Clustering RNA-seq expression data using grade of membership models

Grade of membership models, also known as “admixture models”, “topic models” or “Latent Dirichlet Allocation”, are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple “populations”, and in natural language processing to model documents having words from multiple “topics”.

Here researchers from the University of Chicago illustrate the potential for these models to cluster samples of RNA-seq gene expression data, measured on either bulk samples or single cells. They also provide methods to help interpret the clusters, by identifying genes that are distinctively expressed in each cluster. By applying these methods to several example RNA-seq applications they demonstrate their utility in identifying and summarizing structure and heterogeneity. Applied to data from the GTEx project on 51 human tissues, the approach highlights similarities among biologically-related tissues and identifies distinctively-expressed genes that recapitulate known biology. Applied to single-cell expression data from mouse preimplantation embryos, the approach highlights both discrete and continuous variation through early embryonic development stages, and highlights genes involved in a variety of relevant processes – from germ cell development, through compaction and morula formation, to the formation of inner cell mass and trophoblast at the blastocyte stage.

(a): Structure plot of estimated membership proportions for GoM model with K = 20 clusters fit to 8555 tissue samples from 53 tissues in GTEx data. Each horizontal bar shows the cluster membership proportions for a single sample, ordered so that samples from the same tissue are adjacent to one another. Within each tissue, the samples are sorted by the proportional representation of the underlying clusters. (b): Structure plot of estimated membership proportions for K = 4 clusters fit to only the brain tissue samples. This analysis highlights finer-scale structure among the brain samples that is missed by the global analysis in (a).

Availability – The methods are implemented in the Bioconductor package CountClust: https://www.bioconductor.org/packages/3.3/bioc/html/CountClust.html