Random Forests-based procedure

Description

A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.

Usage

MAVTp(DATA, GS, nbPerm = 5000, numoftree = 500, type = c("cont", "cate"), impt = TRUE)

Arguments

Value

A list of the p-values of random forests for GSA. The importance measurement of individual genes for those significant gene sets will also be output when impt is set TRUE.

Note

R > 2.14.0

Author(s)

Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen

References

H.M. Hsueh, et al. (2013) Random forests-based differential analysis of gene sets for gene expression data. Gene, 518, 179-186.

Examples

data(data)
data(GS)
a=proc.time()
MAVTp(data,GS, nbPerm = 50, numoftree = 500, type = "cate", impt = TRUE)
proc.time()-a

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MAVTgsa)
Loading required package: corpcor
Loading required package: foreach
Loading required package: multcomp
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS

Attaching package: 'TH.data'

The following object is masked from 'package:MASS':

    geyser

Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MAVTgsa/MAVTp.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MAVTp
> ### Title: Random Forests-based procedure
> ### Aliases: MAVTp
> ### Keywords: Random forests
> 
> ### ** Examples
> 
> 
> data(data)
> data(GS)
> a=proc.time()
> MAVTp(data,GS, nbPerm = 50, numoftree = 500, type = "cate", impt = TRUE)
$p.value
[1] 0.18 0.18 0.04 0.26

$importance
$importance[[1]]
$importance[[1]][[1]]
$importance[[1]][[1]][[1]]
$importance[[1]][[1]][[1]][[1]]
[1] "importance measure"

$importance[[1]][[1]][[1]]$Imp
          3 MeanDecreaseAccuracy
  1.2394966           7.16347651
  4.3304974           5.45578411
 -0.6796800           1.62102351
 -1.7019650          -1.81792348
 -2.6858343          -2.50624022
  1.2327842           1.18220578
 -0.2828653           1.17038642
 -0.5775428          -0.07441338


$importance[[1]][[1]]$Imp
          3 MeanDecreaseAccuracy
  1.9483441          7.380194716
  3.6261109          4.528814775
  0.0000000          1.638848009
 -0.2773714          0.006369427
 -0.7883281          0.026744191
 -0.9307865         -0.866663570
 -0.6491591          1.579701697
 -1.2519577         -0.676084452


$importance[[1]]$Imp
          3 MeanDecreaseAccuracy
  2.2194838            8.2443947
  4.3958698            5.6274731
 -0.7262489            0.7090396
  0.8688984            2.5827522
  1.5131112           -0.7360977
 -2.0498002           -1.9224119
  0.4286502           -1.0584243
  1.0702688            2.7853094


$importance$Imp
          3 MeanDecreaseAccuracy
  2.7993791             7.626355
  4.3592078             6.004271
  0.6530317             1.770872
 -0.9279707            -1.848090
  4.0669595             3.494217


> proc.time()-a
   user  system elapsed 
  0.740   0.004   0.744 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>