)
#'
#' #getClusteredTimeseries_RMA_LogRatio_0.7 <- dividedDataIntoSubgroups(timeseries_RMA_LogRatio_0.7, maxElementInCluster)
#' # build all cubes for all clusters build all cubes for all clusters
#' #cubeLeukaemia_RMA_LogRatio_temp <- constructFBNCubeAndNetworkInClusters_combine(getClusteredTimeseries_RMA_LogRatio_0.7, maxK = maxK, temporal = temporal, useParallel = TRUE)
#' # merge all networks? seperate by clusters?
#' #networks_RMA_LogRatio_0.7 <- mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_temp)
#' #totalNetworks_RMA_LogRatio_0.7 <- filterNetworkConnections(networks_RMA_LogRatio_0.7)
#'
#' #save(cubeLeukaemia_RMA_LogRatio_temp, file = "temp/cubeLeukaemia_RMA_LogRatio_temp_cluster.Rdata")
#' leukeamia_study_with_schmidts_output_res <- list(schmidts_realanlysis_output = schmidts_realanlysis_output, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' #save(leukeamia_study_with_schmidts_output_res, file = "temp/leukeamia_study_with_schmidts_output_temp.Rdata")
#' leukeamia_study_with_schmidts_output_res
#' }
#'
#' ## files2<-'D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW'
#' ## files1<-c('D:\\Dropbox\\Dropbox\\FBNNet\\ChildhoodLeukeamiaDataFile\\GSE2677_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE13670_RAW\\GSE13670_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE20489_RAW\\GSE20489_RAW','D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE42088_RAW\\GSE42088_RAW'，'D:\\Dropbox\\Dropbox\\FBNNet\\Genome
#' ## Data\\GSE54992_RAW\\GSE54992_RAW'，'D:\\Dropbox\\Dropbox\\FBNNet\\Genome Data\\GSE57194_RAW\\GSE57194_RAW')
#' #'@export
#' leukeamia_study_cluster <- function(cellDirectory,
#' cutOffInduction = 0.7,
#' cutOffRepression = 0.7,
#' majority = 0.5,
#' sortedtimeseries = NULL,
#' useGCRMA = FALSE,
#' method = c("average", "kmeans", "edgeDetector", "scanStatistic")) {
#' # read affy files and normalized by RMA
#' if (is.null(sortedtimeseries)) {
#' sortedtimeseries <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
#' }
#'
#' targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
#' "T-ALL-25")
#' sortedtimeseries_leukaemia <- sortedtimeseries[targetsamples]
#' cond <- sapply(sortedtimeseries_leukaemia, function(entry) !is.null(entry))
#' sortedtimeseries_leukaemia <- sortedtimeseries_leukaemia[cond]
#'
#' # Generate cube 0.7 = 2.0 ^ 0.7
#' probesets <- rownames(sortedtimeseries_leukaemia[[1]])
#' probesetGeneNameMappings <- mapProbesetNames(probesets)
#'
#' diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries_leukaemia, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
#' majority = majority, probesetGeneNameMappings = probesetGeneNameMappings)
#'
#' commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID
#' commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID
#' commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID
#' commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID
#' commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID
#' commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID
#'
#' finalSet <- unique(c(commonGeneSet1i, commonGeneSet1r, commonGeneSet2i, commonGeneSet2r, commonGeneSet3i, commonGeneSet3r))
#' print(finalSet)
#'
#' # remove duplicate
#' subsetgenedata <- lapply(sortedtimeseries, function(subdata) subdata[rownames(subdata) %in% finalSet, ])
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(subsetgenedata)$convert_data
#' print(rownames(convertedgenedagta[[1]]))
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' # membexp=3 more fussy
#' getClusteredTimeseries_RMA_LogRatio_0.7 <- clusterdDiscreteData(convertedgenedagta,timeseries_RMA_LogRatio_0.7, 30)
#' # build all cubes for all clusters
#' cubeLeukaemia_RMA_LogRatio_0.7 <- constructFBNCubeAndNetworkInClusters(getClusteredTimeseries_RMA_LogRatio_0.7, 4, 1, TRUE)
#' # merge all networks? seperate by clusters?
#' networks_RMA_LogRatio_0.7 <- mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_0.7)
#' totalNetworks_RMA_LogRatio_0.7 <- filterNetworkConnections(networks_RMA_LogRatio_0.7)
#'
#' diffgenes <- list(Induction_0_to_6_or_8 = commonGeneSet1i, Repression_0_to_6_or_8 = commonGeneSet1r, Induction_0_to_24 = commonGeneSet2i, Repression_0_to_24 = commonGeneSet2r,
#' Induction_6_or_8_to_24 = commonGeneSet3i, Repression_6_or_8_to_24 = commonGeneSet3r)
#' list(diff_genes = diffgenes, cube = timeseries_RMA_LogRatio_0.7, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' }
#'
#' # use boolnet
#' #'@export
#' leukeamia_study <- function(cellDirectory,
#' cutOffInduction = 1,
#' cutOffRepression = 1,
#' majority = 0.5,
#' sortedtimeseries = NULL,
#' useGCRMA = FALSE,
#' method = c("average",
#' "kmeans", "edgeDetector", "scanStatistic")) {
#' require(BoolNet)
#' # read affy files and normalized by RMA
#' if (is.null(sortedtimeseries)) {
#' sortedtimeseries <- convertAffyRawDataIntoNormalizedStructureData(cellDirectory, useGCRMA = useGCRMA)
#' }
#'
#' targetsamples <- c("B-ALL-13", "B-ALL-17", "B-ALL-24", "B-ALL-31", "B-ALL-32", "B-ALL-33", "B-ALL-37", "B-ALL-38", "B-ALL-40", "B-ALL-43", "T-ALL-2", "T-ALL-20",
#' "T-ALL-25")
#' sortedtimeseries_leukaemia <- sortedtimeseries[targetsamples]
#' cond <- sapply(sortedtimeseries_leukaemia, function(entry) !is.null(entry))
#' sortedtimeseries_leukaemia <- sortedtimeseries_leukaemia[cond]
#'
#' # Generate cube 0.7 = 2.0 ^ 0.7
#' cutOffInduction = cutOffInduction
#' cutOffRepression = cutOffRepression
#' majority <- majority
#' probesets <- rownames(sortedtimeseries_leukaemia[[1]])
#' probesetGeneNameMappings <- mapProbesetNames(probesets)
#'
#' diffgenes_RMA <- identifyDifferentiallyExpressedGenes(sortedtimeseries_leukaemia, cutOffInduction = cutOffInduction, cutOffRepression = cutOffRepression,
#' majority = majority, probesetGeneNameMappings = probesetGeneNameMappings)
#'
#' commonGeneSet1i <- diffgenes_RMA$DifferentialExpression[[1]]$Induced_ProbeID
#' commonGeneSet1r <- diffgenes_RMA$DifferentialExpression[[1]]$Repressed_ProbeID
#' commonGeneSet2i <- diffgenes_RMA$DifferentialExpression[[2]]$Induced_ProbeID
#' commonGeneSet2r <- diffgenes_RMA$DifferentialExpression[[2]]$Repressed_ProbeID
#' commonGeneSet3i <- diffgenes_RMA$DifferentialExpression[[3]]$Induced_ProbeID
#' commonGeneSet3r <- diffgenes_RMA$DifferentialExpression[[3]]$Repressed_ProbeID
#'
#' finalSet <- unique(c(commonGeneSet1i, commonGeneSet1r, commonGeneSet2i, commonGeneSet2r, commonGeneSet3i, commonGeneSet3r))
#' print(finalSet)
#'
#' # remove duplicate
#' subsetgenedata <- lapply(sortedtimeseries, function(subdata) subdata[rownames(subdata) %in% finalSet, ])
#' convertedgenedagta <- convertTimeseriesProbsetNameToGeneName(subsetgenedata)$convert_data
#' print(rownames(convertedgenedagta[[1]]))
#'
#' # timeseries_RMA_LogRatio_0.7<<-discreteTimeSeries(convertedgenedagta,method='average')
#'
#' if (method == "average") {
#' timeseries_RMA_LogRatio_0.7 <- discreteTimeSeries(convertedgenedagta, method = "average")
#' } else {
#' require(BoolNet)
#' timeseries_RMA_LogRatio_0.7 <- BoolNet::binarizeTimeSeries(convertedgenedagta, method = method)$binarizedMeasurements
#' }
#'
#' # membexp=3 more fussy
#' genes <- rownames(timeseries_RMA_LogRatio_0.7[[1]])
#' # build all cubes for all clusters
#' cubeLeukaemia_RMA_LogRatio_0.7 <- constructFBNCube(genes, genes, timeseries_RMA_LogRatio_0.7, 4, 2, TRUE, TRUE)
#' totalNetworks_RMA_LogRatio_0.7 <- mineFBNNetwork(cubeLeukaemia_RMA_LogRatio_0.7)
#'
#' diffgenes <- list(Induction_0_to_6_or_8 = commonGeneSet1i, Repression_0_to_6_or_8 = commonGeneSet1r, Induction_0_to_24 = commonGeneSet2i, Repression_0_to_24 = commonGeneSet2r,
#' Induction_6_or_8_to_24 = commonGeneSet3i, Repression_6_or_8_to_24 = commonGeneSet3r)
#' list(diff_genes = diffgenes, cube = timeseries_RMA_LogRatio_0.7, network = totalNetworks_RMA_LogRatio_0.7, timeseries = timeseries_RMA_LogRatio_0.7)
#' # merge all networks? seperate by clusters? networks_RMA_LogRatio_0.7<<-mergeClusterNetworks(cubeLeukaemia_RMA_LogRatio_0.7)
#' # totalNetworks_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$TotalNetwork totalGenes_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$TotalNetwork$genes
#' # individualNetworks_RMA_LogRatio_0.7<<-networks_RMA_LogRatio_0.7$IndividualNetwork
#' }
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.