ssea2kda.analyze: Apply second MSEA after merging the modules

In Mergeomics: Integrative network analysis of omics data

Description

ssea2kda.analyze performs a second MSEA for the updated modules after merging the highly overlapped modules (according to a specified overlapping ratio)

Usage

ssea2kda.analyze(job, moddata)

Arguments

job	the data list including the information of modules, genes, and markers, and also involving the database that uses indexed identities for modules, genes, and markers (job$database).
moddata	merged modules including MODULE, GENE, and OVERLAP information

Details

ssea2kda.analyze constructs new gene lists for merged modules and updates module database including module sizes, lengths, densities (based on marker sizes), and gene list. Then, it runs a second MSEA and returns the enrichment scores of the updated module database.

Value

res	data list including updated information (after merge) such as, enrichment scores of merged modules

Author(s)

Ville-Petteri Makinen

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.

See Also

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() 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)

###############  Create intermediary datasets for KDA ##################
syms <- tool.read(system.file("extdata", "symbols.txt", 
package="Mergeomics"))
syms <- syms[,c("HUMAN", "MOUSE")]
names(syms) <- c("FROM", "TO")
## Collect genes and top markers from original files.
noddata <- ssea2kda.import(job.msea$genfile, job.msea$locfile)

## Select candidate modules (significant ones according to FDRs)
res <- job.msea$results
res <- res[order(res$P),]
rows <- which(res$FDR < 0.25)
res <- res[rows,]
    
## Collect member genes.
moddata <- job.msea$moddata
pos <- match(moddata$MODULE, res$MODULE)
moddata <- moddata[which(pos > 0),]

## Restore original identities.
modinfo <- job.msea$modinfo
modinfo$MODULE <- job.msea$modules[modinfo$MODULE]
moddata$MODULE <- job.msea$modules[moddata$MODULE]
moddata$GENE <- job.msea$genes[moddata$GENE]

## Merge and trim overlapping modules.
moddata$OVERLAP <- moddata$MODULE
rmax <- 0.33
moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
rcutoff=rmax)
moddata$MODULE <- moddata$CLUSTER
moddata$GENE <- moddata$ITEM
moddata$OVERLAP <- moddata$GROUPS
moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
moddata <- unique(moddata)
    
## Calculate enrichment scores for merged modules.
tmp <- unique(moddata[,c("MODULE","OVERLAP")])
res <- ssea2kda.analyze(job.msea, moddata)

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

Mergeomics documentation built on Nov. 8, 2020, 6:58 p.m.