Heat map
Before moving on to the details of the ChIP-seq workflow in the next chapter, you have the opportunity here to preview some of the results of the analysis.
In this exercise, you will be looking at how to visualize differences between samples
using heat maps. The data has already been loaded and is formatted to allow plotting with the
heatmap()
function.
The sample correlation matrix is available as sample_cor
and normalized read counts
for each peak are stored in the read_counts
object. In both cases, the first two samples
are from primary tumors, the final two are treatment resistant.
You can pass a vector of group labels to the ColSideColors
and RowSideColors
arguments in the heatmap()
function to highlight which samples belong to the same group.
This exercise is part of the course
ChIP-seq with Bioconductor in R
Exercise instructions
- Create a vector of color names that can be used to label groups in the plot.
- Plot the sample correlation matrix
sample_cor
as a heat map. - Create a heat map of peak read counts.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a vector of colors to label groups (there are 2 samples per group)
group <- c(primary = rep("blue", ___), TURP = rep("red", ___))
# Plot the sample correlation matrix `sample_cor` as a heat map
# Use the group colors to label the rows and columns of the heat map
heatmap(___, ColSideColors = ___, RowSideColors = ___,
cexCol = 0.75, cexRow = 0.75, symm = TRUE)
# Create a heat map of peak read counts
# Use the group colors to label the columns of the heat map
___(___, ColSideColors = ___, labRow = "", cexCol = 0.75)