Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

Hao Dai; Qi-Qi Jin; Lin Li; Luo-Nan Chen

doi:10.24272/j.issn.2095-8137.2020.215

Volume 41 Issue 6

Nov. 2020

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Hao Dai, Qi-Qi Jin, Lin Li, Luo-Nan Chen. Reconstructing gene regulatory networks in single-cell transcriptomic data analysis. Zoological Research, 2020, 41(6): 599-604. doi: 10.24272/j.issn.2095-8137.2020.215

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Hao Dai, Qi-Qi Jin, Lin Li, Luo-Nan Chen. Reconstructing gene regulatory networks in single-cell transcriptomic data analysis. Zoological Research, 2020, 41(6): 599-604. doi: 10.24272/j.issn.2095-8137.2020.215

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Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

doi: 10.24272/j.issn.2095-8137.2020.215

Hao Dai^{1, 2},
Qi-Qi Jin^{1, 3, 4},
Lin Li^{1, 3},
Luo-Nan Chen^{1, 4, 5, 6
,
,}

1.
Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
2.
Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
5.
Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
6.
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China

Funds: This study was supported by the National Key Research and Development Program of China (2017YFA0505500), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38040400), National Science Foundation of China (31771476 and 31930022), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)

More Information

Corresponding author: E-mail: lnchen@sibs.ac.cn
Received Date: 2020-08-03
Accepted Date: 2020-10-20

Published Online: 2020-10-21

Publish Date: 2020-11-18

Abstract

Abstract

Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level. Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples. Here, we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.
- Gene regulatory network,
- Single-cell RNA sequencing,
- Computational algorithm,
- Sample-specific network,
- Cell-type-specific network,
- Cell-specific network

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References(47)

References

[1]	Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. 2017. SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11): 1083−1086. doi: 10.1038/nmeth.4463
[2]	Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5): 411−420. doi: 10.1038/nbt.4096
[3]	Chan TE, Stumpf MPH, Babtie AC. 2017. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Systems, 5(3): 251−267.e3. doi: 10.1016/j.cels.2017.08.014
[4]	Chen LN, Wang RQ, Li CG, Aihara K. 2010. Modeling Biomolecular Networks in Cells: Structures and Dynamics. London: Springer-Verlag.
[5]	Chen LN, Wang RS, Zhang XS. 2009. Biomolecular Networks: Methods and Applications in Systems Biology. New York: John Wiley & Sons.
[6]	Chen SN, Mar JC. 2018. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics, 19(1): 232. doi: 10.1186/s12859-018-2217-z
[7]	Dai H, Li L, Zeng T, Chen LN. 2019. Cell-specific network constructed by single-cell RNA sequencing data. Nucleic Acids Research, 47(11): e62. doi: 10.1093/nar/gkz172
[8]	Eberwine J, Sul JY, Bartfai T, Kim J. 2014. The promise of single-cell sequencing. Nature Methods, 11(1): 25−27. doi: 10.1038/nmeth.2769
[9]	Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ. 2020. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Genome Research, 30(2): 195−204. doi: 10.1101/gr.251603.119
[10]	Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. 2007. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology, 5(1): e8. doi: 10.1371/journal.pbio.0050008
[11]	Gao NP, Ud-Dean SMM, Gandrillon O, Gunawan R. 2018. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics, 34(2): 258−266. doi: 10.1093/bioinformatics/btx575
[12]	Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G. 2008. Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics, 24(17): 1917−1925. doi: 10.1093/bioinformatics/btn336
[13]	Harly C, Kenney D, Ren G, Lai BB, Raabe T, Yang Q, et al. 2019. The transcription factor TCF-1 enforces commitment to the innate lymphoid cell lineage. Nature Immunology, 20(9): 1150−1160. doi: 10.1038/s41590-019-0445-7
[14]	Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. 2010. Inferring regulatory networks from expression data using tree-based methods. PLoS One, 5(9): e12776. doi: 10.1371/journal.pone.0012776
[15]	Huynh-Thu VA, Sanguinetti G. 2015. Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinformatics, 31(10): 1614−1622. doi: 10.1093/bioinformatics/btu863
[16]	Iacono G, Massoni-Badosa R, Heyn H. 2019. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biology, 20(1): 110. doi: 10.1186/s13059-019-1713-4
[17]	Kim S. 2015. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Communications for Statistical Applications and Methods, 22(6): 665−674. doi: 10.5351/CSAM.2015.22.6.665
[18]	Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9(1): 559. doi: 10.1186/1471-2105-9-559
[19]	Li L, Dai H, Fang ZY, Chen LN. 2020. CCSN: single Cell RNA sequencing data analysis by conditional cell-specific network. bioRxiv. doi: 10.1101/2020.01.25.919829.
[20]	Li XY, Chen WZ, Chen Y, Zhang XG, Gu J, Zhang MQ. 2017. Network embedding-based representation learning for single cell RNA-seq data. Nucleic Acids Research, 45(19): e166. doi: 10.1093/nar/gkx750
[21]	Lim CY, Wang HG, Woodhouse S, Piterman N, Wernisch L, Fisher J, et al. 2016. BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics, 17(1): 355. doi: 10.1186/s12859-016-1235-y
[22]	Liu JL, Liu S, Gao HY, Han L, Chu XN, Sheng Y, et al. 2020. Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells. Genome Biology, 21(1): 169. doi: 10.1186/s13059-020-02082-4
[23]	Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH, Gouda N, et al. 2017. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics, 33(15): 2314−2321. doi: 10.1093/bioinformatics/btx194
[24]	Mimitou EP, Cheng A, Montalbano A, Hao S, Stoeckius M, Legut M, et al. 2019. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nature Methods, 16(5): 409−412. doi: 10.1038/s41592-019-0392-0
[25]	Moerman T, Santos SA, González-Blas CB, Simm J, Moreau Y, Aerts J, et al. 2019. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics, 35(12): 2159−2161. doi: 10.1093/bioinformatics/bty916
[26]	Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, et al. 2015. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nature Biotechnology, 33(3): 269−276. doi: 10.1038/nbt.3154
[27]	Pina C, Teles J, Fugazza C, May G, Wang DP, Guo YP, et al. 2015. Single-cell network analysis identifies DDIT3 as a nodal lineage regulator in hematopoiesis. Cell Reports, 11(10): 1503−1510. doi: 10.1016/j.celrep.2015.05.016
[28]	Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM. 2020. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods, 17(2): 147−154. doi: 10.1038/s41592-019-0690-6
[29]	Qiu XJ, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, et al. 2017. Reversed graph embedding resolves complex single-cell trajectories. Nature Methods, 14(10): 979−982. doi: 10.1038/nmeth.4402
[30]	Rozenblatt-Rosen O, Stubbington MJT, Regev A, Teichmann SA. 2017. The human cell atlas: from vision to reality. Nature, 550(7677): 451−453. doi: 10.1038/550451a
[31]	Sagar, Pokrovskii M, Herman JS, Naik S, Sock E, Zeis P, et al. 2020. Deciphering the regulatory landscape of fetal and adult γδ T-cell development at single-cell resolution. The EMBO Journal, 39(13): e104159.
[32]	Sanchez-Castillo M, Blanco D, Tienda-Luna IM, Carrion MC, Huang YF. 2018. A bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics, 34(6): 964−970. doi: 10.1093/bioinformatics/btx605
[33]	Specht AT, Li J. 2017. LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics, 33(5): 764−766.
[34]	Stegle O, Teichmann SA, Marioni JC. 2015. Computational and analytical challenges in single-cell transcriptomics. Nature Reviews Genetics, 16(3): 133−145. doi: 10.1038/nrg3833
[35]	Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck III WM, et al. 2019. Comprehensive integration of single-cell data. Cell, 177(7): 1888−1902.e21. doi: 10.1016/j.cell.2019.05.031
[36]	Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, et al. 2012. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology, 6(1): 133. doi: 10.1186/1752-0509-6-133
[37]	Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li SQ, Morse M, et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4): 381−386. doi: 10.1038/nbt.2859
[38]	Van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, et al. 2018. Recovering gene interactions from single-cell data using data diffusion. Cell, 174(3): 716−729.e27. doi: 10.1016/j.cell.2018.05.061
[39]	Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, et al. 2017. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science, 356(6335): eaah4573. doi: 10.1126/science.aah4573
[40]	Wang Y, Joshi T, Zhang XS, Xu D, Chen L. 2006. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics, 22(19): 2413−2420. doi: 10.1093/bioinformatics/btl396
[41]	Woodhouse S, Piterman N, Wintersteiger CM, Göttgens B, Fisher J. 2018. SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data. BMC Systems Biology, 12(1): 59. doi: 10.1186/s12918-018-0581-y
[42]	Xu HL, Ang YS, Sevilla A, Lemischka IR, Ma'ayan A. 2014. Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLoS Computational Biology, 10(8): e1003777. doi: 10.1371/journal.pcbi.1003777
[43]	Yuan Y, Bar-Joseph Z. 2019. Deep learning for inferring gene relationships from single-cell expression data. Proceedings of the National Academy of Sciences of the United States of America, 116(52): 27151−27158. doi: 10.1073/pnas.1911536116
[44]	Zeng T, Dai H. 2019. Single-cell RNA sequencing-based computational analysis to describe disease heterogeneity. Frontiers in Genetics, 10: 629. doi: 10.3389/fgene.2019.00629
[45]	Zhang S, Zhao J, Lv XD, Fan JL, Lu Y, Zeng T, et al. 2020. Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila. Cell Discovery, 6(1): 43. doi: 10.1038/s41421-020-0173-z
[46]	Zhang XJ, Zhao J, Hao JK, Zhao XM, Chen LN. 2015. Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Research, 43(5): e31. doi: 10.1093/nar/gku1315
[47]	Zhao J, Zhou YW, Zhang XJ, Chen LN. 2016. Part mutual information for quantifying direct associations in networks. Proceedings of the National Academy of Sciences of the United States of America, 113(18): 5130−5135. doi: 10.1073/pnas.1522586113