Orchestrating single-cell analysis with Bioconductor
By R. Amezquita, Aaron T. L. Lun, Etienne Becht, Vincent J. Carey, Lindsay N. Carpp, L. Geistlinger, F. Marini, Kevin C. Rue-Albrecht, Davide Risso, Charlotte Soneson and 7 more. Nature Methods, 2019
840 citations recorded by the source corpus.
Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users. This Perspective highlights open-source software for single-cell analysis released as part of the Bioconductor project, providing an overview for users and developers.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b8ce412d8818a1eae73933fc5f6daf4925d4ac72
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Citations in the corpus
- Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, methodology): “We started with raw feature-barcode matrices for analysis with the Bioconductor suite of R packages for single-cell RNA-seq analysis (Amezquita et al., 2020).”
- Best practices for single-cell analysis across modalities (2023, methodology): “Profiling individual modalities on different populations of cells from the same biological sample is more common, leading to completely missing data matrices 245 .”