diff_path {crossmeta} | R Documentation |
Performs PADOG pathway analysis using KEGG database (downloaded Feb 2017).
diff_path(esets, prev_anals, data_dir = getwd())
esets |
List of annotated esets. Created by |
prev_anals |
Previous result of |
data_dir |
String specifying directory for GSE folders. |
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).
List of named lists, one for each GSE. Each named list contains:
padog_tables |
data.frames containing |
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. |
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.
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)