• Clustering RNAseq data, making heatmaps, and tree cutting to identify gene modules.

    From the archive (originally published 2017-04-04): Clustering is extremely useful for generating hypotheses and data exploration in general. The idea is that genes which have similar expression patterns (co-expression genes) are often controlled by the same regulatory mechanisms (co-regulated genes). Often times co-expressed genes share similar functions so by looking at which genes are found in a cluster we can get an idea of what that cluster is doing. Here we’ll show how to cluster RNAseq data using hierarchical clustering.
  • Clustering RNAseq data using K-means: how many clusters?

    From the archive: Clustering gene expression data allows us to identify substructures in the data and identify groups of genes that behave similarly. This method can help us identify genes that share a biological function (co-functional) and genes that are under the same control logic (co-regulated). Here we’ll show how to cluster RNAseq data using K-means clustering. We’ll address picking the appropriate clustering number then we’ll test drive some visualizations and plots.
  • Clustering RNAseq data using fuzzy c-means clustering

    Note this is part 3 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.