1. Computational and Systems Biology
  2. Genetics and Genomics

Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

  1. Dylan Kotliar  Is a corresponding author
  2. Adrian Veres
  3. M Aurel Nagy
  4. Shervin Tabrizi
  5. Eran Hodis
  6. Douglas A Melton
  7. Pardis C Sabeti
  1. Harvard Medical School, United States
  2. Massachusetts Institute of Technology, United States
  3. Broad Institute of MIT and Harvard, United States
  4. Harvard University, United States
https://doi.org/10.7554/eLife.43803
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Cite this article
  1. Dylan Kotliar
  2. Adrian Veres
  3. M Aurel Nagy
  4. Shervin Tabrizi
  5. Eran Hodis
  6. Douglas A Melton
  7. Pardis C Sabeti
(2019)
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
eLife 8:e43803.
https://doi.org/10.7554/eLife.43803
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Abstract

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell's expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

Data availability

All of the analyzed real datasets are publicly available and the relevant GEO accession codes are included in the manuscript. All of the simulated and real data can be accessed through Code Ocean at the following URL: https://doi.org/10.24433/CO.9044782e-cb96-4733-8a4f-bf42c21399e6

The following previously published data sets were used

Article and author information

Author details

  1. Dylan Kotliar

    Department of Systems Biology, Harvard Medical School, Boston, United States
    For correspondence
    dylan_kotliar@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7968-645X

  2. Adrian Veres

    Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. M Aurel Nagy

    Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Shervin Tabrizi

    Viral Computational Genomics, Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Eran Hodis

    Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Douglas A Melton

    Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1623-5504

  7. Pardis C Sabeti

    Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institute of General Medical Sciences (T32GM007753)

  • Dylan Kotliar
  • Adrian Veres
  • M Aurel Nagy
  • Eran Hodis

National Institute of Allergy and Infectious Diseases (R01AI099210)

  • Pardis C Sabeti

U.S. Food and Drug Administration (HHSF223201810172C)

  • Dylan Kotliar
  • Pardis C Sabeti

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Alfonso Valencia, Barcelona Supercomputing Center - BSC, Spain

Version history

  1. Received: November 21, 2018
  2. Accepted: July 7, 2019
  3. Accepted Manuscript published: July 8, 2019 (version 1)
  4. Version of Record published: July 18, 2019 (version 2)

Copyright

© 2019, Kotliar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Dylan Kotliar
  2. Adrian Veres
  3. M Aurel Nagy
  4. Shervin Tabrizi
  5. Eran Hodis
  6. Douglas A Melton
  7. Pardis C Sabeti
(2019)
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
eLife 8:e43803.
https://doi.org/10.7554/eLife.43803

Share this article

https://doi.org/10.7554/eLife.43803

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