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.
Diese Übung ist Teil des Kurses
ChIP-seq with Bioconductor in R
Anleitung zur Übung
- Compute the pairwise distances between samples using
dist(). - Use
hclust()to create a dendrogram from the distance matrix. - Plot the dendrogram.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# 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(___)