Orchestrating Single-Cell Analysis with Bioconductor
Orchestrating Single-Cell Analysis with Bioconductor
Abstract Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.
Rue-Albrecht Kevin、Waldron Levi、Smith Mike、Morgan Martin、Gottardo Raphael、Marini Federico、Huber Wolfgang、Geistlinger Ludwig、Amezquita Robert A.、Carey Vince J.、Soneson Charlotte、Carpp Lindsay N.、Risso Davide、Lun Aaron T. L.、Pag¨¨s Herv¨|、Hicks Stephanie C.
Kennedy Institute of Rheumatology, University of OxfordGraduate School of Public Health and Health Policy, City University of New York||Institute for Implementation Science in Population Health, City University of New YorkEuropean Molecular Biology LaboratoryBiostatistics and Bioinformatics, Roswell Park Comprehensive Cancer CenterFred Hutchinson Cancer Research CenterInstitute of Medical Biostatistics||Center for Thrombosis and HemostasisEuropean Molecular Biology LaboratoryGraduate School of Public Health and Health Policy, City University of New York||Institute for Implementation Science in Population Health, City University of New YorkFred Hutchinson Cancer Research CenterChanning Division of Network MedicineFriedrich Miescher Institute for Biomedical Research||SIB Swiss Institute of BioinformaticsFred Hutchinson Cancer Research CenterDepartment of Statistical Sciences, University of Padua||Division of Biostatistics and Epidemiology, Department of Healthcare Policy and ResearchCancer Research UK Cambridge Institute, University of CambridgeFred Hutchinson Cancer Research CenterDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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
Rue-Albrecht Kevin,Waldron Levi,Smith Mike,Morgan Martin,Gottardo Raphael,Marini Federico,Huber Wolfgang,Geistlinger Ludwig,Amezquita Robert A.,Carey Vince J.,Soneson Charlotte,Carpp Lindsay N.,Risso Davide,Lun Aaron T. L.,Pag¨¨s Herv¨|,Hicks Stephanie C..Orchestrating Single-Cell Analysis with Bioconductor[EB/OL].(2025-03-28)[2025-08-03].https://www.biorxiv.org/content/10.1101/590562.点此复制
评论