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Depth normalization for single-cell genomics count data

View ORCID ProfileA. Sina Booeshaghi, View ORCID ProfileIngileif B. Hallgrímsdóttir, View ORCID ProfileÁngel Gálvez-Merchán, View ORCID ProfileLior Pachter
doi: https://doi.org/10.1101/2022.05.06.490859
A. Sina Booeshaghi
1Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA
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  • ORCID record for A. Sina Booeshaghi
Ingileif B. Hallgrímsdóttir
2Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
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  • ORCID record for Ingileif B. Hallgrímsdóttir
Ángel Gálvez-Merchán
2Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
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  • ORCID record for Ángel Gálvez-Merchán
Lior Pachter
2Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
3Department of Computing and Mathematical Sciences, Pasadena, CA
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  • For correspondence: lpachter@caltech.edu
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/pachterlab/BHGP_2022

Copyright 
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.
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Posted May 06, 2022.
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Depth normalization for single-cell genomics count data
A. Sina Booeshaghi, Ingileif B. Hallgrímsdóttir, Ángel Gálvez-Merchán, Lior Pachter
bioRxiv 2022.05.06.490859; doi: https://doi.org/10.1101/2022.05.06.490859
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Depth normalization for single-cell genomics count data
A. Sina Booeshaghi, Ingileif B. Hallgrímsdóttir, Ángel Gálvez-Merchán, Lior Pachter
bioRxiv 2022.05.06.490859; doi: https://doi.org/10.1101/2022.05.06.490859

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