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Last data update: 2014.03.03 |
Primary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genesDescriptionPrimary dis-regulation: Pathway analysis approach based on the unexplained dis-regulation of genes UsagepDis(x, graphs, ref = NULL, nboot = 2000, verbose = TRUE, cluster = NULL, seed = NULL) Arguments
DetailsSee details in the cited articles. ValueAn object of class Author(s)Calin Voichita, Sahar Ansari and Sorin Draghici ReferencesVoichita 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
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))
Results
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> library(ROntoTools)
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: boot
Loading required package: KEGGREST
Loading required package: KEGGgraph
Attaching package: 'KEGGgraph'
The following object is masked from 'package:graphics':
plot
Loading required package: Rgraphviz
Loading required package: grid
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ROntoTools/pDis.Rd_%03d_medium.png", width=480, height=480)
> ### Name: pDis
> ### Title: Primary dis-regulation: Pathway analysis approach based on the
> ### unexplained dis-regulation of genes
> ### Aliases: pDis
>
> ### ** 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)
logFC P.Value adj.P.Val entrez
200946_x_at -1.0175141 5.833411e-13 4.172652e-09 hsa:2746
228697_at -3.6479368 7.985427e-13 4.172652e-09 hsa:135114
210254_at 3.2807123 3.086572e-12 9.677020e-09 hsa:932
234726_s_at -0.9792301 7.368175e-12 1.760593e-08 hsa:64418
215905_s_at -1.7733135 7.861797e-12 1.760593e-08 hsa:9410
235542_at -0.9447467 1.617944e-11 2.536288e-08 hsa:200424
>
> # 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")
Using cached pathway data. Database info:
pathway KEGG Pathway Database
path Release 73.0+/01-03, Jan 15
Kanehisa Laboratories
343,170 entries
Default parameters detected. Using pre-parsed data.
>
> # 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)
Performing pathway analysis...
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>
> # obtain summary of results
> head(Summary(pDisRes))
totalpDis totalpDisNorm ppDis pORA pComb
path:hsa05010 112.02331 2.490643 0.03960396 1.360242e-05 8.314496e-06
path:hsa05110 82.39166 4.293900 0.00990099 1.085083e-04 1.583985e-05
path:hsa04145 102.93799 6.762435 0.00990099 2.424942e-04 3.346817e-05
path:hsa04722 96.82865 3.217279 0.00990099 4.644830e-04 6.111726e-05
path:hsa03015 54.07253 3.166901 0.00990099 6.821488e-04 8.716229e-05
path:hsa05016 121.64470 3.893293 0.00990099 6.852905e-04 8.753255e-05
ppDis.fdr pORA.fdr pComb.fdr
path:hsa05010 0.04933714 0.001999556 0.001164229
path:hsa05110 0.01842336 0.007975357 0.001164229
path:hsa04145 0.01842336 0.011882215 0.001639940
path:hsa04722 0.01842336 0.016789618 0.002144548
path:hsa03015 0.01842336 0.016789618 0.002144548
path:hsa05016 0.01842336 0.016789618 0.002144548
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> dev.off()
null device
1
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