Data analysis step 6: Draw a heatmap from RNA-seq data using R
In the last post of this series, I left you with a gene expression profile of the effect of azacitidine on AML3 cells. I decided to use the DESeq output for downstream analysis. If we want to draw a heatmap at this stage, we might struggle because the output provided by the DEB applet does not send back the normalised count data for each sample.
It is not really useful to plot all 5704 genes with FDR adjusted p-values <0.05 on the heatmap, so I will simply show the top 100 by p-value. Here are the general steps I will use in my R script below:
Read the count matrix and DESeq table into R and merge into one tableSort based on p-value with most significant genes on topSelect the columns containing gene name and raw countsScale the data per rowSelect the top 100 genes by significanceGenerate the heatmap with mostly default values Google searches show that R has some quite elaborate heatmap options, especially with features from ggplot2 and RColorBrewer. In this example, I will use buil…
It is not really useful to plot all 5704 genes with FDR adjusted p-values <0.05 on the heatmap, so I will simply show the top 100 by p-value. Here are the general steps I will use in my R script below:
Read the count matrix and DESeq table into R and merge into one tableSort based on p-value with most significant genes on topSelect the columns containing gene name and raw countsScale the data per rowSelect the top 100 genes by significanceGenerate the heatmap with mostly default values Google searches show that R has some quite elaborate heatmap options, especially with features from ggplot2 and RColorBrewer. In this example, I will use buil…