| ssea2kda {Mergeomics} | R Documentation |
ssea2kda forwards MSEA results to weighted key driver analysis (wKDA)
from the first MSEA results, merges the overlapped modules according to
a given overlapping ratio to obtain a relatively independent module set,
apply a second MSEA on the merged modules (supersets), updates and saves
the second MSEA results properly for wKDA process.
ssea2kda(job, symbols = NULL, rmax = NULL, min.module.count=NULL)
job |
data list including the organized results of MSEA process. It has following components: results: updated information of modules including: number of distinct member genes (NGENES), number of distinct member markers (NLOCI), ratio of distinct to non-distinct markers (DENSITY), false discovery rates (FDR). generesults: updated gene-specific information including: indexed gene identity (GENE), gene size (NLOCI), unadjusted enrichment score (SCORE), marker with max value (LOCUS), marker value (VALUE). |
symbols |
dataframe for translating gene symbols |
rmax |
maximum allowed overlap ratio between gene sets |
min.module.count |
minimum number of the pathways to be taken from the MSEA results to merge.
Default value is NULL. If it is not specified, all the pathways having MSEA-FDR
value less than 0.25 will be considered for merging if they are overlapping
with the given ratio |
ssea2kda gets genes and top markers from input files, selects
significant modules with respect to ordered p-values, gets identities of
modules and genes, merges and trims the overlapping modules (either having FDR
less than 0.25 or top min.module.count modules when ranked up to the
P-values), obtains enrichment scores for merged modules, translates the gene
symbols (between species) if needed, and finally saves the module, gene, node,
and marker information into relevant output files.
plan |
an updated data list with the following components: label: unique identifier for the analysis. parent: parent folder for results. modfile: path of module file (columns: MODULE NODE). inffile: path of module information file (columns: MODULE DESCR). nodfile: path of node selection file (columns: NODE). |
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.
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")
job.ssea2kda <- ssea2kda(job.msea, symbols=syms)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")