Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data

  1. Nighat Noureen
  2. Zhenqing Ye
  3. Yidong Chen
  4. Xiaojing Wang
  5. Siyuan Zheng  Is a corresponding author
  1. The University of Texas Health Science Center at San Antonio, United States
  2. University of Texas Health Science Center at San Antonio, United States

Abstract

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark five such methods, including single sample gene set enrichment analysis (ssGSEA), Gene Set Variation Analysis (GSVA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Single cell data sets used in this study including their downloading sources were listed in Supplementary Table 1. Gene sets were downloaded from MSigDB v.7.2. JASMINE source code is available on Github (https://github.com/NNoureen/JASMINE). Source Data contain the numerical data used to generate the figures.

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

Article and author information

Author details

  1. Nighat Noureen

    Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Zhenqing Ye

    Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Yidong Chen

    Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Xiaojing Wang

    Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Siyuan Zheng

    Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, United States
    For correspondence
    zhengs3@uthscsa.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1031-9424

Funding

Cancer Prevention and Research Institute of Texas (RR170055)

  • Siyuan Zheng

Cancer Prevention and Research Institute of Texas (RP170345)

  • Nighat Noureen

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

Copyright

© 2022, Noureen 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. Nighat Noureen
  2. Zhenqing Ye
  3. Yidong Chen
  4. Xiaojing Wang
  5. Siyuan Zheng
(2022)
Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data
eLife 11:e71994.
https://doi.org/10.7554/eLife.71994

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https://doi.org/10.7554/eLife.71994