Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/pathwayExpress.R
Pathway-Express: Pathway analysis of signaling pathways
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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 peRes-class
.
Calin Voichita 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
Tarca AL., Draghici S., Khatri P., Hassan SS., Kim J., Kim CJ., Kusanovic JP., Romero R.: "A Signaling Pathway Impact Analysis for Microarray Experiments", 2008, Bioinformatics, 2009, 25(1):75-82.
Khatri P., Draghici S., Tarca AL., Hassan SS., Romero R.: "A system biology approach for the steady-state analysis of gene signaling networks". Progress in Pattern Recognition, Image Analysis and Applications, Lecture Notes in Computer Science. 4756:32-41, November 2007.
Draghici S., Khatri P., Tarca A.L., Amin K., Done A., Voichita C., Georgescu C., Romero R.: "A systems biology approach for pathway level analysis". Genome Research, 17, 2007.
Summary
, plot,peRes,missing-method
,
keggPathwayGraphs
, setNodeWeights
, setEdgeWeights
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # 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
peRes <- pe(fc, graphs = kpg, ref = ref, nboot = 100, verbose = TRUE)
# obtain summary of results
head(Summary(peRes))
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