LRpath: Testing GO terms or KEGG with logistic regression

In uc-bd2k/CLEAN: Clustering Enrichment Analysis

Testing GO terms or KEGG with logistic regression

Description

This function uses logistic regression to test for enriched biological categories in gene expression data. Our method models the probability of a randomly selected gene belonging to a specific category given the significance level of that gene. For categories significantly affected by the experimental condition, this probability will increase as the significance statistic increases. Categories with significant p-values and positive slope coefficients are enriched with differentially expressed genes.

Usage

LRpath(sigvals, geneids, min.g=10, max.g=NA, sig.cutoff=0.05, database="GO", functionalCategories=NULL, 
	odds.min.max=c(0.001,0.5), species="Hs") 

Arguments

sigvals	A vector of p-values, same length and order as "geneids"
geneids	A vector of Entrez gene IDs, may contain duplicates and missing values
min.g	The minimum number of unique gene IDs analyzed in category to be tested, default = 10
max.g	The maximum number of unique gene IDs analyzed in category to be tested, default = NA (99999)
sig.cutoff	Entrez gene IDs in each category with p-values<sig.cutoff will be returned, default = 0.05
database	Deprecated. Please use 'functionalCategories' instead.
functionalCategories	Functional categories to be tested- currently, options include "GO", "KEGG" and various other categories, default = "GO". Can be provided by function getFunctionalCategories().
odds.min.max	Lower and upper p-values to be used for odds ratio calculation, default= c(0.001, 0.5)
species	Species to further specify database, human="Hs", mouse="Mm", rat="Rn", etc. Default ="Hs".

Details

LRpath: testing GO terms or KEGG with logistic regression Written by: Maureen Sartor, University of Cincinnati, 2008

This function uses logistic regression to test for enriched biological categories in gene expression data. Our method models the probability of a randomly selected gene belonging to a specific category given the significance level of that gene. For categories significantly affected by the experimental condition, this probability will increase as the significance statistic increases. Categories with significant p-values and positive slope coefficients are enriched with differentially expressed genes.

Please acknowledge your use of LRpath in publications by referencing the Sartor et al. (2009) paper.

Value

Object is a dataframe with the following columns: GO or KEGG ID - posterior t-value for IBMT GO or KEGG term - name of category Ontology (only for GO) - BP, MF, or CC n.genes - Number of unique Entrez Gene IDs in category coeff - coefficient of slope (only GO terms with positive values are significant) odds.ratio - Odds ratio, as measure of strenth of enrichment p.value - P-value that slope does not equal zero (that term is enriched) FDR - False Discovery Rate (Benjamini & Hochberg, 1995) sig.genes - comma separated Entrez gene ids in category with p-value<"sig.cutoff"

Author(s)

Maureen Sartor

References

Sartor MA, Leikauf GD, Medvedovic M. 2009. LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25(2):211-7.

See Also

glm, GO.db, KEGG.db, gimmR

Examples

data(gimmOut)
p <- rbeta(94, 0.5, 2)
LRpath(sigvals=p, geneids=gimmOut$clustData[,1], functionalCategories="GO", species="Rn")

uc-bd2k/CLEAN documentation built on Sept. 22, 2022, 4:12 a.m.