Self-assembling manifolds in single-cell RNA sequencing data

  1. Alexander J Tarashansky
  2. Yuan Xue
  3. Pengyang Li
  4. Stephen R Quake
  5. Bo Wang  Is a corresponding author
  1. Stanford University, United States

Abstract

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

Data availability

The schistosome stem cell scRNAseq data generated in this study is available through the Gene Expression Omnibus (GEO) under accession number GSE116920.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Alexander J Tarashansky

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Yuan Xue

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Pengyang Li

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Stephen R Quake

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1613-0809
  5. Bo Wang

    Department of Bioengineering, Stanford University, Stanford, United States
    For correspondence
    wangbo@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8880-1432

Funding

Burroughs Wellcome Fund

  • Bo Wang

Arnold and Mabel Beckman Foundation

  • Bo Wang

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

Ethics

Animal experimentation: In adherence to the Animal Welfare Act and the Public Health Service Policy on Humane Care and Use of Laboratory Animals, all experiments with and care of mice were performed in accordance with protocols approved by the Institutional Animal Care and Use Committees (IACUC) of Stanford University (protocol approval number 30366).

Reviewing Editor

  1. Alex K Shalek, Broad Institute of MIT and Harvard, United States

Publication history

  1. Received: June 3, 2019
  2. Accepted: September 16, 2019
  3. Accepted Manuscript published: September 16, 2019 (version 1)
  4. Version of Record published: October 16, 2019 (version 2)

Copyright

© 2019, Tarashansky 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. Alexander J Tarashansky
  2. Yuan Xue
  3. Pengyang Li
  4. Stephen R Quake
  5. Bo Wang
(2019)
Self-assembling manifolds in single-cell RNA sequencing data
eLife 8:e48994.
https://doi.org/10.7554/eLife.48994

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