## ----eval=FALSE--------------------------------------------------------------- # library("recount3") # library("DESeq2") # library("dplyr") # library("stringr") # library("SummarizedExperiment") # library("tidyr") # library("tibble") # library("magrittr") ## ----eval=FALSE--------------------------------------------------------------- # # # download data recount3 # proj_info <- available_projects() %>% # filter(project == "SRP093386") # se <- create_rse(proj_info) # # count_mat <- compute_read_counts(se) # rownames(count_mat) <- str_replace(rownames(count_mat), "\\.[:number:]+$", "") # # meta <- colData(se) %>% # as_tibble() %>% # separate(sra.sample_title, into = c("cell_line", "treatment", "mutation", "replicate"), sep = "-") %>% # select(cell_line, treatment, mutation, replicate) # # colnames(count_mat) <- paste0(meta$treatment, "_", meta$mutation, "_", meta$replicate) # # # subset to cell line T47D # count_mat <- count_mat[, meta$cell_line == "T47D"] # meta <- meta[meta$cell_line == "T47D", c("treatment", "mutation", "replicate")] # # T47D <- make_dds(count_mat, meta, ah_record = "AH89426") # T47D <- T47D[rowSums(assay(T47D))>0,] # # # round some numeric data to reduce the size of the data object # rowData(T47D)$gc_content <- round(rowData(T47D)$gc_content,1) ## ----eval=FALSE--------------------------------------------------------------- # dds <- T47D # dds <- filter_genes(dds, min_count = 5, min_rep = 4) # dds$mutation <- as.factor(dds$mutation) # dds$treatment <- as.factor(dds$treatment) # design(dds) <- ~ mutation + treatment # # # to not run DESeq2 in the main vignette, # # wo pre-compute the dispersion plot and diff testing results # dds <- DESeq(dds, parallel=T) # # png(filename="disp_ests.png", width=7, height=5, units="in", res=200) # plotDispEsts(dds) # dev.off() # # T47D_diff_testing <- lfcShrink(dds, coef = "mutation_WT_vs_D538G", lfcThreshold = log2(1.5), type = "normal", parallel = TRUE) # T47D_diff_testing$stat <- NULL # T47D_diff_testing$lfcSE <- NULL # T47D_diff_testing$pvalue <- NULL