Effect of Imputation on Gene Network Reconstruction from Single-cell RNA-seq Data

Overview

Journal Patterns (N Y)

Date 2022 Feb 24

PMID 35199064

Authors

Lam-Ha Ly

Martin Vingron

Affiliations

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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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References

Breda J, Zavolan M, van Nimwegen E . Bayesian inference of gene expression states from single-cell RNA-seq data. Nat Biotechnol. 2021; 39(8):1008-1016. DOI: 10.1038/s41587-021-00875-x. View

Blencowe M, Arneson D, Ding J, Chen Y, Saleem Z, Yang X . Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg Top Life Sci. 2020; 3(4):379-398. PMC: 7141415. DOI: 10.1042/etls20180176. View

Huynh-Thu V, Irrthum A, Wehenkel L, Geurts P . Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010; 5(9). PMC: 2946910. DOI: 10.1371/journal.pone.0012776. View

Szklarczyk D, Gable A, Lyon D, Junge A, Wyder S, Huerta-Cepas J . STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2018; 47(D1):D607-D613. PMC: 6323986. DOI: 10.1093/nar/gky1131. View

Lun A, Bach K, Marioni J . Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016; 17:75. PMC: 4848819. DOI: 10.1186/s13059-016-0947-7. View

Hou W, Ji Z, Ji H, Hicks S . A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol. 2020; 21(1):218. PMC: 7450705. DOI: 10.1186/s13059-020-02132-x. View

Chu L, Leng N, Zhang J, Hou Z, Mamott D, Vereide D . Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 2016; 17(1):173. PMC: 4989499. DOI: 10.1186/s13059-016-1033-x. View

Nestorowa S, Hamey F, Pijuan Sala B, Diamanti E, Shepherd M, Laurenti E . A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood. 2016; 128(8):e20-31. PMC: 5305050. DOI: 10.1182/blood-2016-05-716480. View

Moerman T, Aibar Santos S, Gonzalez-Blas C, Simm J, Moreau Y, Aerts J . GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2018; 35(12):2159-2161. DOI: 10.1093/bioinformatics/bty916. View

10.

Ghanbari M, Lasserre J, Vingron M . The Distance Precision Matrix: computing networks from non-linear relationships. Bioinformatics. 2018; 35(6):1009-1017. PMC: 6420154. DOI: 10.1093/bioinformatics/bty724. View

11.

Tang W, Bertaux F, Thomas P, Stefanelli C, Saint M, Marguerat S . bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics. 2019; 36(4):1174-1181. PMC: 7703772. DOI: 10.1093/bioinformatics/btz726. View

12.

Chan T, Stumpf M, Babtie A . Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures. Cell Syst. 2017; 5(3):251-267.e3. PMC: 5624513. DOI: 10.1016/j.cels.2017.08.014. View

13.

Shalek A, Satija R, Shuga J, Trombetta J, Gennert D, Lu D . Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014; 510(7505):363-9. PMC: 4193940. DOI: 10.1038/nature13437. View

14.

Eraslan G, Simon L, Mircea M, Mueller N, Theis F . Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019; 10(1):390. PMC: 6344535. DOI: 10.1038/s41467-018-07931-2. View

15.

Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I . Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun. 2018; 9(1):619. PMC: 5809388. DOI: 10.1038/s41467-018-02866-0. View

16.

Chen S, Mar J . Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics. 2018; 19(1):232. PMC: 6006753. DOI: 10.1186/s12859-018-2217-z. View

17.

Chen M, Zhou X . VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biol. 2018; 19(1):196. PMC: 6233584. DOI: 10.1186/s13059-018-1575-1. View

18.

Cole M, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E . Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. Cell Syst. 2019; 8(4):315-328.e8. PMC: 6544759. DOI: 10.1016/j.cels.2019.03.010. View

19.

Kim S . ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Commun Stat Appl Methods. 2015; 22(6):665-674. PMC: 4681537. DOI: 10.5351/CSAM.2015.22.6.665. View

20.

Camp J, Sekine K, Gerber T, Loeffler-Wirth H, Binder H, Gac M . Multilineage communication regulates human liver bud development from pluripotency. Nature. 2017; 546(7659):533-538. DOI: 10.1038/nature22796. View