path_meta {crossmeta} | R Documentation

## Pathway p-value meta analysis.

### Description

Uses Fisher's method to combine p-values from PADOG pathway analyses.

### Usage

    path_meta(path_anals, ncores = parallel::detectCores(), nperm = ncores *
      10000, by_source = FALSE)

### Arguments

`path_anals` | Previous result of `diff_path`, which can be reloaded using `load_path`.
`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 `add_sources`?

### Details

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.

### Value

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.

### See Also

`sumlog`, `padog`.

### Examples

    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)

[Package crossmeta version 1.6.0 Index]