Mercurial > repos > mingchen0919 > aurora_deseq2_site
diff DESeq_results.Rmd @ 0:6f94b4b9de44 draft
planemo upload
author | mingchen0919 |
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date | Tue, 27 Feb 2018 23:57:53 -0500 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/DESeq_results.Rmd Tue Feb 27 23:57:53 2018 -0500 @@ -0,0 +1,109 @@ +--- +title: 'DESeq2: Results' +output: + html_document: + number_sections: true + toc: true + theme: cosmo + highlight: tango +--- + +```{r setup, include=FALSE, warning=FALSE, message=FALSE} +knitr::opts_chunk$set( + echo = as.logical(opt$X_e), + error = TRUE +) +``` + + +```{r eval=TRUE} +# Import workspace +# fcp = file.copy(opt$X_W, "deseq.RData") +load(opt$X_W) +``` + +# Results {.tabset} + +## Result table + +```{r} +cat('--- View the top 100 rows of the result table ---') +res <- results(dds, contrast = c(opt$X_C, opt$X_T, opt$X_K)) +write.csv(as.data.frame(res), file = opt$X_R) +res_df = as.data.frame(res)[1:100, ] +datatable(res_df, style="bootstrap", filter = 'top', + class="table-condensed", options = list(dom = 'tp', scrollX = TRUE)) +``` + +## Result summary + +```{r} +summary(res) +``` + + +# MA-plot {.tabset} + + + +```{r} +cat('--- Shrinked with Bayesian procedure ---') +plotMA(res) +``` + + +# Histogram of p values + +```{r} +hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20, + col = "grey50", border = "white", main = "", + xlab = "Mean normalized count larger than 1") +``` + + +# Visualization {.tabset} +## Gene clustering + +```{r} +clustering_groups = strsplit(opt$X_M, ',')[[1]] + +topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20) +mat <- assay(rld)[ topVarGenes, ] +mat <- mat - rowMeans(mat) +annotation_col <- as.data.frame(colData(rld)[, clustering_groups]) +colnames(annotation_col) = clustering_groups +rownames(annotation_col) = colnames(mat) +pheatmap(mat, annotation_col = annotation_col) +``` + +## Sample-to-sample distance + +```{r} +sampleDistMatrix <- as.matrix( sampleDists ) +colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) +pheatmap(sampleDistMatrix, + clustering_distance_cols = sampleDists, + col = colors) +``` + +## PCA plot + +```{r} +plotPCA(rld, intgroup = clustering_groups) +``` + +## MDS plot {.tabset} + +### Data table +```{r} +mds <- as.data.frame(colData(rld)) %>% + cbind(cmdscale(sampleDistMatrix)) +knitr::kable(mds) +``` + +### Plot +```{r} +ggplot(mds, aes(x = `1`, y = `2`, col = time)) + + geom_point(size = 3) + coord_fixed() +``` +