mdgsa (version 1.4.2)

mdGsa: Multi-Dimensional Gene Set Analysis.

Description

Performs a Multi-Variate Gene Set Analysis for two genomic measurements.

Usage

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

Arguments

index

ranking index, generally a two column matrix.

annot

an annotation list.

p.adjust.method

p-value adjustment method for multiple testing.

family

see glm.

verbose

verbose.

verbosity

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

fulltable

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

useColnames

if TRUE the names of the two first columns of the matrix 'index' are used in the results data.frame.

...

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

Value

• A data.frame with a row for each Gene Set or block. Columns are: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object] Apart from the 'N' coefficient, all other indices appear in triplicate: one coefficient for each genomic condition and a third one for the interaction.

Details

'index' must be a numerical matrix or data.frame with at least two columns.

If there are more than three columns, the ranking indexes are taken form the two first one. The remaining columns are used as covariates to correct for within the analysis.

Default p-value correction is "BY".

In the output data.frame there are three parameters of each type: 'lor', 'pval', ...one for each of the two genomic conditions analyzed and the third one for the interaction between them.

If available, names of the fist two columns of the index matrix are used in the output data.frame. Changing the order of these two first columns will change the report order, but will not change the interpretation of the results. See Montaner et al. (2010) for further details on the algorithm.

References

Montaner et al. (2010) "Multidimensional Gene Set Analysis of Genomic Data." PLoS ONE.

See Also

uvGsa, mdPat, glm.fit, p.adjust

Examples

rindexMat <- matrix (rnorm (2000), ncol = 2)
colnames (rindexMat) <- c ("genomicVar1", "genomicVar2")
rownames (rindexMat) <- paste0 ("gen", 1:1000)

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

res <- mdGsa (rindexMat, annotList)
res