runGSAhyper {piano} | R Documentation

## Gene set analysis with Fisher's exact test

### Description

Performs gene set analysis (GSA) based on a list of significant genes and a gene set collection, using Fisher's exact test, returning the gene set p-values.

### Usage

    runGSAhyper(genes, pvalues, pcutoff, universe, gsc, gsSizeLim = c(1,
      Inf), adjMethod = "fdr")

### Arguments

`genes` | a vector of all genes in your experiment, or a small list of significant genes.
`pvalues` | a vector (or object to be coerced into one) of pvalues for genes or a binary vector with 0 for significant genes. Defaults to rep(0,length(genes)), i.e. genes is a vector of genes of interest.
`pcutoff` | p-value cutoff for significant genes. Defaults to 0 if pvalues are binary. If p-values are spread in [0,1] defaults to 0.05.
`universe` | a vector of genes that represent the universe. Defaults to genes if pvalues are not all 0. If pvalues are all 0, defaults to all unique genes in gsc.
`gsc` | a gene set collection given as an object of class `GSC` as returned by the `loadGSC` function.
`gsSizeLim` | a vector of length two, giving the minimum and maximum gene set size (number of member genes) to be kept for the analysis. Defaults to `c(1,Inf)`.
`adjMethod` | the method for adjusting for multiple testing. Can be any of the methods supported by `p.adjust`, i.e. `"holm"`, `"hochberg"`, `"hommel"`, `"bonferroni"`, `"BH"`, `"BY"`, `"fdr"` or `"none"`.

### Details

The statistical test performed is a one-tailed Fisher's exact test on the contingency table with columns "In gene set" and "Not in gene set" and rows "Significant" and "Non-significant" (this is equivalent to a hypergeometric test).

Command run for gene set i:

`fisher.test(res$contingencyTable[[i]], alternative="greater")`,

the `res$contingencyTable` object is available from the object returned from `runGSAhyper`.

The main difference between `runGSA` and `runGSAhyper` is that `runGSA` uses the gene-level statistics (numerical values for each gene) to calculate the gene set p-values, whereas `runGSAhyper` only uses the group membership of each gene (in/not in gene set, significant/non-significant). This means that for `runGSAhyper` a p-value cut-off for determining significant genes has to be chosen by the user and after this, all significant genes will be seen as equally significant (i.e. the actual p-values are not used). The advantage with `runGSAhyper` is that you can use it to find enriched gene sets when you only have a list of interesting genes, without any statistics.

### Value

A list-like object containing the following elements:

`pvalues` | a vector of gene set p-values
`p.adj` | a vector of gene set p-values, adjusted for multiple testing
`resTab` | a full result table
`contingencyTable` | a list of the contingency tables used for each gene set
`gsc` | the input gene set collection

### Author(s)

Leif Varemo piano.rpkg@gmail.com and Intawat Nookaew piano.rpkg@gmail.com

### See Also

piano, `loadGSC`, `runGSA`, `fisher.test`, `phyper`, `networkPlot`

### Examples

       # Load example input data (dummy p-values and gene set collection):
       data("gsa_input")

       # Load gene set collection:
       gsc <- loadGSC(gsa_input$gsc)

       # Randomly select 100 genes of interest (as an example):
       genes <- sample(unique(gsa_input$gsc[,1]),100)

       # Run gene set analysis using Fisher's exact test:
       res <- runGSAhyper(genes, gsc=gsc)

       # If you have p-values for the genes and want to make a cutoff for significance:
       genes <- names(gsa_input$pvals) # All gene names
       p <- gsa_input$pvals # p-values for all genes
       res <- runGSAhyper(genes, p, pcutoff=0.001, gsc=gsc)

       # If the 20 first genes are the interesting/significant ones they can be selected
       # with a binary vector:
       significant <- c(rep(0,20),rep(1,length(genes)-20))
       res <- runGSAhyper(genes, significant, gsc=gsc)

[Package piano version 2.2.0 Index]