ssea.analyze.simulate: Simulate scores for MSEA
In zeynebkurtUCLA/Mergeomics: Integrative network analysis of omics data

Description Usage Arguments Value Author(s) References See Also Examples

ssea.analyze.simulate simulates enrichment scores by randomly permuting database with respect to the specified permutation type (either gene-level or marker-level).

1	ssea.analyze.simulate(db, observ, nperm, permtype, trim_start, trim_end)

`db`	database including the indexed identities for modules, genes and markers (e.g. loci): modulesizes: gene counts for modules. modulelengths: distinct marker counts for modules. moduledensities: ratio between distinct and non-distinct markers. genesizes: marker count for each gene. module2genes: gene lists for each module. gene2loci: marker lists for each gene. locus2row: row indices in the marker data frame for each marker. observed: matrix of observed counts of values that exceed each quantile point for each marker. expected: 1.0 - quantile points.
`observ`	observed enrichment scores
`nperm`	maximum nubmer of permutations (for simulation)
`permtype`	permutation type (either gene or locus)
`trim_start`	percentile taken from the beginning for trimming away a defined proportion of genes with significant trait association to avoid signal inflation of null background in gene permutation. Default value is 0.002.
`trim_end`	percentile taken from the ending point for trimming away a defined proportion of genes with significant trait association to avoid signal inflation of null background in gene permutation. Default value is 0.998.

scoresets

simulated score lists for the statistically significant modules

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.

ssea.analyze

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)

## Observed enrichment scores.
db <- job.msea$database
scores <- ssea.analyze.observe(db)
nmods <- length(scores)

## Simulated scores.
nperm <- job.msea$nperm
trim_start=0.002 # default
trim_end=1-trim_start
nullsets <- ssea.analyze.simulate(db, scores, nperm, job.msea$permtype,
trim_start, trim_end)

## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")