Wrapper to calculate Discovery Odds Ratios on feature values.

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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

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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

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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)

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