Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics
Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics
Abstract Given the growing number of clinically integrated cancer single-cell transcriptomic studies, robust differential enrichment methods for gene signatures to dissect tumor cellular states for discovery and translation are critical. Current analysis strategies neither adequately represent the hierarchical structure of clinical single-cell transcriptomic datasets nor account for the variability in the number of recovered cells per sample, leading to results potentially confounded by sample-driven biology with high false positives instead of accurately representing true differential enrichment of group-level biology (e.g., treatment responders vs. non-responders). This problem is especially prominent for single-cell analyses of the tumor compartment, because high intra-patient similarity (as opposed to inter-patient similarity) results in stricter hierarchical structured data that confounds enrichment analysis. Furthermore, to identify signatures which are truly representative of the entire group, there is a need to quantify the robustness of otherwise statistically significant signatures to sample exclusion. Here, we present a new nonparametric statistical method, BEANIE, to account for these issues, and demonstrate its utility in two cancer cohorts stratified by clinical groups to reduce biological hypotheses and guide translational investigations. Using BEANIE, we show how the consideration of sample-specific versus group biology greatly decreases the false positive rate and guides identification of robust signatures that can also be corroborated across different cell type compartments.
Fan Zenghua、Fong Lawrence、He Meng Xiao、Van Allen Eliezer M.、Johri Shreya、Bi Kevin、Conway Jake、Crowdis Jett P.、Titchen Breanna M.、Park Jihye、Fu Jingxin、Volkes Natalie I.、Liu David
Division of Hematology/Oncology, Department of Medicine, University of California Parker Institute for Cancer Immunotherapy, University of CaliforniaDivision of Hematology/Oncology, Department of Medicine, University of California Parker Institute for Cancer Immunotherapy, University of CaliforniaDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MIT||Harvard Graduate Program in Bioinformatics and Integrative GenomicsDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MIT||Harvard Graduate Program in Biological and Biomedical SciencesDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MITDepartment of Thoracic and Head and Neck Oncology, MD Anderson Cancer Center, Department of Genomic MedicineDepartment of Medical Oncology, Dana-Farber Cancer Institute||Broad Institute of Harvard and MIT
医学研究方法肿瘤学细胞生物学
Fan Zenghua,Fong Lawrence,He Meng Xiao,Van Allen Eliezer M.,Johri Shreya,Bi Kevin,Conway Jake,Crowdis Jett P.,Titchen Breanna M.,Park Jihye,Fu Jingxin,Volkes Natalie I.,Liu David.Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics[EB/OL].(2025-03-28)[2025-04-24].https://www.biorxiv.org/content/10.1101/2021.10.22.465130.点此复制
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