Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Shreya Johri, Kevin Bi, Breanna M. Titchen, Jingxin Fu, Jake Conway, Jett P. Crowdis, Natalie I. Vokes, Zenghua Fan, Lawrence Fong, Jihye Park, David Liu, Meng Xiao He and Eliezer M. Van Allen ()
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Shreya Johri: Dana-Farber Cancer Institute
Kevin Bi: Dana-Farber Cancer Institute
Breanna M. Titchen: Dana-Farber Cancer Institute
Jingxin Fu: Dana-Farber Cancer Institute
Jake Conway: Dana-Farber Cancer Institute
Jett P. Crowdis: Dana-Farber Cancer Institute
Natalie I. Vokes: MD Anderson Cancer Center
Zenghua Fan: University of California
Lawrence Fong: University of California
Jihye Park: Dana-Farber Cancer Institute
David Liu: Dana-Farber Cancer Institute
Meng Xiao He: Dana-Farber Cancer Institute
Eliezer M. Van Allen: Dana-Farber Cancer Institute

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract With the growth of clinical cancer single-cell RNA sequencing studies, robust differential expression methods for case/control analyses (e.g., treatment responders vs. non-responders) using gene signatures are pivotal to nominate hypotheses for further investigation. However, many commonly used methods produce a large number of false positives, do not adequately represent the patient-specific hierarchical structure of clinical single-cell RNA sequencing data, or account for sample-driven confounders. Here, we present a nonparametric statistical method, BEANIE, for differential expression of gene signatures between clinically relevant groups that addresses these issues. We demonstrate its use in simulated and real-world clinical datasets in breast cancer, lung cancer and melanoma. BEANIE outperforms existing methods in specificity while maintaining sensitivity, as demonstrated in simulations. Overall, BEANIE provides a methodological strategy to inform biological insights into unique and shared differentially expressed gene signatures across different tumor states, with utility in single-study, meta-analysis, and cross-validation across cell types.

Date: 2025
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DOI: 10.1038/s41467-025-57377-6

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