gseaSignificance: ES' (enrichment scores) sifgnificance.

In phenoTest: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.

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

phenoTest documentation built on Nov. 8, 2020, 7:53 p.m.