fitDO: Wrapper to calculate Discovery Odds Ratios on feature values.

In HCBravoLab/metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Wrapper to calculate Discovery Odds Ratios on feature values.

Description

This function returns a data frame of p-values, odds ratios, lower and upper confidence limits for every row of a matrix. The discovery odds ratio is calculated as using Fisher's exact test on actual counts. The test's hypothesis is whether or not the discovery of counts for a feature (of all counts) is found in greater proportion in a particular group.

Usage

fitDO(obj, cl, norm = TRUE, log = TRUE, adjust.method = "fdr", cores = 1, ...)

Arguments

obj	A MRexperiment object with a count matrix, or a simple count matrix.
cl	Group comparison
norm	Whether or not to normalize the counts - if MRexperiment object.
log	Whether or not to log2 transform the counts - if MRexperiment object.
adjust.method	Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See p.adjust for more details.
cores	Number of cores to use.
...	Extra options for makeCluster

Value

Matrix of odds ratios, p-values, lower and upper confidence intervals

See Also

cumNorm fitZig fitPA fitMeta

Examples

data(lungData)
k = grep("Extraction.Control",pData(lungData)$SampleType)
lungTrim = lungData[,-k]
lungTrim = lungTrim[-which(rowSums(MRcounts(lungTrim)>0)<20),]
res = fitDO(lungTrim,pData(lungTrim)$SmokingStatus);
head(res)

HCBravoLab/metagenomeSeq documentation built on March 17, 2024, 3:21 p.m.