Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts
Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts
Abstract Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling which limit their scope and generality. We propose a novel method that departs from standard analysis pipelines, comparing and clustering cells based not on their transcript or gene quantifications but on their transcript-compatibility read counts. In re-analysis of two landmark yet disparate single-cell RNA-Seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.
Kamath Govinda M.、Ntranos Vasilis、Zhang Jesse、Tse David N.、Pachter Lior
Department of Electrical Engineering, Stanford UniversityDepartment of Electrical Engineering and Computer Sciences, University of CaliforniaDepartment of Electrical Engineering, Stanford UniversityDepartment of Electrical Engineering, Stanford University||Department of Electrical Engineering and Computer Sciences, University of CaliforniaDepartments of Mathematics and Molecular and Cell Biology, University of California
细胞生物学分子生物学生物科学研究方法、生物科学研究技术
Kamath Govinda M.,Ntranos Vasilis,Zhang Jesse,Tse David N.,Pachter Lior.Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts[EB/OL].(2025-03-28)[2025-04-29].https://www.biorxiv.org/content/10.1101/036863.点此复制
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