JOINTLY: interpretable joint clustering of single-cell transcriptomes

Andreas Fønss Møller, Jesper Grud Skat Madsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.

Original languageEnglish
Article number8473
JournalNature Communications
Volume14
Number of pages15
ISSN2041-1723
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Algorithms
  • Cluster Analysis
  • Gene Expression Profiling/methods
  • Sequence Analysis, RNA/methods
  • Single-Cell Analysis/methods
  • Transcriptome/genetics

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