Note this is part 2 of a series on clustering RNAseq data. Check out part one on hierarcical clustering here and part two on K-means clustering here. Clustering gene expression is a particularly useful data reduction technique for RNAseq experiments. It allows us to bin genes by expression profile, correlate those bins to external factors like phenotype, and discover groups of co-regulated genes. Two common methods for clustering are hierarchical (agglomerative) clustering and k-means (centroid based) clustering which we discussed in part one and part two of this series.