3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Visualizing the Structure of RNA-seq Expression Data using Grade of Membership Models

      Preprint
      , ,
      bioRxiv

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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 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. The methods are implemented in the Bioconductor package CountClust.

          Related collections

          Author and article information

          Journal
          bioRxiv
          May 04 2016
          Article
          10.1101/051631
          abc7536c-b9ab-46cc-b06c-0a767050381d
          © 2016
          History

          Genetics
          Genetics

          Comments

          Comment on this article