mdPat: Multi-Dimensional Gene Set Analysis Pattern Classification.

In mdgsa: Multi Dimensional Gene Set Analysis.

Description

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

Usage

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:

• q1i: block displaced toward quadrant 1 (0 < X & 0 < Y) with interaction.

• q2i: block displaced toward quadrant 2 (0 > X & 0 < Y) with interaction.

• q3i: block displaced toward quadrant 3 (0 > X & 0 > Y) with interaction.

• q4i: block displaced toward quadrant 4 (0 < X & 0 > Y) with interaction.

• q1f: block displaced toward quadrant 1, no interaction.

• q2f: block displaced toward quadrant 2, no interaction.

• q3f: block displaced toward quadrant 3, no interaction.

• q4f: block displaced toward quadrant 4, no interaction.

• xh: block shifted to positive X values.

• xl: block shifted to negative X values.

• yh: block shifted to positive Y values.

• yl: block shifted to negative Y values.

• b13: bimodal block. Half of the genes displaced towards quadrant 1 and the other half towards quadrant 3.

• b24: bimodal block. Half of the genes displaced towards quadrant 2 and the other half towards quadrant 4.

• NS: non significant block.

Value

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

Author(s)

David Montaner [e-mail]

References

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

See Also

mdGsa, uvPat

Examples

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

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