## ----setup, echo=FALSE, results="hide"----------------------------------- library(BiocStyle) knitr::opts_chunk$set(tidy=FALSE, dev="png", message=FALSE, error=FALSE, warning=TRUE) ## ----load-data----------------------------------------------------------- library(DEGreport) data(humanGender) ## ----experiment---------------------------------------------------------- library(DESeq2) idx <- c(1:10, 75:85) dds <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx], colData(humanGender)[idx,], design=~group) dds <- DESeq(dds) res <- results(dds) ## ----count-design-------------------------------------------------------- counts <- counts(dds, normalized = TRUE) design <- as.data.frame(colData(dds)) ## ----check-factor-------------------------------------------------------- degCheckFactors(counts[, 1:6]) ## ----qc------------------------------------------------------------------ degQC(counts, design[["group"]], pvalue = res[["pvalue"]]) ## ----cov----------------------------------------------------------------- resCov <- degCovariates(log2(counts(dds)+0.5), colData(dds)) ## ----corcov-------------------------------------------------------------- cor <- degCorCov(colData(dds)) names(cor) ## ----report, eval=FALSE-------------------------------------------------- # createReport(colData(dds)[["group"]], counts(dds, normalized = TRUE), # row.names(res)[1:20], res[["pvalue"]], path = "~/Downloads") ## ----degComps------------------------------------------------------------ degs <- degComps(dds, combs = "group", contrast = list("group_Male_vs_Female", c("group", "Female", "Male"))) names(degs) ## ----deg----------------------------------------------------------------- deg(degs[[1]]) ## ----raw----------------------------------------------------------------- deg(degs[[1]], "raw", "tibble") ## ----significants-------------------------------------------------------- significants(degs[[1]], fc = 0, fdr = 0.05) ## ----significants-list--------------------------------------------------- significants(degs, fc = 0, fdr = 0.05) ## ----significants-list-full---------------------------------------------- significants(degs, fc = 0, fdr = 0.05, full = TRUE) ## ----plotMA-------------------------------------------------------------- degMA(degs[[1]], diff = 2, limit = 3) ## ----plotMA-raw---------------------------------------------------------- degMA(degs[[1]], diff = 2, limit = 3, raw = TRUE) ## ----plotMA-cor---------------------------------------------------------- degMA(degs[[1]], limit = 3, correlation = TRUE) ## ----deseq2-volcano------------------------------------------------------ res[["id"]] <- row.names(res) show <- as.data.frame(res[1:10, c("log2FoldChange", "padj", "id")]) degVolcano(res[,c("log2FoldChange", "padj")], plot_text = show) ## ----deseq2-gene-plots--------------------------------------------------- degPlot(dds = dds, res = res, n = 6, xs = "group") ## ----deseq2-gene-plot-wide----------------------------------------------- degPlotWide(dds, rownames(dds)[1:5], group="group") ## ----markers------------------------------------------------------------- data(geneInfo) degSignature(humanGender, geneInfo, group = "group") ## ----deseq2-------------------------------------------------------------- resreport <- degResults(dds = dds, name = "test", org = NULL, do_go = FALSE, group = "group", xs = "group", path_results = NULL) ## ----shiny, eval=FALSE--------------------------------------------------- # degObj(counts, design, "degObj.rda") # library(shiny) # shiny::runGitHub("lpantano/shiny", subdir="expression") ## ----pattern------------------------------------------------------------- ma = assay(rlog(dds))[row.names(res)[1:100],] res <- degPatterns(ma, design, time = "group") ## ----filter, results="asis"---------------------------------------------- cat("gene in original count matrix: 1000") filter_count <- degFilter(counts(dds), design, "group", min=1, minreads = 50) cat("gene in final count matrix", nrow(filter_count)) ## ----degColors----------------------------------------------------------- library(ComplexHeatmap) th <- HeatmapAnnotation(df = colData(dds), col = degColors(colData(dds), TRUE)) Heatmap(log2(counts(dds) + 0.5)[1:10,], top_annotation = th) library(pheatmap) pheatmap(log2(counts(dds) + 0.5)[1:10,], annotation_col = as.data.frame(colData(dds))[,1:4], annotation_colors = degColors(colData(dds)[1:4], con_values = c("white", "red") ) ) ## ----sessionInfo--------------------------------------------------------- sessionInfo()