Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods

Overview

Journal Bioinform Biol Insights

Publisher Sage Publications

Specialty Biology

Date 2024 Nov 6

PMID 39502448

Authors

Karamveer

Yasin Uzun

Affiliations

Soon will be listed here.

Abstract

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

References

Gao N, Ud-Dean S, Gandrillon O, Gunawan R . SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics. 2017; 34(2):258-266. PMC: 5860204. DOI: 10.1093/bioinformatics/btx575. View

Buganim Y, Faddah D, Cheng A, Itskovich E, Markoulaki S, Ganz K . Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell. 2012; 150(6):1209-22. PMC: 3457656. DOI: 10.1016/j.cell.2012.08.023. View

Street K, Risso D, Fletcher R, Das D, Ngai J, Yosef N . Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018; 19(1):477. PMC: 6007078. DOI: 10.1186/s12864-018-4772-0. View

Chen H, Guo J, Mishra S, Robson P, Niranjan M, Zheng J . Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. Bioinformatics. 2014; 31(7):1060-6. DOI: 10.1093/bioinformatics/btu777. View

Haury A, Mordelet F, Vera-Licona P, Vert J . TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012; 6:145. PMC: 3598250. DOI: 10.1186/1752-0509-6-145. View

Zhou Q, Chipperfield H, Melton D, Wong W . A gene regulatory network in mouse embryonic stem cells. Proc Natl Acad Sci U S A. 2007; 104(42):16438-43. PMC: 2034259. DOI: 10.1073/pnas.0701014104. View

Seong H, Han S, Sul W . Prokaryotic DNA methylation and its functional roles. J Microbiol. 2021; 59(3):242-248. DOI: 10.1007/s12275-021-0674-y. View

Aubin-Frankowski P, Vert J . Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics. 2020; 36(18):4774-4780. DOI: 10.1093/bioinformatics/btaa576. View

Xu R, Venayagamoorthy G, Wunsch 2nd D . Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Netw. 2007; 20(8):917-27. DOI: 10.1016/j.neunet.2007.07.002. View

10.

Lim C, Wang H, Woodhouse S, Piterman N, Wernisch L, Fisher J . BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics. 2016; 17(1):355. PMC: 5012073. DOI: 10.1186/s12859-016-1235-y. View

11.

Kang Y, Thieffry D, Cantini L . Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms. Front Genet. 2021; 12:617282. PMC: 8019823. DOI: 10.3389/fgene.2021.617282. View

12.

Matsumoto H, Kiryu H, Furusawa C, Ko M, Ko S, Gouda N . SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics. 2017; 33(15):2314-2321. PMC: 5860123. DOI: 10.1093/bioinformatics/btx194. View

13.

Schmeier S, Alam T, Essack M, Bajic V . TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions. Nucleic Acids Res. 2016; 45(D1):D145-D150. PMC: 5210517. DOI: 10.1093/nar/gkw1007. View

14.

Hamey F, Nestorowa S, Kinston S, Kent D, Wilson N, Gottgens B . Reconstructing blood stem cell regulatory network models from single-cell molecular profiles. Proc Natl Acad Sci U S A. 2017; 114(23):5822-5829. PMC: 5468644. DOI: 10.1073/pnas.1610609114. View

15.

Kim D, Tran A, Kim H, Lin Y, Yang J, Yang P . Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl. 2023; 9(1):51. PMC: 10587078. DOI: 10.1038/s41540-023-00312-6. View

16.

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

17.

Shrivastava H, Zhang X, Song L, Aluru S . GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks. J Comput Biol. 2022; 29(1):27-44. DOI: 10.1089/cmb.2021.0437. View

18.

Dai H, Jin Q, Li L, Chen L . Reconstructing gene regulatory networks in single-cell transcriptomic data analysis. Zool Res. 2020; 41(6):599-604. PMC: 7671911. DOI: 10.24272/j.issn.2095-8137.2020.215. View

19.

Datlinger P, Rendeiro A, Schmidl C, Krausgruber T, Traxler P, Klughammer J . Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods. 2017; 14(3):297-301. PMC: 5334791. DOI: 10.1038/nmeth.4177. View

20.

Zhang Y, Zheng L, Zhang L, Hu X, Ren X, Zhang Z . Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients. Sci Data. 2019; 6(1):131. PMC: 6656756. DOI: 10.1038/s41597-019-0131-5. View