# HG changeset patch
# User mingchen0919
# Date 1502143887 14400
# Node ID 507e408cd6cc3b502c3b1148c2fb92cd79202ab9
# Parent  665daa8b8bdb689a895b0c0eaa5b3081a6fb03eb
Uploaded

diff -r 665daa8b8bdb -r 507e408cd6cc DESeq_visualization.Rmd
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/DESeq_visualization.Rmd	Mon Aug 07 18:11:27 2017 -0400
@@ -0,0 +1,114 @@
+---
+title: 'DESeq2: Visualization'
+output:
+    html_document:
+      number_sections: true
+      toc: true
+      theme: cosmo
+      highlight: tango
+---
+
+```{r setup, include=FALSE, warning=FALSE, message=FALSE}
+knitr::opts_chunk$set(
+  echo = ECHO
+)
+
+library(stringi)
+library(DESeq2)
+library(pheatmap)
+library(PoiClaClu)
+library(RColorBrewer)
+```
+
+# Import workspace
+
+```{r eval=TRUE}
+fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
+load("deseq.RData")
+```
+
+# Visualization
+
+## Heatmaps of sample-to-sample distances {.tabset}
+
+### rlog-transformed values
+
+```{r}
+sampleDistMatrix <- as.matrix( sampleDists )
+colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
+pheatmap(sampleDistMatrix,
+         # clustering_distance_rows = sampleDists,
+         clustering_distance_cols = sampleDists,
+         col = colors)
+```
+
+### Poisson Distance
+
+```{r}
+count_t = t(counts(dds))
+rownames(count_t) = colnames(counts(dds))
+poisd <- PoissonDistance(count_t)
+samplePoisDistMatrix <- as.matrix( poisd$dd )
+rownames(samplePoisDistMatrix) = rownames(count_t)
+colnames(samplePoisDistMatrix) = rownames(count_t)
+pheatmap(samplePoisDistMatrix,
+         # clustering_distance_rows = poisd$dd,
+         clustering_distance_cols = poisd$dd,
+         col = colors)
+```
+
+
+## PCA plots {.tabset}
+
+### Using `plotPCA()` function 
+
+```{r}
+# interest groups
+col_index = as.numeric(strsplit("INTGROUPS_PCA", ',')[[1]])
+intgroup_pca = colnames(sample_table)[col_index]
+```
+
+```{r}
+plotPCA(rld, intgroup = intgroup_pca)
+```
+
+
+### Using *ggplot2*
+
+```{r}
+pcaData <- plotPCA(rld, intgroup = intgroup_pca, returnData = TRUE)
+percentVar <- round(100 * attr(pcaData, "percentVar"))
+ggplot(pcaData, aes(x = PC1, y = PC2, color = time)) +
+  geom_point(size =3) +
+  xlab(paste0("PC1: ", percentVar[1], "% variance")) +
+  ylab(paste0("PC2: ", percentVar[2], "% variance")) +
+  coord_fixed()
+```
+
+### PCA data table
+
+```{r}
+knitr::kable(pcaData)
+```
+
+
+## MDS plots {.tabset}
+
+### Using rlog-transformed values
+
+```{r}
+mds <- as.data.frame(colData(rld))  %>%
+         cbind(cmdscale(sampleDistMatrix))
+mds
+ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
+  geom_point(size = 3) + coord_fixed()
+```
+
+### Using the *Poisson Distance*
+
+```{r}
+mdsPois <- as.data.frame(colData(dds)) %>%
+   cbind(cmdscale(samplePoisDistMatrix))
+ggplot(mdsPois, aes(x = `1`, y = `2`, col = time)) +
+  geom_point(size = 3) + coord_fixed()
+```