Published May 9, 2022
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Depth normalization for single-cell genomics count data
Abstract
Single-cell genomics analysis requires normalization of feature counts that stabilizes variance while accounting for variable cell sequencing depth. We discuss some of the trade-offs present with current widely used methods, and analyze their performance on 526 single-cell RNA-seq datasets. The results lead us to recommend proportional fitting prior to log transformation followed by an additional proportional fitting.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. This version posted May 6, 2022. This project started with an investigation of normalization of orthogonal barcoding tags. We thank John Thompson and Linda Hsieh-Wilson for helpful initial discussions related to that problem. We thank Tara Chari for helpful insights on normalization and clustering. Lambda Moses helped with reviewing the Seurat source code. Data and code availability: All data and code to reproduce the figures and results in the paper are available at https://github.com/pachterlab/BHGP_2022. Author contributions: A.S.B., and L.P. developed the project idea. A.G.M. pre-processed the datasets. A.S.B. performed the analysis. A.S.B. and A.G.M. compiled the supplementary material. A.S.B. drafted the paper. I.B.H. performed the overdispersion simulation. A.S.B., I.B.H., A.G.M., and L.P. wrote, reviewed, and edited the paper. The authors declare no competing interests.Attached Files
Submitted - 2022.05.06.490859v1.full.pdf
Supplemental Material - media-1.pdf
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2022.05.06.490859v1.full.pdf
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Additional details
- Eprint ID
- 114652
- Resolver ID
- CaltechAUTHORS:20220509-762177400
- Created
-
2022-05-09Created from EPrint's datestamp field
- Updated
-
2022-05-09Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering