## ---- message=FALSE------------------------------------------------------ library(crossmeta) # specify where data will be downloaded data_dir <- file.path(getwd(), "data", "LY") # gather all GSEs gse_names <- c("GSE9601", "GSE15069", "GSE50841", "GSE34817", "GSE29689") # gather Illumina GSEs (see 'Checking Raw Illumina Data') illum_names <- c("GSE50841", "GSE34817", "GSE29689") # download raw data # get_raw(gse_names, data_dir) ## ---- eval=FALSE--------------------------------------------------------- # # this is why we gathered Illumina GSEs # open_raw_illum(illum_names, data_dir) ## ---- message=FALSE, warning=FALSE--------------------------------------- library(lydata) # location of raw data data_dir <- system.file("extdata", package = "lydata") ## ---- message=FALSE, warning=FALSE, results='hide'----------------------- # reloads if previously called esets <- load_raw(gse_names, data_dir) ## ---- eval=FALSE--------------------------------------------------------- # library(Biobase) # library(AnnotationDbi) # # # check feature data to see what columns are available # head(fData(esets$GSE15069)) # # # if using RStudio # # View(fData(esets$GSE15069)) # # # annotation package for appropriate species # library(org.Mm.eg.db) # # # map from accession number to entrez gene ids # acnums <- as.character(fData(esets$GSE15069)$GB_ACC) # enids <- mapIds(org.Mm.eg.db, acnums, "ENTREZID", "ACCNUM") # # # add 'GENE_ID' column with entrez ids # fData(esets$GSE15069)$GENE_ID <- enids # # # use crossmeta to map from entrez gene ids to homologous hgnc symbol # esets$GSE15069 <- symbol_annot(esets$GSE15069) # # # to overwrite saved eset (to avoid repeating above) # saveRDS(esets$GSE15069, file.path(data_dir, "GSE15069", "GSE15069_eset.rds")) ## ---- eval=FALSE--------------------------------------------------------- # anals <- diff_expr(esets, data_dir) ## ------------------------------------------------------------------------ # load auto-saved results of previous call to diff_expr prev <- load_diff(gse_names, data_dir) # supply prev to diff_expr # anals <- diff_expr(esets, data_dir, prev_anals=prev) ## ---- message=FALSE, warning=FALSE, results='hide', fig.keep='none'------ library(Biobase) # load eset gse_name <- c("GSE34817") eset <- load_raw(gse_name, data_dir) # inspect pData of eset # View(pData(eset$GSE34817)) # if using RStudio head(pData(eset$GSE34817)) # otherwise # get group info from pData (differs based on eset) group <- pData(eset$GSE34817)$characteristics_ch1.1 # make group names concise and valid group <- gsub("treatment: ", "", group) group <- make.names(group) # add group to eset pData pData(eset$GSE34817)$group <- group # setup selections sel <- setup_prev(eset, contrasts = "LY-DMSO") # run differential expression analysis # anal <- diff_expr(eset, data_dir, prev_anal = sel) ## ---- message=FALSE, results='hide'-------------------------------------- # run GUI to add tissue sources # anals <- add_sources(prev, data_dir) # for usage details ?add_sources ## ---- message=FALSE, results='hide'-------------------------------------- # re-load previous analyses if need to anals <- load_diff(gse_names, data_dir) # perform meta analyses by tissue source es_res <- es_meta(anals, by_source = TRUE) # for explanation of values ?es_meta ## ------------------------------------------------------------------------ # re-load previous differential expression analyses anals <- load_diff(gse_names, data_dir) # add tissue sources if haven't already # anals <- add_sources(anals, data_dir) # pathway analysis for each contrast # path_anals <- diff_path(esets, anals, data_dir) # reload previous pathway analyses # path_anals <- load_path(gse_names, data_dir) # pathway meta analysis by tissue source # path_res <- path_meta(path_anals, ncores = 1, nperm = 5, by_source = TRUE) ## ---- message=FALSE, results='hide'-------------------------------------- # explore_paths(es_res, path_res) # for usage details ?explore_paths ## ---- eval=FALSE--------------------------------------------------------- # # subject is the focus of the meta-analysis (e.g. drug/disease name) # contribute(anals, subject = "LY294002") # # # Thank you! ## ------------------------------------------------------------------------ sessionInfo()