Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics
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...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57377-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850029331051446272 |
|---|---|
| author | 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 Eliezer M. Van Allen |
| author_facet | 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 Eliezer M. Van Allen |
| author_sort | Shreya Johri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a4fc5340e7204c0ca4fa0a239af0f83b |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a4fc5340e7204c0ca4fa0a239af0f83b2025-08-20T02:59:32ZengNature PortfolioNature Communications2041-17232025-03-0116111410.1038/s41467-025-57377-6Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomicsShreya Johri0Kevin Bi1Breanna M. Titchen2Jingxin Fu3Jake Conway4Jett P. Crowdis5Natalie I. Vokes6Zenghua Fan7Lawrence Fong8Jihye Park9David Liu10Meng Xiao He11Eliezer M. Van Allen12Department of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Thoracic and Head and Neck Oncology, MD Anderson Cancer CenterDivision of Hematology/Oncology, University of CaliforniaDivision of Hematology/Oncology, University of CaliforniaDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteDepartment of Medical Oncology, Dana-Farber Cancer InstituteAbstract 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.https://doi.org/10.1038/s41467-025-57377-6 |
| spellingShingle | 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 Eliezer M. Van Allen Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics Nature Communications |
| title | Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics |
| title_full | Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics |
| title_fullStr | Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics |
| title_full_unstemmed | Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics |
| title_short | Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics |
| title_sort | dissecting tumor cell programs through group biology estimation in clinical single cell transcriptomics |
| url | https://doi.org/10.1038/s41467-025-57377-6 |
| work_keys_str_mv | AT shreyajohri dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT kevinbi dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT breannamtitchen dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT jingxinfu dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT jakeconway dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT jettpcrowdis dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT natalieivokes dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT zenghuafan dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT lawrencefong dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT jihyepark dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT davidliu dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT mengxiaohe dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics AT eliezermvanallen dissectingtumorcellprogramsthroughgroupbiologyestimationinclinicalsinglecelltranscriptomics |