gs.tTest {gage} | R Documentation

## Gene set differential expression test

### Description

These functions test for perturbation of gene sets relative to all genes in the microarray data. They are the testing module for `gage` and single array analysis workflow.

They use different statistical tests: gs.tTest uses two-sample t-test, gs.zTest uses one-sample z-test, gs,KSTest uses Kolmogorov-Smirnov test.

### Usage

    gs.tTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)
    gs.zTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)
    gs.KSTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)

### Arguments

`exprs` | an expression matrix or matrix-like data structure, with genes as rows and samples as columns.
`gsets` | a named list, each element contains a gene set that is a character vector of gene IDs or symbols. For example, type head(kegg.gs). A gene set can also be a "smc" object defined in PGSEA package. Make sure that the same gene ID system is used for both `gsets` and `exprs`.
`set.size` | gene set size (number of genes) range to be considered for enrichment test. Tests for too small or too big gene sets are not robust statistically or informative biologically. Default to be set.size = c(10, 500).
`same.dir` | whether to test for changes in a gene set toward a single direction (all genes up or down regulated) or changes towards both directions simultaneously. For experimentally derived gene sets, GO term groups, etc, coregulation is commonly the case, hence same.dir = TRUE (default); In KEGG, BioCarta pathways, genes frequently are not coregulated, hence it could be informative to let same.dir = FALSE. Although same.dir = TRUE could also be interesting for pathways.
`...` | other arguments to be passed into the secondary functions, not used currently.

### Details

These functions are the gene set test module for `gage` and single array analysis workflow. When used in `gage` function, the function names are optional values for `saaTest` argument. Check help information for `gage` for details.

These functions may also used independently without calling `gage` function.

### Value

As the raw results of gene set tests, a list of 5 components is returned:

`results` | matrix of test statistics, gene sets are rows, samp-ref pairs are columns
`p.results` | matrix of p-values for up-regulation (greater than) tests, gene sets are rows, samp-ref pairs are columns
`ps.results` | matrix of p-values for down-regulation (less than) tests, gene sets are rows, samp-ref pairs are columns
`mstat` | vector of test statistics mean for individual gene sets. Normally, its absoluate value measures the magnitude of gene-set level changes, and its sign indicates direction of the changes. For gs.KSTest, mstat is always positive.
`setsizes` | vector of effective set size (number of genes) individual gene sets

### Author(s)

Weijun Luo <luo_weijun@yahoo.com>

### References

Luo, W., Friedman, M., Shedden K., Hankenson, K. and Woolf, P GAGE: Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161

### See Also

`gage` the main function for GAGE analysis

### Examples

    data(gse16873)
    cn=colnames(gse16873)
    hn=grep('HN',cn, ignore.case =TRUE)
    dcis=grep('DCIS',cn, ignore.case =TRUE)
    data(kegg.gs)

    #kegg test
    exprs.gage = gagePrep(gse16873, ref = hn, samp = dcis)
    str(exprs.gage)
    rawRes = gs.tTest(exprs.gage, gsets = kegg.gs)
    str(rawRes)
    head(rawRes$results)
    head(rawRes$p.results)

[Package gage version 2.30.0 Index]