SIG: Statistical Analysis and Comprehension of the Human Cell Atlas in R/Bioconductor #5
Introduction of yourself:
Should it be held during Developer Day?
Description of the topic:
A description the principal investigators and their role in the project is provided here:
Finally, in the birds-of-a-feather session we will discuss and highlight existing and proposed Bioconductor software aimed at the analysis of single-cell data to accomplish the aims of this project. For example, we have developed a unified representation for single-cell data with the SingleCellExperiment S4 class, which is an extension of the popular SummarizedExperiment class. In the past year, this class has been widely incorporated into many popular Bioconductor single-cell packages (e.g. scater, MAST, scDD, scPipe, scran, splatter, zinbwave, DropletUtils, clusterExperiment, SC3, destiny, and BASiCS) enabling improved interoperability between packages. To make tools and analyses scalable to millions of cells, we have proposed Bioconductor infrastructure and efficient data representations for large single-cell data with millions or billions of cells. This infrastructure is primarily based on out-of-memory computations with Bioconductor packages such a HDF5Array (implements HDF5-based on-disk representation), DelayedArray (implements lazy manipulation for efficient interactive analyses), rhdf5client (facilitates use of HDF Server or HDF Cloud for remote array data), and BioCParallel (standardizes parallel processing throughout the Bioconductor ecosystem).
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