SIG: Statistical Analysis and Comprehension of the Human Cell Atlas in R/Bioconductor #5
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Hi, Stephanie, thank you for the excellent proposal. I'm Zhun Miao from Tsinghua University of China, and I'm very interested to participate the group. Thank you! See you then! |
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Introduction of yourself:
Bioconductor developers involved in the Chan Zuckerberg Initiative (CZI) to develop collaborative computational tools for the Human Cell Atlas (HCA).
Should it be held during Developer Day?
Preferably, yes.
Desired outputs:
Description of the topic:
International projects generating large amounts of single-cell data, such as the Human Cell Atlas (HCA), have led to a great demand from researchers for fast, scalable, and efficient infrastructure and tools to analyze and to effectively extract knowledge from billions of single cells. This led to a call for applications for funding from the Chan-Zuckerberg Initiative (CZI) to develop collaborative computational tools to access, analyze and understand data from the HCA. The Bioconductor community submitted a joint proposal in August 2017 titled the Statistical Analysis and Comprehension of the Human Cell Atlas in R/Bioconductor and we were recently awarded funding for one year to (1) provide a coherent programmatic interface to the HCA, and (2) enable scalable interactive statistical analysis of large single-cell data. This birds-of-a-feather session is to provide a summary of what was done in the past year and what we plan to do in the next year. Our project aims are:
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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