pDis {ROntoTools} | R Documentation |
Primary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genes
pDis(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL)
x |
named vector of log fold changes for the differentially expressed genes; |
graphs |
list of pathway graphs as objects of type |
ref |
the reference vector for all genes in the analysis; if the reference is not provided or it is identical to |
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 |
See details in the cited articles.
An object of class pDisRes-class
.
Calin Voichita, Sahar Ansari and Sorin Draghici
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.
Summary
,
keggPathwayGraphs
, setNodeWeights
, setEdgeWeights
# 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))