spia: Signaling Pathway Impact Analysis (SPIA) based on...
In SPIA: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function implements the SPIA algorithm to analyze KEGG signaling pathways.
Usage
1
Arguments
de
A named vector containing log2 fold-changes of the differentially expressed genes. The names of this numeric vector
are Entrez gene IDs.
all
A vector with the Entrez IDs in the reference set. If the data was obtained from a microarray experiment,
this set will contain all genes present on the specific array used for the experiment. This vector should
contain all names of the de argument.
organism
A three letter character designating the organism. See a full list at ftp://ftp.genome.jp/pub/kegg/xml/organisms.
data.dir
Location of the "organism"SPIA.RData file containing the pathways data generated with makeSPIAdata.
If set to NULL will look for this file in the extdata folder of the SPIA library.
pathids
A character vector with the names of the pathways to be analyzed. If left NULL all pathways available will be tested.
nB
Number of bootstrap iterations used to compute the P PERT value. Should be larger
than 100. A recommended value is 2000.
plots
If set to TRUE, the function plots the gene perturbation accumulation vs log2 fold
change for every gene on each pathway. The null distribution of the total net accumulations from which PPERT is computed, is plotted as well.
The figures are sent to the SPIAPerturbationPlots.pdf file in the current directory.
verbose
If set to TRUE, displays the number of pathways already analyzed.
beta
Weights to be assigned to each type of gene/protein relation type. It should be a named numeric vector of length 23, whose names must be:
c("activation","compound","binding/association","expression","inhibition","activation_phosphorylation","phosphorylation",
"indirect","inhibition_phosphorylation","dephosphorylation_inhibition","dissociation","dephosphorylation","activation_dephosphorylation",
"state","activation_indirect","inhibition_ubiquination","ubiquination","expression_indirect","indirect_inhibition","repression",
"binding/association_phosphorylation","dissociation_phosphorylation","indirect_phosphorylation")
If set to null, beta will be by default chosen as: c(1,0,0,1,-1,1,0,0,-1,-1,0,0,1,0,1,-1,0,1,-1,-1,0,0,0).
combine
Method used to combine the two types of p-values. If set to "fisher" it will use Fisher's method. If set to "norminv" it will use the normal inversion method.
Details
See cited documents for more details.
Value
A data frame containing the ranked pathways and various statistics: pSize is the number of genes on the pathway;
NDE is the number of DE genes per pathway; tA is the observed total preturbation
accumulation in the pathway; pNDE is the probability to observe at least NDE genes on
the pathway using a hypergeometric model;
pPERT is the probability to observe a total accumulation more extreme than tA only by
chance;
pG is the p-value obtained by combining pNDE and pPERT;
pGFdr and pGFWER are the
False Discovery Rate and respectively Bonferroni adjusted global p-values; and the Status gives the direction
in which the pathway is perturbed (activated or inhibited).
KEGGLINK gives a web link to the KEGG website that displays the pathway image with the differentially expressed genes
highlighted in red.
Author(s)
Adi Laurentiu Tarca <atarca@med.wayne.edu>, Purvesh Khatri, Sorin Draghici
References
Adi L. Tarca, Sorin Draghici, Purvesh Khatri, et. al, A Signaling Pathway Impact Analysis for
Microarray Experiments, 2008, Bioinformatics, 2009, 25(1):75-82.
Purvesh Khatri, Sorin Draghici, Adi L. Tarca, Sonia S. Hassan, Roberto Romero. 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.
See Also
Examples
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# Example using a colorectal cancer dataset obtained using Affymetrix geneChip technology (GEE GSE4107).
# Suppose that proper preprocessing was performed and a two group moderated t-test was applied. The topTable
# result from limma package for this data set is called "top".
#The following lines will annotate each probeset to an entrez ID identifier, will keep the most significant probeset for each
#gene ID and retain those with FDR<0.05 as differentially expressed.
#You can run these lines if hgu133plus2.db package is available
#data(colorectalcancer)
#x <- hgu133plus2ENTREZID
#top$ENTREZ<-unlist(as.list(x[top$ID]))
#top<-top[!is.na(top$ENTREZ),]
#top<-top[!duplicated(top$ENTREZ),]
#tg1<-top[top$adj.P.Val<0.1,]
#DE_Colorectal=tg1$logFC
#names(DE_Colorectal)<-as.vector(tg1$ENTREZ)
#ALL_Colorectal=top$ENTREZ
data(colorectalcancer)
# pathway analysis using SPIA; # use nB=2000 or higher for more accurate results
#uses older version of KEGG signalimng pathways graphs
res<-spia(de=DE_Colorectal, all=ALL_Colorectal, organism="hsa",beta=NULL,nB=2000,plots=FALSE, verbose=TRUE,combine="fisher")
res
# Create the evidence plot
plotP(res)
#now combine pNDE and pPERT using the normal inversion method without running spia function again
res$pG=combfunc(res$pNDE,res$pPERT,combine="norminv")
res$pGFdr=p.adjust(res$pG,"fdr")
res$pGFWER=p.adjust(res$pG,"bonferroni")
plotP(res,threshold=0.05)
#highlight the colorectal cancer pathway in green
points(I(-log(pPERT))~I(-log(pNDE)),data=res[res$ID=="05210",],col="green",pch=19,cex=1.5)
#run SPIA using pathways data generated from (up-to-date) xml files that you can obtain from
#KEGG ftp or by downloading them from each pathway's web page:
# e.g. go to http://www.genome.jp/kegg/pathway/hsa/hsa04010.html and click on DOwnload KGML
#to get the xml file for pathway 4010
makeSPIAdata(kgml.path=system.file("extdata/keggxml/hsa",package="SPIA"),organism="hsa",out.path="./")
res<-spia(de=DE_Colorectal, all=ALL_Colorectal, organism="hsa",data.dir="./")
res
SPIA documentation built on Nov. 8, 2020, 5:44 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function implements the SPIA algorithm to analyze KEGG signaling pathways.
Usage
1 |
Arguments
de |
A named vector containing log2 fold-changes of the differentially expressed genes. The names of this numeric vector are Entrez gene IDs. |
all |
A vector with the Entrez IDs in the reference set. If the data was obtained from a microarray experiment,
this set will contain all genes present on the specific array used for the experiment. This vector should
contain all names of the |
organism |
A three letter character designating the organism. See a full list at ftp://ftp.genome.jp/pub/kegg/xml/organisms. |
data.dir |
Location of the "organism"SPIA.RData file containing the pathways data generated with makeSPIAdata. If set to NULL will look for this file in the extdata folder of the SPIA library. |
pathids |
A character vector with the names of the pathways to be analyzed. If left NULL all pathways available will be tested. |
nB |
Number of bootstrap iterations used to compute the P PERT value. Should be larger than 100. A recommended value is 2000. |
plots |
If set to TRUE, the function plots the gene perturbation accumulation vs log2 fold change for every gene on each pathway. The null distribution of the total net accumulations from which PPERT is computed, is plotted as well. The figures are sent to the SPIAPerturbationPlots.pdf file in the current directory. |
verbose |
If set to TRUE, displays the number of pathways already analyzed. |
beta |
Weights to be assigned to each type of gene/protein relation type. It should be a named numeric vector of length 23, whose names must be:
If set to null, beta will be by default chosen as: c(1,0,0,1,-1,1,0,0,-1,-1,0,0,1,0,1,-1,0,1,-1,-1,0,0,0). |
combine |
Method used to combine the two types of p-values. If set to |
Details
See cited documents for more details.
Value
A data frame containing the ranked pathways and various statistics: pSize is the number of genes on the pathway;
NDE is the number of DE genes per pathway; tA is the observed total preturbation
accumulation in the pathway; pNDE is the probability to observe at least NDE genes on
the pathway using a hypergeometric model;
pPERT is the probability to observe a total accumulation more extreme than tA only by
chance;
pG is the p-value obtained by combining pNDE and pPERT;
pGFdr and pGFWER are the
False Discovery Rate and respectively Bonferroni adjusted global p-values; and the Status gives the direction
in which the pathway is perturbed (activated or inhibited).
KEGGLINK gives a web link to the KEGG website that displays the pathway image with the differentially expressed genes
highlighted in red.
Author(s)
Adi Laurentiu Tarca <atarca@med.wayne.edu>, Purvesh Khatri, Sorin Draghici
References
Adi L. Tarca, Sorin Draghici, Purvesh Khatri, et. al, A Signaling Pathway Impact Analysis for
Microarray Experiments, 2008, Bioinformatics, 2009, 25(1):75-82.
Purvesh Khatri, Sorin Draghici, Adi L. Tarca, Sonia S. Hassan, Roberto Romero. 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.
See Also
Examples
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 | # Example using a colorectal cancer dataset obtained using Affymetrix geneChip technology (GEE GSE4107).
# Suppose that proper preprocessing was performed and a two group moderated t-test was applied. The topTable
# result from limma package for this data set is called "top".
#The following lines will annotate each probeset to an entrez ID identifier, will keep the most significant probeset for each
#gene ID and retain those with FDR<0.05 as differentially expressed.
#You can run these lines if hgu133plus2.db package is available
#data(colorectalcancer)
#x <- hgu133plus2ENTREZID
#top$ENTREZ<-unlist(as.list(x[top$ID]))
#top<-top[!is.na(top$ENTREZ),]
#top<-top[!duplicated(top$ENTREZ),]
#tg1<-top[top$adj.P.Val<0.1,]
#DE_Colorectal=tg1$logFC
#names(DE_Colorectal)<-as.vector(tg1$ENTREZ)
#ALL_Colorectal=top$ENTREZ
data(colorectalcancer)
# pathway analysis using SPIA; # use nB=2000 or higher for more accurate results
#uses older version of KEGG signalimng pathways graphs
res<-spia(de=DE_Colorectal, all=ALL_Colorectal, organism="hsa",beta=NULL,nB=2000,plots=FALSE, verbose=TRUE,combine="fisher")
res
# Create the evidence plot
plotP(res)
#now combine pNDE and pPERT using the normal inversion method without running spia function again
res$pG=combfunc(res$pNDE,res$pPERT,combine="norminv")
res$pGFdr=p.adjust(res$pG,"fdr")
res$pGFWER=p.adjust(res$pG,"bonferroni")
plotP(res,threshold=0.05)
#highlight the colorectal cancer pathway in green
points(I(-log(pPERT))~I(-log(pNDE)),data=res[res$ID=="05210",],col="green",pch=19,cex=1.5)
#run SPIA using pathways data generated from (up-to-date) xml files that you can obtain from
#KEGG ftp or by downloading them from each pathway's web page:
# e.g. go to http://www.genome.jp/kegg/pathway/hsa/hsa04010.html and click on DOwnload KGML
#to get the xml file for pathway 4010
makeSPIAdata(kgml.path=system.file("extdata/keggxml/hsa",package="SPIA"),organism="hsa",out.path="./")
res<-spia(de=DE_Colorectal, all=ALL_Colorectal, organism="hsa",data.dir="./")
res
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