Visualizing the structure of RNA-seq expression data using grade of membership models.

ABSTRACT: 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 we illustrate the potential for these models to cluster samples of RNA-seq gene expression data, measured on either bulk samples or single cells. We 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 we demonstrate their ut

SUBMITTER: Dey KK 

PROVIDER: S-EPMC5363805 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature