ssea.analyze.randloci: Estimate enrichment from randomized marker
In zeynebkurtUCLA/Mergeomics: Integrative network analysis of omics data

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

ssea.analyze.randloci simulates enrichment scores by randomizing the marker that mapped to genes from all modules (from database, db)

1	ssea.analyze.randloci(db, targets)

db

database including the indexed identities for modules, genes and markers:

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.

targets

all modules

scores

randomly simulated enrichment scores

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$nperm
observ <- scores
## Include only non-empty modules for simulation.
nmods <- length(db$modulesizes)
targets <- which(db$modulesizes > 0)
hits <- rep(NA, nmods)
hits[targets] <- 0
    
## Prepare data structures to hold null samples.
keys <- rep(0, nperm)
scores <- rep(NA, nperm)
scoresets <- list()
for(i in 1:nmods) scoresets[[i]] <- double()
## Simulate random scores.
## within a for loop: check capacity, find new statistics, update snull  
## distribution (simulated null distr.) by permuting loci
snull <- ssea.analyze.randloci(db, targets)

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