pDis {ROntoTools} | R Documentation

## Primary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genes

### Description

Primary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genes

### Usage

    pDis(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE,
      cluster = NULL, seed = NULL)

### Arguments

`x` | named vector of log fold changes for the differentially expressed genes; `names(x)` must use the same id's as `ref` and the nodes of the `graphs`
`graphs` | list of pathway graphs as objects of type `graph` (e.g., `graphNEL`); the graphs must be weighted graphs (i.e., have an attribute `weight` for both nodes and edges)
`ref` | the reference vector for all genes in the analysis; if the reference is not provided or it is identical to `names(x)` a cut-off free analysis is performed
`nboot` | number of bootstrap iterations
`verbose` | print progress output
`cluster` | a cluster object created by makeCluster for parallel computations
`seed` | an integer value passed to set.seed() during the boostrap permutations

### Details

See details in the cited articles.

### Value

An object of class `pDisRes-class`.

### Author(s)

Calin Voichita, Sahar Ansari and Sorin Draghici

### References

Voichita C., Donato M., Draghici S.: "Incorporating gene significance in the impact analysis of signaling pathways", IEEE Machine Learning and Applications (ICMLA), 2012 11th International Conference on, Vol. 1, p.126-131, 2012 Ansari, S., Voichita, C., Donato, M., Tagett, R., & Draghici, S. A Novel Pathway Analysis Approach Based on the Unexplained Disregulation of Genes.

### See Also

`Summary`, `keggPathwayGraphs`, `setNodeWeights`, `setEdgeWeights`

### Examples

    # load a multiple sclerosis study (public data available in Array Express
    # ID: E-GEOD-21942)
    # This file contains the top table, produced by the limma package with
    # added gene information. All the probe sets with no gene associate to them,
    # have been removed. Only the most significant probe set for each gene has been
    # kept (the table is already ordered by p-value)
    # The table contains the expression fold change and signficance of each
    # probe set in peripheral blood mononuclear cells (PBMC) from 12 MS patients
    # and 15 controls.
    load(system.file("extdata/E-GEOD-21942.topTable.RData", package = "ROntoTools"))
    head(top)

    # select differentially expressed genes at 1% and save their fold change in a
    # vector fc and their p-values in a vector pv
    fc <- top$logFC[top$adj.P.Val <= .01]
    names(fc) <- top$entrez[top$adj.P.Val <= .01]

    pv <- top$P.Value[top$adj.P.Val <= .01]
    names(pv) <- top$entrez[top$adj.P.Val <= .01]

    # alternativly use all the genes for the analysis
    # NOT RUN:
    # fc <- top$logFC
    # names(fc) <- top$entrez

    # pv <- top$P.Value
    # names(pv) <- top$entrez

    # get the reference
    ref <- top$entrez

    # load the set of pathways
    kpg <- keggPathwayGraphs("hsa")

    # set the beta information (see the citated documents for meaning of beta)
    kpg <- setEdgeWeights(kpg)

    # inlcude the significance information in the analysis (see Voichita:2012
    # for more information)
    # set the alpha information based on the pv with one of the predefined methods
    kpg <- setNodeWeights(kpg, weights = alphaMLG(pv), defaultWeight = 1)

    # perform the pathway analysis
    # in order to obtain accurate results the number of boostraps, nboot, should
    # be increase to a number like 2000
    pDisRes <- pDis(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE)

    # obtain summary of results
    head(Summary(pDisRes))

[Package ROntoTools version 2.12.0 Index]