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\name{ssea.finish.genes}
\alias{ssea.finish.genes}
\title{
Organize and save gene-realted MSEA results
}
\description{
\code{ssea.finish.genes} organizes and stores the gene-related MSEA
results into relevant output file. It finds the top markers within genes,
update gene scores and gene sizes, and save the results.
}
\usage{
ssea.finish.genes(job)
}
\arguments{
\item{job}{
data list including the information about the MSEA process:
\preformatted{
folder: output folder for results.
database: database including indexed identities for
modules, genes, and markers.
}
}
}
\value{
\item{job }{data list including the organized gene-related results of
MSEA process: \preformatted{
generesults: updated gene-specific information;
indexed gene identity (GENE),
gene size (NLOCI),
unadjusted enrichment score (SCORE),
marker with maximum value (LOCUS),
marker value (VALUE).
}
}
}
\examples{
job.msea <- list()
job.msea$label <- "hdlc"
job.msea$folder <- "Results"
job.msea$genfile <- system.file("extdata",
"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$marfile <- system.file("extdata",
"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$modfile <- system.file("extdata",
"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
job.msea$inffile <- system.file("extdata",
"coexpr.info.txt", package="Mergeomics")
job.msea$nperm <- 100 ## default value is 20000
## ssea.start() process takes long time while merging the genes sharing high
## amounts of markers (e.g. loci). it is performed with full module list in
## the vignettes. Here, we used a very subset of the module list (1st 10 mods
## from the original module file) and we collected the corresponding genes
## and markers belonging to these modules:
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
unique(moddata$GENE)))),]
mardata <- mardata[which(!is.na(match(mardata$MARKER,
unique(gendata$MARKER)))),]
## save this to a temporary file and set its path as new job.msea$modfile:
tool.save(moddata, "subsetof.coexpr.modules.txt")
tool.save(gendata, "subsetof.genfile.txt")
tool.save(mardata, "subsetof.marfile.txt")
job.msea$modfile <- "subsetof.coexpr.modules.txt"
job.msea$genfile <- "subsetof.genfile.txt"
job.msea$marfile <- "subsetof.marfile.txt"
## run ssea.start() and prepare for this small set: (due to the huge runtime)
job.msea <- ssea.start(job.msea)
job.msea <- ssea.prepare(job.msea)
job.msea <- ssea.control(job.msea)
job.msea <- ssea.analyze(job.msea)
job.msea <- ssea.finish(job.msea)
## Estimate mod FDR values, sort according to significance, save full results:
job.msea <- ssea.finish.fdr(job.msea)
## Collect top markers(e.g.loci) within genes, save genes with top marker Pval
job.msea <- ssea.finish.genes(job.msea)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
}
\references{
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
Mergeomics: multidimensional data integration to identify pathogenic
perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\author{
Ville-Petteri Makinen
}
\seealso{
\code{\link{ssea.finish}}
}
|
