# HG changeset patch
# User mingchen0919
# Date 1502143842 14400
# Node ID cf601273873710cf6aca86ea774a98e2a6fbe943
# Parent  b1ad9a9985737491880ae1df2e7533b641af4c46
Uploaded

diff -r b1ad9a998573 -r cf6012738737 DESeq_results.Rmd
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/DESeq_results.Rmd	Mon Aug 07 18:10:42 2017 -0400
@@ -0,0 +1,85 @@
+---
+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$set(
+  echo = ECHO
+)
+
+library(DESeq2)
+library(pheatmap)
+library(genefilter)
+```
+
+# Import workspace
+
+```{r eval=TRUE}
+fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
+load("deseq.RData")
+```
+
+# Results {.tabset}
+
+## Result table
+
+```{r}
+group = colnames(sample_table)[CONTRAST_GROUP]
+res <- results(dds, contrast = c(group, 'TREATMENT_LEVEL', 'CONDITION_LEVEL'))
+datatable(as.data.frame(res), style="bootstrap", filter = 'top',
+          class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
+```
+
+## Result summary
+
+```{r}
+summary(res)
+```
+
+
+# MA-plot {.tabset}
+
+## Shrinked with `lfcShrink()` function
+
+```{r eval=FALSE}
+shrink_res = DESeq2::lfcShrink(dds, contrast = c(group, 'TREATMENT_LEVEL', 'CONDITION_LEVEL'), res=res)
+plotMA(shrink_res)
+```
+
+## Shrinked with Bayesian procedure
+
+```{r}
+plotMA(res)
+```
+
+
+# Histogram of p values
+
+```{r}
+hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,
+     col = "grey50", border = "white", main = "",
+     xlab = "Mean normalized count larger than 1")
+```
+
+
+# Gene clustering
+
+```{r}
+group_index = as.numeric(strsplit("CLUSTERING_GROUPS", ',')[[1]])
+clustering_groups = colnames(sample_table)[group_index]
+
+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)
+```
+