Mergeomics (version 1.0.0)

ssea.start: Create a job for MSEA

Description

Creates identities (for modules, member genes, and loci) to start MSEA process.

Usage

ssea.start(plan)

Arguments

plan

a data list with the following components:

label: unique identifier for the analysis
folder: output folder for results
modfile: path to module file (cols: MODULE GENE)
locfile: path to marker file (cols: LOCUS VALUE)
genfile: path to gene file (cols: GENE LOCUS)
inffile: path to module info file (cols: MODULE DESCR)
seed: seed for random number generator
permtype: gene for gene-level, locus for marker-level
nperm: max number of random permutations
mingenes: min number of genes per module (after merging)
maxgenes: max number of genes per module
quantiles: cutoffs for test statistic
maxoverlap: max overlap allowed between genes

Value

job

a data list with the following components:

modules: module identities as characters.
genes: gene identities as characters.
loci: marker identities as characters.
moddata: preprocessed module data (indexed identities)
modinfo: description of the modules. 
gendata: preprocessed mapping data between genes and 
markers (indexed identities).
locdata: preprocessed marker data (indexed identities)
geneclusters: genes with shared markers.

Details

ssea.start imports modules, genes-locus mapping, and locus values; removes the genes with no locus values from the list, find identities for modules, genes, loci components, and excludes missing data and factorize identities for these components.

References

Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems. bioRxiv doi: http://dx.doi.org/10.1101/036012

See Also

ssea.analyze, ssea.control, ssea.finish, ssea.prepare, ssea2kda

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() for this small set:(due to the huge runtime we did not use
## full sets of modules, genes, and markers)
job.msea <- ssea.start(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")