Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Perspective

Integrated Pipelines for Inferring Gene Regulatory Networks from Single-Cell Data

Author(s): Aimin Chen, Tianshou Zhou and Tianhai Tian*

Volume 17, Issue 7, 2022

Published on: 01 August, 2022

Page: [559 - 564] Pages: 6

DOI: 10.2174/1574893617666220511234247

Abstract

Background: Single-cell technologies provide unprecedented opportunities to study heterogeneity of molecular mechanisms. In particular, single-cell RNA-sequence data have been successfully used to infer gene regulatory networks with stochastic expressions. However, there are still substantial challenges in measuring the relationships between genes and selecting the important genetic regulations.

Objective: This prospective provides a brief review of effective methods for the inference of gene regulatory networks.

Methods: We concentrate on two types of inference methods, namely the model-free methods and mechanistic methods for constructing gene networks.

Results: For the model-free methods, we mainly discuss two issues, namely the measures for quantifying gene relationship and criteria for selecting significant connections between genes. The issue for mechanistic methods is different mathematical models to describe genetic regulations accurately.

Conclusions: We advocate the development of ensemble methods that combine two or more methods together.

Keywords: Single-cell, network inference, statistical inference, mechanistic models, integrated inference, GRN.

Next »
[1]
Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet 2019; 20(5): 257-72.
[http://dx.doi.org/10.1038/s41576-019-0093-7] [PMID: 30696980]
[2]
Lähnemann D, Köster J, Szczurek E, et al. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21(1): 31.
[http://dx.doi.org/10.1186/s13059-020-1926-6] [PMID: 32033589]
[3]
De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol 2010; 8(10): 717-29.
[http://dx.doi.org/10.1038/nrmicro2419] [PMID: 20805835]
[4]
Marbach D, Costello JC, Küffner R, et al. Wisdom of crowds for robust gene network inference. Nat Methods 2012; 9(8): 796-804.
[http://dx.doi.org/10.1038/nmeth.2016] [PMID: 22796662]
[5]
Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA. Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform 2014; 15(2): 195-211.
[http://dx.doi.org/10.1093/bib/bbt034] [PMID: 23698722]
[6]
Zhao M, He W, Tang J, Zou Q, Guo F. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Brief Bioinform 2021; 22(5): bbab009.
[http://dx.doi.org/10.1093/bib/bbab009]
[7]
Chen S, Mar JC. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics 2018; 19(1): 232.
[http://dx.doi.org/10.1186/s12859-018-2217-z] [PMID: 29914350]
[8]
Stumpf MPH. Inferring better gene regulation networks from single-cell data. Curr Opin Syst Biol 2021; 27: 100342.
[http://dx.doi.org/10.1016/j.coisb.2021.05.003]
[9]
Hu X, Hu Y, Wu F, Leung RWT, Qin J. Integration of single-cell multi-omics for gene regulatory network inference. Comput Struct Biotechnol J 2020; 18: 1925-38.
[http://dx.doi.org/10.1016/j.csbj.2020.06.033] [PMID: 32774787]
[10]
Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. Biochim Biophys Acta Gene Regul Mech 2020; 1863(6): 194430.
[http://dx.doi.org/10.1016/j.bbagrm.2019.194430] [PMID: 31678629]
[11]
Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat Methods 2020; 17(2): 147-54.
[http://dx.doi.org/10.1038/s41592-019-0690-6] [PMID: 31907445]
[12]
Nguyen H, Tran D, Tran B, Pehlivan B, Nguyen T. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief Bioinform 2021; 22(3): bbaa190.
[13]
Skinnider MA, Squair JW, Foster LJ. Evaluating measures of association for single-cell transcriptomics. Nat Methods 2019; 16(5): 381-6.
[http://dx.doi.org/10.1038/s41592-019-0372-4] [PMID: 30962620]
[14]
Liu ZP. Quantifying gene regulatory relationships with association measures: A comparative study. Front Genet 2017; 8: 96.
[http://dx.doi.org/10.3389/fgene.2017.00096] [PMID: 28751908]
[15]
Quinn TP, Richardson MF, Lovell D, Crowley TM. propr: An R-package for identifying proportionally abundant features using compositional data analysis. Sci Rep 2017; 7(1): 16252.
[http://dx.doi.org/10.1038/s41598-017-16520-0] [PMID: 29176663]
[16]
Wang J, Cheung LW, Delabie J. New probabilistic graphical models for genetic regulatory networks studies. J Biomed Inform 2005; 38(6): 443-55.
[http://dx.doi.org/10.1016/j.jbi.2005.04.003] [PMID: 15996532]
[17]
Zhang X, Zhao XM, He K, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 2012; 28(1): 98-104.
[http://dx.doi.org/10.1093/bioinformatics/btr626] [PMID: 22088843]
[18]
Chan TE, Stumpf MPH, Babtie AC. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst 2017; 5(3): 251-67.
[http://dx.doi.org/10.1016/j.cels.2017.08.014] [PMID: 28957658]
[19]
Papili Gao N, Ud-Dean SMM, Gandrillon O, Gunawan R. SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics 2018; 34(2): 258-66.
[http://dx.doi.org/10.1093/bioinformatics/btx575] [PMID: 28968704]
[20]
Cordero P, Stuart JM. Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories. Pac Symp Biocomput 2017; 22: 576-87.
[http://dx.doi.org/10.1142/9789813207813_0053] [PMID: 27897008]
[21]
Thorne T, Stumpf MPH. Inference of temporally varying Bayesian networks. Bioinformatics 2012; 28(24): 3298-305.
[http://dx.doi.org/10.1093/bioinformatics/bts614] [PMID: 23074260]
[22]
Deshpande A, Chu LF, Stewart R, et al. Network inference with granger causality ensembles on single-cell transcriptomic data. bioRxiv 2019; 2019; 534834.
[http://dx.doi.org/10.1101/534834]
[23]
Lu J, Dumitrascu B, McDowell IC, et al. Causal network inference from gene transcriptional time-series response to glucocorticoids. PLOS Comput Biol 2021; 17(1): e1008223.
[http://dx.doi.org/10.1371/journal.pcbi.1008223] [PMID: 33513136]
[24]
Qiu X, Rahimzamani A, Wang L, et al. Inferring causal gene regulatory networks from coupled single-cell expression dynamics using scribe. Cell Syst 2020; 10(3): 265-74.
[http://dx.doi.org/10.1016/j.cels.2020.02.003] [PMID: 32135093]
[25]
Specht AT, Li J. LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics 2017; 33(5): 764-6.
[PMID: 27993778]
[26]
Matsumoto H, Kiryu H, Furusawa C, et al. SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 2017; 33(15): 2314-21.
[http://dx.doi.org/10.1093/bioinformatics/btx194] [PMID: 28379368]
[27]
Aubin-Frankowski PC, Vert JP. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Boinformatics 2020; 36(18): 4774-80.
[http://dx.doi.org/10.1093/bioinformatics/btaa576] [PMID: 33026066]
[28]
Qin J, Hu Y, Xu F, Yalamanchili HK, Wang J. Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. Methods 2014; 67(3): 294-303.
[http://dx.doi.org/10.1016/j.ymeth.2014.03.006] [PMID: 24650566]
[29]
Omranian N, Eloundou-Mbebi JM, Mueller-Roeber B, Nikoloski Z. Gene regulatory network inference using fused LASSO on multiple data sets. Sci Rep 2016; 6: 20533.
[http://dx.doi.org/10.1038/srep20533]
[30]
Shea MA, Ackers GK. The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation. J Mol Biol 1985; 181(2): 211-30.
[http://dx.doi.org/10.1016/0022-2836(85)90086-5] [PMID: 3157005]
[31]
Wei J, Hu X, Zou X, Tian T. Reverse-engineering of gene networks for regulating early blood development from single-cell measurements. BMC Med Genomics 2017; 10 (Suppl. 5): 72.
[http://dx.doi.org/10.1186/s12920-017-0312-z] [PMID: 29297370]
[32]
Warne DJ, Baker RE, Simpson MJ. Simulation and inference algorithms for stochastic biochemical reaction networks: From basic concepts to state-of-the-art. J R Soc Interface 2019; 16(151): 20180943.
[http://dx.doi.org/10.1098/rsif.2018.0943] [PMID: 30958205]
[33]
Haury AC, Mordelet F, Vera-Licona P, Vert JP. TIGRESS: Trustful inference of gene regulation using stability selection. BMC Syst Biol 2012; 6(1): 145.
[http://dx.doi.org/10.1186/1752-0509-6-145] [PMID: 23173819]
[34]
Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci USA 2016; 113(15): 3932-7.
[http://dx.doi.org/10.1073/pnas.1517384113] [PMID: 27035946]
[35]
Matsumoto H, Kiryu H. SCOUP: A probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation. BMC Bioinformatics 2016; 17(1): 232.
[http://dx.doi.org/10.1186/s12859-016-1109-3] [PMID: 27277014]
[36]
Bonnaffoux A, Herbach U, Richard A, et al. WASABI: A dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 2019; 20(1): 220.
[http://dx.doi.org/10.1186/s12859-019-2798-1] [PMID: 31046682]
[37]
Wei J, Zhou T, Zhang X, Tian T. DTFLOW: Inference and visualization of single-cell pseudotime trajectory using diffusion propagation. Genomics Proteomics Bioinformatics 2021; 19(2): 306-18.
[http://dx.doi.org/10.1016/j.gpb.2020.08.003] [PMID: 33662626]
[38]
Ocone A, Haghverdi L, Mueller NS, Theis FJ. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 2015; 31(12): i89-96.
[http://dx.doi.org/10.1093/bioinformatics/btv257] [PMID: 26072513]
[39]
Kitano H. Towards a theory of biological robustness. Mol Syst Biol 2007; 3(1): 137.
[http://dx.doi.org/10.1038/msb4100179] [PMID: 17882156]
[40]
Wang J, Wu Q, Hu XT, Tian T. An integrated platform for reverse-engineering protein-gene interaction network. Methods 2016; 110: 3-13.
[http://dx.doi.org/10.1016/j.ymeth.2016.08.001] [PMID: 27514497]
[41]
Wu S, Cui T, Zhang X, Tian T. A non-linear reverse-engineering method for inferring genetic regulatory networks. PeerJ 2020; 8: e9065.
[http://dx.doi.org/10.7717/peerj.9065] [PMID: 32391205]
[42]
Sanchez-Castillo M, Blanco D, Tienda-Luna IM, Carrion MC, Huang Y. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics 2018; 34(6): 964-70.
[http://dx.doi.org/10.1093/bioinformatics/btx605] [PMID: 29028984]
[43]
Moignard V, Woodhouse S, Haghverdi L, et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 2015; 33(3): 269-76.
[http://dx.doi.org/10.1038/nbt.3154] [PMID: 25664528]
[44]
Shu H, Zhou J, Lian Q, et al. Modeling gene regulatory networks using neural network architectures. Nat Comput Sci 2021; 1(7): 491-501.
[http://dx.doi.org/10.1038/s43588-021-00099-8]
[45]
Yuan Y, Bar-Joseph Z. Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci USA 2019; 116(52): 27151-8.
[http://dx.doi.org/10.1073/pnas.1911536116] [PMID: 31822622]

Rights & Permissions Print Cite
Article Metrics
31
3
© 2024 Bentham Science Publishers | Privacy Policy