1. Computational and Systems Biology
  2. Stem Cells and Regenerative Medicine
Download icon

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
Tools and Resources
  • Cited 11
  • Views 5,451
  • Annotations
Cite this article as: eLife 2019;8:e48994 doi: 10.7554/eLife.48994

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.

Metrics

  • 5,451
    Page views
  • 815
    Downloads
  • 11
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Categories and tags

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology

    Mapping single-cell atlases throughout Metazoa unravels cell type evolution

    Alexander J Tarashansky et al.
    Research Advance Updated

    Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

    1. Computational and Systems Biology

    Inequalities in the distribution of National Institutes of Health research project grant funding

    Michael S Lauer, Deepshikha Roychowdhury
    Research Article Updated

    Previous reports have described worsening inequalities of National Institutes of Health (NIH) funding. We analyzed Research Project Grant data through the end of Fiscal Year 2020, confirming worsening inequalities beginning at the time of the NIH budget doubling (1998–2003), while finding that trends in recent years have reversed for both investigators and institutions, but only to a modest degree. We also find that career-stage trends have stabilized, with equivalent proportions of early-, mid-, and late-career investigators funded from 2017 to 2020. The fraction of women among funded PIs continues to increase, but they are still not at parity. Analyses of funding inequalities show that inequalities for investigators, and to a lesser degree for institutions, have consistently been greater within groups (i.e. within groups by career stage, gender, race, and degree) than between groups.

    1. Computational and Systems Biology
    2. Epidemiology and Global Health

    Characterizing human mobility patterns in rural settings of sub-Saharan Africa

    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.