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.
