Detection of differentially abundant cell subpopulations in scRNA-seq data

Jun Zhao, Ariel Jaffe, Henry Li, Ofir Lindenbaum, Esen Sefik, Ruaidhrí Jackson, Xiuyuan Cheng, Richard A Flavell, Yuval Kluger

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.
Original languageEnglish
Article numbere2100293118
Pages (from-to)e2100293118
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number22
StatePublished - 1 Jun 2021

Bibliographical note

Funding Information:
Ximerakis et al. (17), cell/study/ SCP263/aging-mouse-brain#/.] ACKNOWLEDGMENTS. We thank Rihao Qu, Manolis Roulis, Jonathan Levinsohn, Peggy Myung, and Shelli Farhadian for useful discussions and suggestions. This work was supported by NIH Grants R01GM131642, UM1 DA051410, 2P50CA121974, R01DK121948, R01GM135928, and R01HG008383.

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.


  • *RNA-seq
  • *local differential abundance
  • *single cell
  • Aging/*genetics/metabolism
  • B-Lymphocytes/immunology/virology
  • Brain/cytology/metabolism
  • COVID-19/*genetics/immunology/pathology/virology
  • Cell Lineage/*genetics/immunology
  • Cytokines/genetics/immunology
  • Datasets as Topic
  • Dendritic Cells/immunology/virology
  • Gene Expression Profiling
  • Gene Expression Regulation
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Melanoma/*genetics/immunology/pathology
  • Monocytes/immunology/virology
  • Phenotype
  • RNA, Small Cytoplasmic/*genetics/immunology
  • SARS-CoV-2/pathogenicity
  • Severity of Illness Index
  • Single-Cell Analysis/methods
  • Skin Neoplasms/*genetics/immunology/pathology
  • T-Lymphocytes/immunology/virology
  • Transcriptome


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