Description Usage Arguments Value Note Author(s) References Examples
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.
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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. |
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.
R > 2.14.0
Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
H.M. Hsueh, et al. (2013) Random forests-based differential analysis of gene sets for gene expression data. Gene, 518, 179-186.
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