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  Effect of imputation on gene network reconstruction from single-cell RNA-seq data

Ly, L.-H., & Vingron, M. (2022). Effect of imputation on gene network reconstruction from single-cell RNA-seq data. Patterns, 3: 100414. doi:10.1016/j.patter.2021.100414.

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Patterns_Ly and Vingron_2021.pdf (Publisher version), 3MB
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 Creators:
Ly, Lam-Ha1, Author              
Vingron, Martin1, Author              
Affiliations:
1Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: imputation gene regulation network reconstruction network inference single-cell data analysis single-cell RNA-seq
 Abstract: Despite the advances in single-cell transcriptomics, the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA sequencing (scRNA-seq) data make it hard to infer networks. Imputation can be applied in order to enhance gene-gene correlations and facilitate downstream analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks. To study this, we evaluate the differences on the performance and structure of reconstructed networks before and after imputation in single-cell data. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. However, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms while warning against the indiscriminate use of imputation before network reconstruction in general.

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Language(s): eng - English
 Dates: 2021-11-252022-02-11
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.patter.2021.100414
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Title: Patterns
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 3 Sequence Number: 100414 Start / End Page: - Identifier: ISSN: 2666-3899
CoNE: https://pure.mpg.de/cone/journals/resource/2666-3899