mdPat: Multi-Dimensional Gene Set Analysis Pattern Classification.

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

Description

Classifies significant patterns form a Multi-Variate Gene Set Analysis.

Usage

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mdPat(gsaout, cutoff = 0.05, pvalue = "padj")

Arguments

gsaout

data.frame; output from mdGsa.

cutoff

p-value cutoff for considering significant a Gene Set.

pvalue

p-value column to be used. Default is named "padj" as in mdGsa output.

Details

Sign of the three 'lor' and p-values are used to classify functional blocks. The classification is done in the two dimensional space previously analyzed by mdGsa.

All possible functional block classifications in the bi-dimensional gene set analysis are:

Value

A character vector indicating the pattern associated to each Gene Set.

Author(s)

David Montaner dmontaner@cipf.es

References

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

See Also

mdGsa, uvPat

Examples

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N <- c (10, 20, 30, 40)
lor.X <- c (1.45, -0.32, 1.89, -1.66)
lor.Y <- c (2.36, -1.86, 0.43, -2.01)
lor.I <- c (0.89, -0.12, 0.24,  3.55)
pval.X <- c (0.001, 0.002, 0.003, 0.06)
pval.Y <- c (0.002, 0.003, 0.06,  0.07)
pval.I <- c (0.003, 0.02,  0.05,  0.08)
padj.X <- p.adjust (pval.X, "BY")
padj.Y <- p.adjust (pval.Y, "BY")
padj.I <- p.adjust (pval.I, "BY")

mdGsa.res <- as.data.frame (cbind (N,
                                   lor.X, lor.Y, lor.I,
                                   pval.X, pval.Y, pval.I,
                                   padj.X, padj.Y, padj.I))
mdGsa.res

mdGsa.res[,"pat"] <- mdPat (mdGsa.res)
mdGsa.res

dmontaner/mdgsa documentation built on May 15, 2019, 9:35 a.m.