MAVTp: Random Forests-based procedure

Description Usage Arguments Value Note Author(s) References Examples

View source: R/MAVTgsa.R

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

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

Arguments

DATA

a gene expression data matrix with samples in columns. The first row contains the information of the experimental condition of each sample. The remaining rows contain gene expression.

GS

an m x k binary matrix with code (0, 1), where k is the number of gene sets. Each column represents a pre-defined gene set.

nbPerm

the number of permutation specified

numoftree

the number of trees to grow

type

This can be one of "cont" (continuous phenotypes) and "cate" (categorical phenotypes).

impt

If TRUE (default), the importance measurement will be output.

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

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

MAVTgsa documentation built on May 29, 2017, 8 p.m.