Get startedGet started for free

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.

This exercise is part of the course

ChIP-seq with Bioconductor in R

View Course

Exercise instructions

  • Compute the pairwise distances between samples using dist().
  • Use hclust() to create a dendrogram from the distance matrix.
  • Plot the dendrogram.

Hands-on interactive exercise

Have a go at this exercise by completing this sample 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(___)
Edit and Run Code