## ----------------------------------------------------------------------------- rm(list=ls()) ## ----------------------------------------------------------------------------- library("DEComplexDisease") #load RNA-seq counts matrix data(exp) #load sample annotation vector data(cl) #load sample ER status annotation data(ann.er) exp[1:5,1:5] head(cl, 4) ## ----------------------------------------------------------------------------- deg=bi.deg(exp, cl, method="edger", cutoff=0.05, cores=4) ## ----------------------------------------------------------------------------- Plot(deg, ann=ann.er, show.genes=row.names(deg)[1:5]) ## ----------------------------------------------------------------------------- res.deg=deg.specific(deg, min.genes=30, min.patients=5, cores=4) ## ----------------------------------------------------------------------------- res.deg.test=deg.specific(deg, test.patients=colnames(deg)[1:10], min.genes=50, min.patients=8, cores=4) ## ----------------------------------------------------------------------------- Plot(res.deg, ann=ann.er, max.n=5 ) ## ----------------------------------------------------------------------------- Plot(res.deg.test, ann=ann.er, max.n=5) ## ----------------------------------------------------------------------------- seed.mod1 = seed.module(deg, res.deg=res.deg, min.genes=50, min.patients=20, overlap=0.85, cores=4) ## ----------------------------------------------------------------------------- seed.mod2 = seed.module(deg, test.patients=colnames(deg)[1:10], min.genes=50, min.patients=20, overlap=0.85, cores=4) ## ----------------------------------------------------------------------------- Plot(seed.mod1, ann=ann.er, type="model", max.n=5) ## ----------------------------------------------------------------------------- cluster.mod1 <- cluster.module(seed.mod1, cores=4) ## ----------------------------------------------------------------------------- cluster.mod2 <- cluster.module(seed.mod1, vote.seed=TRUE, cores=4) ## ----------------------------------------------------------------------------- sort(names(cluster.mod1), decreasing=TRUE) names(cluster.mod1[["decd.input"]]) names(cluster.mod1[["decd.clustering"]]) names(cluster.mod1[["M1"]]) ## ----------------------------------------------------------------------------- Plot(cluster.mod1, ann=ann.er, type="model", max.n=5) ## ----------------------------------------------------------------------------- module.overlap(cluster.mod1, max.n=5) ## ----------------------------------------------------------------------------- res.mod1 <- seed.module(deg[,1:26], min.genes=50, min.patients=10, overlap=0.85, cores=4) res.mod1 <- cluster.module(res.mod1) res.mod2 <- seed.module(deg[,27:52], min.genes=50, min.patients=10, overlap=0.85, cores=4) res.mod2 <- cluster.module(res.mod2) ## ----------------------------------------------------------------------------- module.compare(res.mod1, res.mod2, max.n1=5, max.n2=5) ## ----------------------------------------------------------------------------- names(cluster.mod1[["M1"]][["curve"]]) head(cluster.mod1[["M1"]][["curve"]][["no.gene"]]) head(cluster.mod1[["M1"]][["curve"]][["no.patient"]]) ## ----------------------------------------------------------------------------- module.curve(cluster.mod1, "M1") ## ----------------------------------------------------------------------------- x=c(50,40) names(x)<-c("M1","M3") new.cluster.mod1=module.modeling(cluster.mod1, keep.gene.num = x, model.method='slope.clustering', cores=4) #here, only "M1" and "M3" are modified new.cluster.mod1=module.modeling(cluster.mod1, keep.gene.num = 50) # here, all the modules are modified module.curve(new.cluster.mod1, "M1") ## ----------------------------------------------------------------------------- module.screen(cluster.mod1, feature.patients=sample(colnames(deg),10)) #search modules module.screen(seed.mod1, feature.patients=sample(colnames(deg),10), method="fisher.test")