Putative cell type discovery from single-cell gene expression data.

Journal: Nature methods
Published Date: May 18, 2020

Abstract

We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The differentially expressed feature genes discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterized by the feature genes as markers. Benchmarking using expert-annotated scRNA-seq datasets shows that our method automatically identifies the 'ground truth' cell assignments with high accuracy.

Authors

  • Zhichao Miao
    European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK.
  • Pablo Moreno
    Cheminformatics and Metabolism, European Molecular Biology Laboratory - European Bioinformatics Institute, Cambridge, UK. pmoreno@ebi.ac.uk.
  • Ni Huang
    European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK.
  • Irene Papatheodorou
    EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.
  • Alvis Brazma
    European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK. brazma@ebi.ac.uk.
  • Sarah A Teichmann
    Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

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