diff_path {crossmeta}R Documentation

Differential expression of KEGG pathways.

Description

Performs PADOG pathway analysis using KEGG database (downloaded Feb 2017).

Usage

diff_path(esets, prev_anals, data_dir = getwd())

Arguments

esets

List of annotated esets. Created by load_raw.

prev_anals

Previous result of diff_expr, which can be reloaded using load_diff.

data_dir

String specifying directory for GSE folders.

Details

If you wish to perform source-specific pathway meta-analyses, add_sources must be used before diff_paths.

For each GSE, analysis results are saved in the corresponding GSE folder in data_dir that was created by get_raw. PADOG outperforms other pathway analysis algorithms at prioritizing expected pathways (see references).

Value

List of named lists, one for each GSE. Each named list contains:

padog_tables

data.frames containing padog pathway analysis results for each contrast.

If add_sources is used first:

sources

Named vector specifying selected sample source for each contrast. Vector names identify the contrast.

pairs

List of character vectors indicating tissue sources that should be treated as the same source for subsequent pathway meta-analysis.

References

Tarca AL, Bhatti G, Romero R. A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity. Chen L, ed. PLoS ONE. 2013;8(11):e79217. doi:10.1371/journal.pone.0079217.

Dong X, Hao Y, Wang X, Tian W. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights. Scientific Reports. 2016;6:18871. doi:10.1038/srep18871.

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 esets
esets <- load_raw(gse_names, data_dir)

# load previous differential expression analysis
anals <- load_diff(gse_names, data_dir)

# add tissue sources to perform seperate meta-analyses for each source (recommended)
# anals <- add_sources(anals)

# perform pathway analysis for each contrast
# path_anals <- diff_path(esets, anals, data_dir)

[Package crossmeta version 1.6.0 Index]