## ----setup, include=F---------------------------------------------------- knitr::opts_chunk$set(cache=TRUE) ## ------------------------------------------------------------------------ library(workshopBioc2016) data(meth_set) data(gm_set) data(gratio_set) meth_set gm_set gratio_set ## ----eval=F-------------------------------------------------------------- # library(epivizr) # app <- startEpiviz(workspace="kr0MpYk4g0u",try_ports=TRUE) ## ----eval=F-------------------------------------------------------------- # library(GenomicFeatures) # library(TxDb.Hsapiens.UCSC.hg19.knownGene) # promoter_regions <- promoters(TxDb.Hsapiens.UCSC.hg19.knownGene,upstream=1000, # downstream=200) # promoters_track <- app$plot(promoter_regions,datasource_name="Promoters") ## ----eval=F-------------------------------------------------------------- # library(doParallel) # cores <- detectCores() # registerDoParallel(cores) # meth_set_breast <- meth_set[,which(meth_set$Tissue=="breast")] # dmp <- dmpFinder(meth_set_breast,meth_set_breast$Status,type="categorical") # head(dmp) ## ---- eval=FALSE--------------------------------------------------------- # dmp_gr <- granges(gm_set[rownames(dmp),]) # dmps_track <- app$plot(dmp_gr,datasource_name="Differentially Methylated Positions") ## ----eval=F,results="hide"----------------------------------------------- # betas <- getBeta(gratio_set) # pd <- pData(gratio_set) # fac <- paste(pd$Tissue, pd$Status, sep=":") # sample_indices <- split(seq(len=nrow(pd)), fac) # # mean_betas <- sapply(sample_indices, function(ind) rowMeans(betas[,ind])) ## ---- eval=FALSE--------------------------------------------------------- # cpg_gr <- granges(gm_set) # mcols(cpg_gr) <- mean_betas # beta_track <- app$plot(cpg_gr,datasource_name="Percent Methylation",type="bp", settings=list(step=1, interpolation="basis")) ## ---- eval=FALSE--------------------------------------------------------- # dmp_gr$pval <- -log10(dmp$pval) ## ----eval=F-------------------------------------------------------------- # # first declare the GRanges object as a data source # gr_obj <- app$data_mgr$add_measurements(dmp_gr, "pvalues", type="bp", columns="pval") # # # then add the track # pvals_track <- app$chart_mgr$visualize("StackedLineTrack", datasource=gr_obj) ## ----eval=F-------------------------------------------------------------- # app$navigate("chr11",119870000,120100000) ## ---- eval=FALSE--------------------------------------------------------- # app$stop_app() ## ----eval=F-------------------------------------------------------------- # app <- startEpiviz(workspace="kr0MpYk4g0u",try_ports=TRUE) ## ----eval=F-------------------------------------------------------------- # promoter_track <- app$plot(promoter_regions, datasource_name="Promoters") # beta_track <- app$plot(cpg_gr, datasource_name="Percent Methylation", type="bp", settings=list(step=1, interpolation="basis")) ## ----eval=F,results="hide",message=F------------------------------------- # # first subset to breast samples # gratio_set_breast <- gratio_set[,which(gratio_set$Tissue=="breast")] # # # make a design matrix to use with bumphunter # status <- pData(gratio_set_breast)$Status # mod <- model.matrix(~status) # # # cluster cpgs into regions holding potential dmrs # gr <- granges(gratio_set_breast) # chr <- as.factor(seqnames(gr)) # pos <- start(gr) # cl <- clusterMaker(chr, pos, maxGap=500) # # # find dmrs # bumps <- bumphunter(gratio_set_breast, mod, cluster=cl, cutoff=0.1, B=0) ## ----eval=F-------------------------------------------------------------- # # categorize dmrs by type # dmr_gr <- with(bumps$table, GRanges(chr, IRanges(start,end), area=area, value=value)) # dmr_gr$type <- ifelse(abs(dmr_gr$value) < 0.1, "neither", # ifelse(dmr_gr$value<0, "hypo", "hyper")) # table(dmr_gr$type) # # # make a GRanges object for each dmr type # hyper_gr <- dmr_gr[dmr_gr$type == "hyper"] # hypo_gr <- dmr_gr[dmr_gr$type == "hypo"] # # # add each of these as a datasource on epiviz # hypo_ds <- app$data_mgr$add_measurements(hypo_gr, "Hypo DMRs") # hyper_ds <- app$data_mgr$add_measurements(hyper_gr, "Hyper DMRs") # # # add the track # measurements <- c(hypo_ds$get_measurements(), hyper_ds$get_measurements()) # dmr_track <- app$chart_mgr$visualize("BlocksTrack", measurements = measurements) ## ----eval=F-------------------------------------------------------------- # ranges <- IRanges(start=c(44282278, 61038553, 131265454), # end=c(44331716, 61051026, 131565783)) # ranges <- ranges * 0.5 # slideshow_regions <- GRanges(seqnames=c("chr11", "chr20", "chr10"), ranges=ranges) # app$slideshow(slideshow_regions) ## ----eval=F-------------------------------------------------------------- # hyper_promoters <- subsetByOverlaps(hyper_gr, promoter_regions) # o <- order(-hyper_promoters$area)[1:5] # top_promoters <- hyper_promoters[o,] # top_promoters <- top_promoters + 10000 # app$slideshow(top_promoters) ## ---- eval=FALSE--------------------------------------------------------- # app$stop_app() ## ----eval=F-------------------------------------------------------------- # app <- startEpiviz(workspace="kr0MpYk4g0u",try_ports=TRUE) ## ----eval=F-------------------------------------------------------------- # promoter_track <- app$plot(promoter_regions, datasource_name="Promoters") # beta_track <- app$plot(cpg_gr, datasource_name="Percent Methylation", type="bp", settings=list(step=50, interpolation="basis")) ## ----eval=F-------------------------------------------------------------- # cpg_gr$breast_diff <- cpg_gr$`breast:cancer` - cpg_gr$`breast:normal` # diff_track <- app$plot(cpg_gr, datasource_name="Methylation Beta Difference", type="bp", # columns="breast_diff", settings=list(step=50, interpolation="basis")) ## ----eval=F,warning=F,results="hide",message=F--------------------------- # cl <- cpgCollapse(gm_set_breast) # blocks <- blockFinder(cl$object, mod, cluster=cl$blockInfo$pns, cutoff = 0.1) ## ----eval=F-------------------------------------------------------------- # blocks_gr <- with(blocks$table, GRanges(chr, IRanges(start,end), area=area, value=value)) # blocks_gr$type <- ifelse(abs(blocks_gr$value)<0.1, "neither", # ifelse(blocks_gr$value<0, "hypo", "hyper")) # table(blocks_gr$type) # # hypo_blocks <- blocks_gr[blocks_gr$type == "hypo",] # hypo_track <- app$plot(hypo_blocks, datasource_name="Breast Hypo Blocks") ## ---- eval=FALSE--------------------------------------------------------- # o <- order(-width(hypo_blocks))[1:5] # slideshow_regions <- hypo_blocks[o,] * .5 # app$slideshow(slideshow_regions) ## ----eval=F-------------------------------------------------------------- # gene_regions <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene,columns="gene_id", # single.strand.genes.only=TRUE) # hypo_gene_blocks <- subsetByOverlaps(hypo_blocks, gene.regions) # o <- order(-hypo_gene_blocks$area)[1:5] # slideshow_regions <- hypo_gene_blocks[o,] * .5 # app$slideshow(slideshow_regions)