Clustering samples
Another way to look at similarities between samples is through hierarchical clustering. This process involves two stages. First, you'll have to calculate the
distance between samples based on the (normalized coverage) of peaks using the dist() function. Then you can use these pairwise distances to group similar samples together
using hclust(). This will produce a dendrogram that captures the hierarchical relationship between samples.
A matrix with suitably normalized coverage data is available as R object cover.
Cet exercice fait partie du cours
ChIP-seq with Bioconductor in R
Instructions
- Compute the pairwise distances between samples using
dist(). - Use
hclust()to create a dendrogram from the distance matrix. - Plot the dendrogram.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Compute the pairwise distances between samples using `dist`
cover_dist <- ___(t(cover))
# Use `hclust()` to create a dendrogram from the distance matrix
cover_dendro <- ___(cover_dist)
# Plot the dendrogram
plot(___)