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Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data

Author

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  • Alok K. Maity

    (University of Chinese Academy of Sciences, Chinese Academy of Sciences)

  • Andrew E. Teschendorff

    (University of Chinese Academy of Sciences, Chinese Academy of Sciences
    University College London)

Abstract

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Suggested Citation

  • Alok K. Maity & Andrew E. Teschendorff, 2023. "Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39017-z
    DOI: 10.1038/s41467-023-39017-z
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    References listed on IDEAS

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    1. M. Büttner & J. Ostner & C. L. Müller & F. J. Theis & B. Schubert, 2021. "scCODA is a Bayesian model for compositional single-cell data analysis," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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