gseaSignatures {phenoTest} | R Documentation

## Compute ES (enrichment scores) and es.sim (simulated enrichment scores) for given phenotipic variable(s) and signature(s).

### Description

This function has been deprecated. You could better use `gsea` instead.

This function computes the first step in the process of obtaining a GSEA-like plot. It computes the enrichment scores and simulated enrichment scores for each variable and signature. The output will usually be used as input for the `gseaSignificance` function. An important parameter of the function is `logScale`. Its default value is TRUE which means that by default the provided scores (i.e. fold changes, hazard ratios) will be log scaled. Remember to change this parameter to FALSE if your scores are already log scaled. The `getEs`, `getEsSim`, `getFc`, `getHr` and `getFcHr` methods can be used to acces each subobject. For more information please visit the man pages of each method.

### Usage

    gseaSignatures(x,gsets,logScale=TRUE,absVals=FALSE,averageRepeats=FALSE,
      B=1000,mc.cores=1,test='perm', minGenes=10,maxGenes=500,center=FALSE)

### Arguments

`x` | `ePhenoTest`, `numeric` or `matrix` object containing hazard ratios or fold changes.
`gsets` | character or list object containing the names of the genes that belong to each signature.
`logScale` | if values should be log scaled.
`absVals` | if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions.
`averageRepeats` | if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names.
`B` | number of simulations to perform.
`mc.cores` | number of processors to use.
`test` | the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test.
`minGenes` | gene sets with less than minGenes genes will be removed from the analysis.
`maxGenes` | gene sets with more than maxGenes genes will be removed from the analysis.
`center` | if we want to center scores (fold changes or hazard ratios). The following is will be done: x = x-mean(x).

### Details

The following preprocessing was done on the provided scores (i.e. fold changes, hazard ratios) to avoid errors during the enrichment score computation: -When having two scores with the same name its average was used. -Zeros were removed. -Scores without names (which can not be in any signature) removed. -Non complete cases (i.e. NAs, NaNs) were removed. ES score was calculated for each signature and variable (see references). If parameter `test` is 'perm' the signature was permutted and the ES score was recalculated (this happened B times for each variable, 1000 by default). If `test` is 'wilcox' a wilcoxon test in which we test the fact that the average value of the genes that do belong to our signtaure is different from the average value of the genes that do not belong to our signature will be performed. If `test` is 'ttperm' a permutation t-test will be used. Take into account that the final plot will be different when 'wilcox' is used.

### Author(s)

Evarist Planet

### References

Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102

### Examples

    #load epheno object
    data(epheno)
    epheno

    #we construct two signatures
    sign1 <- sample(featureNames(epheno))[1:20]
    sign2 <- sample(featureNames(epheno))[50:75]
    mySignature <- list(sign1,sign2)
    names(mySignature) <- c('My first signature','My preferred signature')

    #run gsea functions
    #my.gseaSignatures <- gseaSignatures(x=epheno,signatures=mySignature,B=100,mc.cores=1)
    #my.gseaSignificance <- gseaSignificance(my.gseaSignatures)
    #my.summary <- summary(my.gseaSignificance)
    #my.summary
    #plot(my.gseaSignatures,my.gseaSignificance)

[Package phenoTest version 1.34.0 Index]