IGST.weight.bootmrmrsvm: Computation of weights for informative genes or gene set...

In IGST: Informative Gene Selection Tool

Description

The function computes the weights associated with each genes for a given dataset using SVM and MRMR feature selection technique with bootstrapping procedure.

Usage

IGST.weight.bootmrmrsvm (x, y, re, v)

Arguments

x	x is a n by p data frame of gene expression values where rows represent genes and columns represent samples. Each cell entry represents the expression level of a gene in a sample or subject (row names of x as gene names or gene ids).
y	y is a p by 1 numeric vector with entries 1 or -1 representing sample labels, where, 1\/-1 represents the sample label of subjects or samples for stress or control condition(for two class problems).
v	v is a scalar representing the weightage of a method and must be within 0 and 1.
re	re is a scalar representing the number of bootstrap generated, re must be sufficiently large (i.e. number of times bootstrap samples are generated.

Value

The function returns a vector of weights associated with each genes computed from SVM and MRMR feature selection technique with bootstrapping procedure for a given dataset.

Author(s)

Nitesh Kumar Sharma, Dwijesh Chandra Mishra, Neeraj Budhlakoti and Md. Samir Farooqi

References

Wang, J., Chen, L., Wang, Y., Zhang, J., Liang, Y., & Xu, D. (2013). A computational systems biology study for understanding salt tolerance mechanism in rice. PLoS One, 8(6), e64929.

Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(02), 185-205.

Mishra DC, Kumar S, Lal SB, Saha A, Chaturvedi KK, Budhlakoti N, et al.( 2018) TAGPT: A Web Server for Prediction of Trait Associated Genes using Gene Expression Data. Annals of Genetics and Genetic Disorder. 1(1): 1003.

Examples

#################################
library(IGST)
data(rice_cold)
x<-rice_cold[-1,]
y<-rice_cold[1,]
y<-as.matrix(y)
y<-as.vector(y)
#s<-10
#Q<-0.5
v<-0.5
re<-10
IGST.weight.bootmrmrsvm (x, y, re, v)

Example output

  [1] 4.126273e-01 1.766711e+00 1.549642e+00 1.341809e-01 1.215618e+00
  [6] 4.367231e-01 5.158822e-02 7.646560e-02 1.316665e-01 2.611074e+00
 [11] 2.094938e-02 9.529526e-02 2.386444e-01 2.527871e-01 5.359878e-02
 [16] 1.215776e-03 4.048369e-02 2.434713e-01 8.625074e-02 9.477831e-03
 [21] 1.319513e-02 1.179517e-02 1.422376e+00 1.432472e-03 1.117485e-01
 [26] 4.495849e-01 1.443536e+01 6.756029e-02 7.953080e-02 1.221467e+00
 [31] 3.353843e-01 1.026145e-01 2.240908e-02 4.055536e-03 1.102359e+00
 [36] 6.710691e-02 3.239436e-01 3.994037e+00 8.995259e-01 4.544809e-03
 [41] 2.436556e-01 8.306875e-01 9.840337e-02 3.367817e-02 1.068160e+00
 [46] 3.771382e-01 1.779214e-01 2.435679e+00 2.161562e-03 6.656067e-02
 [51] 1.552458e-01 1.877861e-02 3.876687e-01 6.516755e-01 7.696792e-02
 [56] 8.620792e-02 1.143075e-02 6.765394e-01 4.240087e-02 2.663039e-01
 [61] 3.297050e-03 3.008939e-02 4.933949e-01 6.488863e-02 2.478881e-01
 [66] 2.432176e-02 1.750209e-01 4.262543e-01 2.744566e-01 3.505249e-03
 [71] 3.333767e-01 9.481941e-03 1.429355e-01 2.122421e-01 2.930641e-01
 [76] 9.651883e-01 9.504086e-01 8.542315e-01 1.517515e-01 3.289279e-03
 [81] 3.453957e-02 8.562028e-02 2.980974e-02 2.935417e-01 4.430654e-01
 [86] 4.004068e-01 9.279368e-02 1.099170e-01 4.462526e-02 2.967794e-01
 [91] 1.523476e-01 2.065250e-02 5.952227e-03 2.128581e-03 2.513197e-01
 [96] 6.790115e-01 2.282588e-01 2.640860e-01 1.145872e+00 3.600364e-02
[101] 1.597516e-02 2.937921e-01 5.264845e-03 2.824864e-02 6.288155e-03
[106] 1.137611e+00 7.263428e-04 7.355010e-02 5.796196e-02 7.451652e-03
[111] 1.463292e-03 4.483976e-03 6.600246e-03 4.231507e-02 1.402664e+00
[116] 3.402619e-02 8.550899e-01 1.595443e-03 3.214937e-02 1.285119e+00
[121] 1.293803e-01 4.716706e-03 1.667864e-01 2.261679e-01 6.850826e-01
[126] 1.971498e-01 2.190080e-01 1.332816e-02 3.399511e-01 4.093432e-02
[131] 1.962522e-01 5.774291e-02 1.941579e-01 4.101623e-02 2.773175e-01
[136] 3.561902e-02 4.867108e-02 7.089748e-02 2.084179e-01 1.085287e-01
[141] 1.616184e-01 3.737437e-01 6.948077e-02 7.625069e-02 4.108617e-01
[146] 1.652398e-01 8.498431e-02 3.823636e-03 3.675217e-02 5.679350e-01
[151] 1.529573e-02 9.394486e-01 4.140240e-02 1.756669e-02 3.968930e-01
[156] 2.098349e-01 3.476135e-01 3.337724e-01 7.857714e-01 3.653201e-01
[161] 2.897627e-02 4.121222e-03 4.477845e-03 2.732684e-02 1.010357e+00
[166] 1.377588e-01 1.160220e-03 1.410670e-02 8.938782e-03 4.753391e-01
[171] 7.104598e-03 4.250841e-01 3.382717e-01 6.167237e-01 7.464033e+00
[176] 3.504645e-01 4.492974e-01 2.102032e-02 9.623668e-02 2.563344e-02
[181] 1.543010e-02 1.882175e-01 7.411616e-02 1.822586e-01 2.294166e-01
[186] 4.951772e-01 3.149164e+01 7.131665e-02 2.514511e-01 2.100043e+00
[191] 2.930171e+01 1.198937e-02 6.017592e-02 5.192162e-01 1.190088e-03
[196] 1.678137e-01 4.589452e-02 1.065471e+01 2.257617e-01 1.532687e-01
[201] 3.154863e-02 7.172188e-01 1.240437e-01 1.334842e-01 8.093626e-02
[206] 8.766276e+00 5.435413e-02 2.423330e-02 6.486721e-02 2.225836e-03
[211] 5.290141e-02 2.476479e-01 7.952332e-02 9.541991e-01 4.537712e-02
[216] 1.084243e-03 1.964576e-01 2.621078e-02 9.219880e-02 5.985595e-02
[221] 9.899681e-01 3.212049e-01 1.093600e-02 2.740718e+00 1.854571e-02
[226] 3.129730e-01 5.265676e-03 1.801977e+00 1.542574e+00 7.418646e-02
[231] 1.586535e-02 6.477663e-01 1.752544e-02 1.004190e+00 3.856333e+00
[236] 1.161200e+00 4.174659e-01 2.417286e-01 7.532540e-02 1.477968e-01
[241] 1.010835e-02 4.017955e-02 7.126622e-03 6.891822e-03 2.284583e-01
[246] 7.431742e-02 1.523972e-01 7.078047e-01 1.433271e-01 2.645708e-01
attr(,"class")
[1] "Weight values"

IGST documentation built on Jan. 31, 2020, 5:07 p.m.