Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

Zhang, R, Atwal, GS, Lim, WK (March 2021) Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing. Patterns, 2 (3). p. 100211. ISSN 2666-3899

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DOI: 10.1016/j.patter.2021.100211

Abstract

In this study, we benchmarked five representative single-cell RNA-sequencing data-preprocessing methods with a focus on their influence in inferring gene-gene expression correlations. We found that substantial correlation artifacts have been introduced during the preprocessing steps due to data oversmoothing, raising the issue that correlation computed from these preprocessed data may not be reliable and should be treated with caution. We then proposed a noise-regularization method to penalize the oversmoothed data, which can effectively eliminate the artifacts while retaining the majority of the true correlations. The regularized correlations can be further applied to construct gene-gene correlation networks, which is helpful for obtaining mechanistic insights into the complex biological systems.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
Investigative techniques and equipment > assays > Single cell sequencing
CSHL Authors:
Communities: CSHL labs > Atwal lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 12 March 2021
Date Deposited: 06 May 2021 20:54
Last Modified: 06 May 2021 20:54
URI: https://repository.cshl.edu/id/eprint/40034

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