Abstract
AbstractResearchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
Funder
National Institute of General Medical Sciences
Directorate for Biological Sciences
Directorate for Mathematical and Physical Sciences
Johnson and Johnson
Alfred P. Sloan Foundation
W. M. Keck Foundation
Publisher
Springer Science and Business Media LLC
Cited by
367 articles.
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