mdPat {mdgsa} | R Documentation

## Multi-Dimensional Gene Set Analysis Pattern Classification.

### 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 dmontaner@cipf.es

### 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

[Package mdgsa version 1.19.0 Index]