SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data
SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data
ABSTRACT Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here, we present single-cell aggregation of cell-states (SEACells), an algorithm for identifying metacells; overcoming the sparsity of single-cell data, while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying accurate, compact, and well-separated metacells in both RNA and ATAC modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and measure gene accessibility in each metacell. Metacell-level analysis scales to large datasets and are particularly well suited for patient cohorts, including facilitation of data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation, and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a COVID-19 patient cohort.
Setty Manu、Masilionis Ignas、Chalign¨| Ronan、Nawy Tal、Pe?ˉer Dana、Brown Chrysothemis C、Pe?ˉer Itsik、Persad Sitara、Choo Zi-Ning、Dien Christine
Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center||Basic Sciences Division, Fred Hutchinson Cancer Research Center||Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Research CenterComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterHuman Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center||Department of Pediatrics, Memorial Sloan Kettering Cancer CenterDepartment of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia UniversityComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center||Department of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia UniversityComputational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer CenterBasic Sciences Division, Fred Hutchinson Cancer Research Center||Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Research Center
生物科学研究方法、生物科学研究技术细胞生物学分子生物学
Setty Manu,Masilionis Ignas,Chalign¨| Ronan,Nawy Tal,Pe?ˉer Dana,Brown Chrysothemis C,Pe?ˉer Itsik,Persad Sitara,Choo Zi-Ning,Dien Christine.SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data[EB/OL].(2025-03-28)[2025-06-09].https://www.biorxiv.org/content/10.1101/2022.04.02.486748.点此复制
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