path_meta {crossmeta} | R Documentation |
Uses Fisher's method to combine p-values from PADOG pathway analyses.
path_meta(path_anals, ncores = parallel::detectCores(), nperm = ncores * 10000, by_source = FALSE)
path_anals |
Previous result of |
ncores |
Number of cores to use. Default is all available. |
nperm |
Number of permutation to perform to calculate p-values. |
by_source |
Should seperate meta-analyses be performed for each tissue
source added with |
Permutation p-values are determined by shuffling pathway names associated with PADOG p-values prior to meta-analysis. Permutation p-values are then adjusted using the Benjamini & Hochberg method to obtain false discovery rates.
A list of matrices, one for each tissue source. Each matrix contains a column of PADOG p-values for each contrast and permutation p- and fdr-values for the meta analysis.
library(lydata) # location of data data_dir <- system.file("extdata", package = "lydata") # gather GSE names gse_names <- c("GSE9601", "GSE15069", "GSE50841", "GSE34817", "GSE29689") # load previous pathway analyses # path_anals <- load_path(gse_names, data_dir) # perform pathway meta analysis # path_res <- path_meta(path_anals, ncores = 1, nperm = 100)