Description Usage Arguments Value Author(s) References See Also Examples
ssea.start.configure
checks the input parameter before MSEA process
starts and assigns default values for non-exist fields of the input data
object.
1 | ssea.start.configure(plan)
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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) marfile: path to marker file (cols: MARKER VALUE) genfile: path to gene file (cols: GENE MARKER) inffile: path to module info file (cols: MODULE DESCR) seed: seed for random number generator permtype: gene for gene-level, marker 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 |
plan |
a data list including checked and assigned values (to non-existing fields) of the input parameter: label: unique identifier for the analysis folder: output folder for results modfile: path to module file (cols: MODULE GENE) marfile: path to marker file (cols: MARKER VALUE) genfile: path to gene file (cols: GENE MARKER) inffile: path to module info file (cols: MODULE DESCR) seed: seed for random number generator permtype: gene for gene-level, marker 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 |
Ville-Petteri Makinen
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.
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 | 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.configure(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")
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