gseaSignificance {phenoTest} | R Documentation

## ES' (enrichment scores) sifgnificance.

### Description

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

This function performs the second step in the process of obtaining a GSEA-like plot. It computes the NES (normalized enrichment score), p values and fdr (false discovery rate) for all variables and signatures. A `gseaSignaturesSign` or `gseaSignaturesVar` object will be needed as input (these objects can be obtained with the `gseaSignatures` function). For an overview of the output use the `summary` method. The next step after using the `gseaSignificance` function would be using the `plot` method.

### Usage

    gseaSignificance(x,p.adjust.method='none',pval.comp.method='original',pval.smooth.tail=TRUE)

### Arguments

`x` | `gseaSignaturesSign` or `gseaSignaturesVar` object obtained with the `gseaSignatures` method. This object contains the enrichment scores ,the simulated enrichment scores and the fold changes or hazard ratios.
`p.adjust.method` | p adjustment method to be used. Common options are 'BH', 'BY', 'bonferroni' or 'none'. All available options and their explanations can be found on the `p.adjust` function manual.
`pval.comp.method` | the p value computation method. Has to be one of 'signed' or 'original'. The default one is 'original'. See details for more information.
`pval.smooth.tail` | if we want to estimate the tail of the ditribution where the pvalues will be generated.

### Details

The simulated enrichment scores and the calculated one are used to find the p value.

P value calculation depends on the parameter `pval.comp.method`. The default value is 'original'. In 'original' we are simply computing the proportion of anbolute simulated ES which are larger than the observed absolute ES. In 'signed' we are computing the proportion of simulated ES which are larger than the observed ES (in case of having positive enrichment score) and the proportion of simulated ES which are smaller than the observed ES (in case of having negative enrichment score).

### Author(s)

Evarist Planet

### References

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

C.A. Tsai and J.J. Chen. Kernel estimation for adjusted p-values in multiple testing. Computational Statistics & Data Analysis http://econpapers.repec.org/article/eeecsdana/v_3a51_3ay_3a2007_3ai_3a8_3ap_3a3885-3897.htm

### Examples

    #for examples see the help file of gseaSigntaures: ?gseaSignatures

[Package phenoTest version 1.38.0 Index]