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Review
. 2020 Feb;17(2):137-145.
doi: 10.1038/s41592-019-0654-x. Epub 2019 Dec 2.

Orchestrating single-cell analysis with Bioconductor

Robert A Amezquita  1 , Aaron T L Lun  2   3 , Etienne Becht  1 , Vince J Carey  4 , Lindsay N Carpp  1 , Ludwig Geistlinger  5   6 , Federico Marini  7   8 , Kevin Rue-Albrecht  9 , Davide Risso  10   11 , Charlotte Soneson  12   13 , Levi Waldron  5   6 , Hervé Pagès  1 , Mike L Smith  14 , Wolfgang Huber  14 , Martin Morgan  15 , Raphael Gottardo  16 , Stephanie C Hicks  17
Affiliations
Review

Orchestrating single-cell analysis with Bioconductor

Robert A Amezquita et al. Nat Methods. 2020 Feb.

Erratum in

Abstract

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.

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Conflict of interest statement

Competing Financial Interests

RG declares ownership in CellSpace Biosciences.

Figures

Figure 1:
Figure 1:. Number of Bioconductor packages for the analysis of high-throughput sequencing data over ten years.
Bioconductor software packages associated with the analysis of sequencing data were tracked by date of submission over the course of ten years. Software packages were uniquely defined by their primary sequencing technology association, with examples of specific terms used for annotation in parentheses.
Figure 2:
Figure 2:. Overview of the SingleCellExperiment class.
The SingleCellExperiment class instantiates an object (SingleCellExperiment herein abbreviated sce) capable of storing various datatypes associated with single-cell assays. A sce object is organized into components (e.g. rowData, assays, colData, reducedDims). In the assays component the rows represent features such as genes (horizontal pink bands), and the columns represent cells (vertical yellow band). The rowData and colData components can hold information (such as metadata) about the features and cells, respectively. Note that in the colData and reducedDims components, cells are represented as rows (horizontal yellow bands) and the number of columns in the assays component must match the number of rows in the colData and reducedDims components.
Figure 3:
Figure 3:. Bioconductor workflow for analyzing single-cell data.
A typical analytical workflow using Bioconductor leads to the creation and evolution of a SingleCellExperiment (or sce) object during data processing and downstream statistical analysis (left column). An example of a sce object evolving throughout the course of a workflow is shown, including visualization, analysis, and annotation (right column).
Figure 4:
Figure 4:. Select visualizations derived from various Bioconductor workflows.
Various visualizations associated with preprocessing (blue boxes) and downstream statistical analyses (orange boxes). The example data set used throughout was generated as part of the Human Cell Atlas [21]). Details on the generation of these figures are described in our online companion book (https://osca.bioconductor.org)
See this image and copyright information in PMC

References

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