uvGsa: Uni-Variate Gene Set Analysis.

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Performs a Uni-Variate Gene Set Analysis using a logistic regression model.

Usage

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uvGsa(index, annot, p.adjust.method = "BY", family = quasibinomial(),
  verbose = TRUE, verbosity = 100, fulltable = FALSE, ...)

Arguments

index

ranking index, generally a numerical named vector.

annot

an annotation list.

p.adjust.method

p-value adjustment method for multiple testing.

family

see glm.fit.

verbose

verbose.

verbosity

integer indicating which iterations should be indicated if verbose = TRUE.

fulltable

if TRUE, 'sd', 't' and 'convergence' indicator from the glm fit are included in the output.

...

further arguments to be pasted to glm.fit, for instance 'weights'.

Details

'index' may also be a numerical matrix or data.frame. If such a matrix has more than one column, the ranking index is taken form the first one. The remaining columns are used as covariates to correct for within the analysis.

Default p-value correction is "BY".

Value

A data.frame with a row for each Gene Set or block. Columns are:

N:

number of genes annotated to the Gene Set.

lor:

log Odds Ratio estimated for the Gene Set.

pval:

p-values associated to each log Odds Ratio.

padj:

adjusted p-values.

sd:

standard deviations associated to each log Odds Ratio.

t:

t statistic associated to each log Odds Ratio.

Author(s)

David Montaner dmontaner@cipf.es

See Also

mdGsa, uvPat, glm.fit, p.adjust

Examples

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rindex <- rnorm (1000)
names (rindex) <- paste0 ("gen", 1:1000)

annotList <- list (geneSet1 = sample (names (rindex), size = 10),
                   geneSet2 = sample (names (rindex), size = 15),
                   geneSet3 = sample (names (rindex), size = 20))

res <- uvGsa (rindex, annotList)
res

mdgsa documentation built on Nov. 8, 2020, 5:05 p.m.