diff DESeq_results.Rmd @ 6:2f8ddef8d545 draft

update deseq2

--- a/DESeq_results.Rmd	Tue Aug 08 15:06:40 2017 -0400
+++ b/DESeq_results.Rmd	Tue Nov 07 13:50:32 2017 -0500
@@ -10,17 +10,14 @@
 
 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
 knitr::opts_chunk$set(
-  echo = ECHO
+  echo = ECHO,
+  error = TRUE
 )
-
-library(DESeq2)
-library(pheatmap)
-library(genefilter)
 ```
 
-# Import workspace
 
 ```{r eval=TRUE}
+# Import workspace
 fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
 load("deseq.RData")
 ```
@@ -30,9 +27,11 @@
 ## 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',
+cat('--- View the top 100 rows of the result table ---')
+res <- results(dds, contrast = c('CONTRAST_FACTOR', 'TREATMENT_LEVEL', 'CONDITION_LEVEL'))
+write.csv(as.data.frame(res), file = 'deseq_results.csv')
+res_df = as.data.frame(res)[1:100, ]
+datatable(res_df, style="bootstrap", filter = 'top',
           class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
 ```
 
@@ -45,16 +44,10 @@
 
 # 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}
+cat('--- Shrinked with Bayesian procedure ---')
 plotMA(res)
 ```
 
@@ -68,11 +61,11 @@
 ```
 
 
-# Gene clustering
+# Visualization {.tabset}
+## Gene clustering
 
 ```{r}
-group_index = as.numeric(strsplit("CLUSTERING_GROUPS", ',')[[1]])
-clustering_groups = colnames(sample_table)[group_index]
+clustering_groups = strsplit("CLUSTERING_FACTORS", ',')[[1]]
 
 topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)
 mat  <- assay(rld)[ topVarGenes, ]
@@ -83,3 +76,34 @@
 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()
+```
+