### R code from vignette source 'gskb.Rnw' ################################################### ### code chunk number 1: style-Sweave ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: gskb.Rnw:26-27 ################################################### options(width=80) ################################################### ### code chunk number 3: gskb.Rnw:95-99 ################################################### library(gskb) data(mm_miRNA) mm_miRNA[[1]][1:10] ################################################### ### code chunk number 4: gskb.Rnw:119-121 (eval = FALSE) ################################################### ## source("http://bioconductor.org/biocLite.R") ## biocLite("PGSEA") ################################################### ### code chunk number 5: gskb.Rnw:123-147 ################################################### library(PGSEA) library(gskb) data(mm_miRNA) gse<-read.csv("http://ge-lab.org/gskb/GSE40261.csv",header=TRUE, row.name=1) # Gene are centered by mean expression gse <- gse - apply(gse,1,mean) pg <- PGSEA(gse, cl=mm_miRNA, range=c(15,2000), p.value=NA) # Remove pathways that has all NAs. This could be due to that pathway has # too few matching genes. pg2 <- pg[rowSums(is.na(pg))!= dim(gse)[2], ] # Difference in Average Z score in two groups of samples is calculated and # the pathways are ranked by absolute value. diff <- abs( apply(pg2[,1:4],1,mean) - apply(pg2[,5:8], 1, mean) ) pg2 <- pg2[order(-diff), ] sub <- factor( c( rep("Control",4),rep("Anti-miR-29",4) ) ) smcPlot(pg2[1:15,],sub,scale=c(-12,12),show.grid=TRUE,margins=c(1,1,7,19),col=.rwb) ################################################### ### code chunk number 6: gskb.Rnw:162-208 (eval = FALSE) ################################################### ## library(gskb) ## data(mm_miRNA) ## ## ## GSEA 1.0 -- Gene Set Enrichment Analysis / Broad Institute ## ## GSEA.prog.loc<- "http://ge-lab.org/gskb/GSEA.1.0.R" ## source(GSEA.prog.loc, max.deparse.length=9999) ## ## GSEA( ## # Input/Output Files :------------------------------------------------ ## ## # Input gene expression Affy dataset file in RES or GCT format ## input.ds = "http://ge-lab.org/gskb/mouse_data.gct", ## ## # Input class vector (phenotype) file in CLS format ## input.cls = "http://ge-lab.org/gskb/mouse.cls", ## ## # Gene set database in GMT format ## gs.db = mm_miRNA, ## ## # Directory where to store output and results (default: "") ## output.directory = getwd(), ## ## # Program parameters :----------------------------------------------- ## doc.string = "mouse", ## non.interactive.run = T, ## reshuffling.type = "sample.labels", ## nperm = 1000, ## weighted.score.type = 1, ## nom.p.val.threshold = -1, ## fwer.p.val.threshold = -1, ## fdr.q.val.threshold = 0.25, ## topgs = 10, ## adjust.FDR.q.val = F, ## gs.size.threshold.min = 15, ## gs.size.threshold.max = 500, ## reverse.sign = F, ## preproc.type = 0, ## random.seed = 3338, ## perm.type = 0, ## fraction = 1.0, ## replace = F, ## save.intermediate.results = F, ## OLD.GSEA = F, ## use.fast.enrichment.routine = T ## ) ################################################### ### code chunk number 7: gskb.Rnw:225-247 ################################################### library(PGSEA) library(gskb) d1 <- scan("http://ge-lab.org/gskb/2-MousePath/MousePath_Co-expression_gmt.gmt", what="", sep="\n", skip=1) mm_Co_expression <- strsplit(d1, "\t") names(mm_Co_expression) <- sapply(mm_Co_expression, '[[', 1) pg <- PGSEA(gse, cl=mm_Co_expression, range=c(15,2000), p.value=NA) # Remove pathways that has all NAs. This could be due to that pathway has # too few matching genes. pg2 <- pg[rowSums(is.na(pg))!= dim(gse)[2], ] # Difference in Average Z score in two groups of samples is calculated and # the pathways are ranked by absolute value. diff <- abs( apply(pg2[,1:4],1,mean) - apply(pg2[,5:8], 1, mean) ) pg2 <- pg2[order(-diff), ] sub <- factor( c( rep("Control",4),rep("Anti-miR-29",4) ) ) smcPlot(pg2[1:15,],sub,scale=c(-12,12),show.grid=TRUE,margins=c(1,1,7,19),col=.rwb) ################################################### ### code chunk number 8: gskb.Rnw:257-258 ################################################### sessionInfo()