Visualization

Martin Morgan
February 3, 2015

Three approaches

Genome-centric

Interactive visualization

Reports

And…

Lab

Gviz / ggbio

Work through vignette section 2 the Gviz package, and then the 'Plotting fold changes in genomic space' in the RNASeq lab.

Peruse the vignette of the ggbio package.

Run the following DESeq2 work flow to arrive at a top-table; coerce the result to a data.frame.

library(DESeq2)
library(airway)
data(airway)
se = airway
dds <- DESeqDataSet(se, design = ~ cell + dex)
dds$dex <- relevel(dds$dex, "untrt")
dds <- DESeq(dds)
res <- results(dds)
resdf <- as.data.frame(res)

A 'volcano plot' shows the relationship between P-value and log fold change. Here's a basic volcano plot using base graphics; create a volcano plot using ggplot2 and / or lattice.

plot(-log10(padj) ~ log2FoldChange, resdf)