SC3 - consensus clustering of single-cell RNA-Seq data
SC3 - consensus clustering of single-cell RNA-Seq data
Abstract Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.
Kirschner Kristina、Kiselev Vladimir Yu.、Schaub Michael T.、Natarajan Kedar N、Hemberg Martin、Yiu Andrew、Chandra Tamir、Green Anthony R、Reik Wolf、Andrews Tallulah、Barahona Mauricio
Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, CambridgeWellcome Trust Sanger Institute, Hinxton, CambridgeDepartment of Mathematics and naXys, University of Namur||ICTEAM, Universit¨| catholique de LouvainWellcome Trust Sanger Institute, Hinxton, Cambridge||EMBL-European Bioinformatics Institute, Hinxton, CambridgeWellcome Trust Sanger Institute, Hinxton, CambridgeWellcome Trust Sanger Institute, Hinxton, CambridgeWellcome Trust Sanger Institute, Hinxton, Cambridge||Epigenetics Programme, The Babraham Institute, Babraham, CambridgeCambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, CambridgeWellcome Trust Sanger Institute, Hinxton, Cambridge||Epigenetics Programme, The Babraham Institute, Babraham, Cambridge||Centre for Trophoblast Research, University of Cambridge, CambridgeWellcome Trust Sanger Institute, Hinxton, CambridgeDepartment of Mathematics, Imperial College London, London
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
Kirschner Kristina,Kiselev Vladimir Yu.,Schaub Michael T.,Natarajan Kedar N,Hemberg Martin,Yiu Andrew,Chandra Tamir,Green Anthony R,Reik Wolf,Andrews Tallulah,Barahona Mauricio.SC3 - consensus clustering of single-cell RNA-Seq data[EB/OL].(2025-03-28)[2025-08-02].https://www.biorxiv.org/content/10.1101/036558.点此复制
评论