Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition

Jonathan Mitchel; M Grace Gordon; Richard K Perez; Evan Biederstedt; Raymund Bueno; Chun Jimmie Ye; Peter V Kharchenko

doi:10.1038/s41587-024-02411-z

Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition

Nat Biotechnol. 2024 Sep 23. doi: 10.1038/s41587-024-02411-z. Online ahead of print.

Authors

Jonathan Mitchel^{1

2}, M Grace Gordon^{3

4}, Richard K Perez⁵, Evan Biederstedt¹, Raymund Bueno^{3

4}, Chun Jimmie Ye⁶, Peter V Kharchenko^{7

8}

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
² Program in Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA.
³ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
⁴ UCSF Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁵ School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁶ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. [email protected].
⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. [email protected].
⁸ San Diego Institute of Science, Altos Labs, San Diego, CA, USA. [email protected].

PMID: 39313646
DOI: 10.1038/s41587-024-02411-z

Abstract

Tissue-level and organism-level biological processes often involve the coordinated action of multiple distinct cell types. The recent application of single-cell assays to many individuals should enable the study of how donor-level variation in one cell type is linked to that in other cell types. Here we introduce a computational approach called single-cell interpretable tensor decomposition (scITD) to identify common axes of interindividual variation by considering joint expression variation across multiple cell types. scITD combines expression matrices from each cell type into a higher-order matrix and factorizes the result using the Tucker tensor decomposition. Applying scITD to single-cell RNA-sequencing data on 115 persons with lupus and 83 persons with coronavirus disease 2019, we identify patterns of coordinated cellular activity linked to disease severity and specific phenotypes, such as lupus nephritis. scITD results also implicate specific signaling pathways likely mediating coordination between cell types. Overall, scITD offers a tool for understanding the covariation of cell states across individuals, which can yield insights into the complex processes that define and stratify disease.