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]