TestGeneSets {GSAR} | R Documentation

## Test a List of Gene Sets Using a Specific Statistical Method

### Description

A wrapper function that invokes a specific statistical method from the ones available in package GSAR (see Rahmatallah et. al. 2014, Rahmatallah et. al. 2012 and Friedman and Rafsky 1979 for details) to test a list of gene sets in a sequential order and returns results in a list object.

### Usage

    TestGeneSets(object, group, geneSets=NULL, min.size=10, max.size=500,
    test=NULL, nperm=1000, mst.order=1, pvalue.only=TRUE)

### Arguments

`object` | a numeric matrix with columns and rows respectively corresponding to samples and features.
`group` | a numeric vector indicating group associations for samples. Possible values are 1 and 2.
`geneSets` | a list of character vectors providing the identifiers of features to be considered in each gene set.
`min.size` | a numeric value indicating the minimum allowed gene set size. Default value is 10.
`max.size` | a numeric value indicating the maximum allowed gene set size. Default value is 500.
`test` | a character parameter indicating which statistical method to use for testing the gene sets. Must be one of “`GSNCAtest`”, “`WWtest`”, “`KStest`”, “`MDtest`”, “`RKStest`”, “`RMDtest`”.
`nperm` | number of permutations used to estimate the null distribution of the test statistic. If not given, a default value 1000 is used.
`mst.order` | numeric value to indicate the consideration of the union of the first `mst.order` MSTs when “`RKStest`” or “`RMDtest`” are used. Default value is 1. Maximum allowed value is 5.
`pvalue.only` | logical. If `TRUE` (default), the p-value is returned. If `FALSE` a list of length three containing the observed statistic, the vector of permuted statistics, and the p-value is returned.

### Details

This is a wrapper function that facilitates the use of any statistical method in package GSAR for multiple gene sets that are provided in a list object. The function filters out any gene that is abscent in the considered data (input parameter `object`) from the gene sets and discard any set that is too small in size (has less than `min.size` genes) or too large (has more than `max.size` genes). The function performs the specified method for all the remaining gene sets in a sequential order and return results in a list object.

### Value

A list object of length equals the length of the provided gene set list. When `pvalue.only=TRUE` (default), each item in the returned list by function `TestGeneSets` consists of a numeric p-value indicating the attained significance level obtained by the specified method. When `pvalue.only=FALSE`, each item in the returned list is a list of length 3 with the following components:

`statistic` | the value of the observed test statistic.
`perm.stat` | numeric vector of the resulting test statistic for `nperm` random permutations of sample labels.
`p.value` | p-value indicating the attained significance level.

### Author(s)

Yasir Rahmatallah and Galina Glazko

### References

Rahmatallah Y., Emmert-Streib F. and Glazko G. (2014) Gene sets net correlations analysis (GSNCA): a multivariate differential coexpression test for gene sets. Bioinformatics 30, 360–368.

Rahmatallah Y., Emmert-Streib F. and Glazko G. (2012) Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics 28, 3073–3080.

Friedman J. and Rafsky L. (1979) Multivariate generalization of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697–717.

### See Also

`GSNCAtest`, `WWtest`, `MDtest`, `KStest`, `RKStest`, `RMDtest`.

### Examples

    ## generate a feature set of size 50 in two conditions
    ## where each condition has 20 samples
    ## use multivariate normal distribution
    library(MASS)
    ngenes <- 50
    nsamples <- 40
    ## let the mean vector have zeros of length 50 for both conditions
    zero_vector <- array(0,c(1,ngenes))

    ## set the covariance matrix to be an identity matrix for both conditions
    cov_mtrx <- diag(ngenes)
    gp <- mvrnorm(nsamples, zero_vector, cov_mtrx)
    ## apply a mean shift of 5 to the first 10 features under condition 1
    gp[1:20,1:10] <- gp[1:20,1:10] + 5
    dataset <- aperm(gp, c(2,1))
    ## assign a unique identifier to each gene
    rownames(dataset) <- as.character(c(1:ngenes))
    ## first 20 samples belong to condition 1
    ## second 20 samples belong to condition 2
    sample.labels <- c(rep(1,20),rep(2,20))
    ## construct 3 named gene sets such that they respectively consist of
    ## genes 1 to 20, 11 to 40, and 31 to 50. Notice that gene sets
    ## can have intersections and can be of different sizes
    ## Sine only the first 10 genes have a significant difference between
    ## the two conditions the only the first gene set (set1) returns a
    ## small p-value when KStest is selected
    geneSets <- list("set1"=as.character(c(1:20)), "set2"=as.character(c(11:40)),
    "set3"=as.character(c(31:40)))
    results <- TestGeneSets(object=dataset, group=sample.labels,
    geneSets=geneSets, test="KStest")

[Package GSAR version 1.16.0 Index]