Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks


Цитировать

Полный текст

Аннотация

Background:Time-course single-cell RNA sequencing (scRNA-seq) data represent dynamic gene expression values that change over time, which can be used to infer causal relationships between genes and construct dynamic gene regulatory networks (GRNs). However, most of the existing methods are designed for bulk RNA sequencing (bulk RNA-seq) data and static scRNA-seq data, and only a few methods, such as CNNC and DeepDRIM can be directly applied to time-course scRNA-seq data.

Objective:This work aims to infer causal relationships between genes and construct dynamic gene regulatory networks using time-course scRNA-seq data.

Methods:We propose an analytical method for inferring GRNs from single-cell time-course data based on temporal convolutional networks (scTGRN), which provides a supervised learning approach to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional network to train and infer the causal relationship between genes.

Results:We validate the performance of scTGRN on five real datasets and four simulated datasets, and the experimental results show that scTGRN outperforms existing models in constructing GRNs. In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms other models.

Conclusion:The analysis shows that scTGRN can not only accurately identify the causal relationship between genes, but also can be used to achieve gene function assignment.

Об авторах

Dayu Tan

Institutes of Physical Science and Information Technology, Anhui University

Email: info@benthamscience.net

Jing Wang

School of Computer Science and Technology, Anhui University

Email: info@benthamscience.net

Zhaolong Cheng

School of Computer Science and Technology, Anhui University

Email: info@benthamscience.net

Yansen Su

Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University

Email: info@benthamscience.net

Chunhou Zheng

Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University

Автор, ответственный за переписку.
Email: info@benthamscience.net

Список литературы

  1. 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. doi: 10.1093/bib/bbaa190 PMID: 34020546
  2. Fiers MWEJ, Minnoye L, Aibar S, Bravo González-Blas C, Kalender Atak Z, Aerts S. Mapping gene regulatory networks from single-cell omics data. Brief Funct Genomics 2018; 17(4): 246-54. doi: 10.1093/bfgp/elx046 PMID: 29342231
  3. Xu Y, Chen J, Lyu A, Cheung WK, Zhang L. dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data. Brief Bioinform 2022; 23(6): bbac424. doi: 10.1093/bib/bbac424 PMID: 36168811
  4. Zhao M, He W, Tang J, Zou Q, Guo F. A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. Brief Bioinform 2022; 23(2): bbab568. doi: 10.1093/bib/bbab568 PMID: 35062026
  5. Jeannette C, Hieu T, Julian S, Gulrez C. Towards spatio-temporally resolved developmental cardiac gene regulatory networks in zebrafish. Brief Funct Genomics 2021; 20(6): 427-33.
  6. Huynh-Thu VA, Geurts P. dynGENIE3: Dynamical GENIE3 for the inference of gene networks from time series expression data. Sci Rep 2018; 8(1): 3384. doi: 10.1038/s41598-018-21715-0 PMID: 29467401
  7. Wang J, Ma A, Ma Q, Xu D, Joshi T. Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks. Comput Struct Biotechnol J 2020; 18: 3335-43. doi: 10.1016/j.csbj.2020.10.022 PMID: 33294129
  8. Cliff A, Romero J, Kainer D, Walker A, Furches A, Jacobson D. A high-performance computing implementation of iterative random forest for the creation of predictive expression networks. Genes 2019; 10(12): 996. doi: 10.3390/genes10120996 PMID: 31810264
  9. Chen J, Cheong C, Lan L, et al. DeepDRIM: A deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data. Brief Bioinform 2021; 22(6): bbab325. doi: 10.1093/bib/bbab325 PMID: 34424948
  10. Siebert S, Farrell JA, Cazet JF, et al. Stem cell differentiation trajectories in Hydra resolved at single-cell resolution. Science 2019; 365(6451): eaav9314. doi: 10.1126/science.aav9314 PMID: 31346039
  11. Yuan Y, Bar-Joseph Z. Deep learning of gene relationships from single cell time-course expression data. Brief Bioinform 2021; 22(5): bbab142. doi: 10.1093/bib/bbab142 PMID: 33876191
  12. Zhang Y, Chang X, Liu X. Inference of gene regulatory networks using pseudo-time series data. Bioinformatics 2021; 37(16): 2423-31. doi: 10.1093/bioinformatics/btab099 PMID: 33576787
  13. 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. doi: 10.1093/bioinformatics/btx194 PMID: 28379368
  14. Yuan Y, Bar-Joseph Z. Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci 2019; 116(52): 27151-8. doi: 10.1073/pnas.1911536116 PMID: 31822622
  15. Semrau S, Goldmann JE, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun 2017; 8(1): 1096. doi: 10.1038/s41467-017-01076-4 PMID: 29061959
  16. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015; 161(5): 1187-201. doi: 10.1016/j.cell.2015.04.044 PMID: 26000487
  17. Petropoulos S, Edsgärd D, Reinius B, et al. Single-cell rna-seq reveals lineage and x chromosome dynamics in human preimplantation embryos. Cell 2016; 165(4): 1012-26. doi: 10.1016/j.cell.2016.03.023 PMID: 27062923
  18. Chu LF, Leng N, Zhang J, et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol 2016; 17(1): 173. doi: 10.1186/s13059-016-1033-x
  19. Zhang Y, Liu T, Meyer CA, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008; 9(9): R137. doi: 10.1186/gb-2008-9-9-r137 PMID: 18798982
  20. Ernst J, Plasterer HL, Simon I, Bar-Joseph Z. Integrating multiple evidence sources to predict transcription factor binding in the human genome. Genome Res 2010; 20(4): 526-36. doi: 10.1101/gr.096305.109 PMID: 20219943
  21. Cannoodt R, Saelens W, Deconinck L, Saeys Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat Commun 2021; 12(1): 3942. doi: 10.1038/s41467-021-24152-2 PMID: 34168133
  22. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 2019; 20(1): 296. doi: 10.1186/s13059-019-1874-1 PMID: 31870423
  23. Fan Y, Ma X. Gene regulatory network inference using 3d convolutional neural network. Proc Conf AAAI Artif Intell 2021; 35(1): 99-106. doi: 10.1609/aaai.v35i1.16082
  24. Bai S, Kolter JZ. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv 2018; 180301271.
  25. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016; 770-8. doi: 10.1109/CVPR.2016.90
  26. 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. doi: 10.1093/bioinformatics/btx575 PMID: 28968704
  27. Mohamed Salleh FH, Arif SM, Zainudin S, Firdaus-Raih M. Reconstructing gene regulatory networks from knock-out data using gaussian noise model and pearson correlation coefficient. Comput Biol Chem 2015; 59(Pt B): 3-14. doi: 10.1016/j.compbiolchem.2015.04.012 PMID: 26278974
  28. Song L, Langfelder P, Horvath S. Comparison of co-expression measures: Mutual information, correlation, and model based indices. BMC Bioinformatics 2012; 13(1): 328. doi: 10.1186/1471-2105-13-328 PMID: 23217028
  29. Alexander Wolf F, Philipp A, Fabian J. Scanpy: Large-scale single-cell gene expression data analysis. Genome Biol 2018; 19: 1-5.
  30. Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun 2019; 10(1): 390. doi: 10.1038/s41467-018-07931-2 PMID: 30674886
  31. Deshpande A, Chu LF, Stewart R, Gitter A. Network inference with Granger causality ensembles on single-cell transcriptomics. Cell Rep 2022; 38(6): 110333. doi: 10.1016/j.celrep.2022.110333 PMID: 35139376
  32. Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: Tool for the unification of biology. Nat Genet 2000; 25(1): 25-9. doi: 10.1038/75556 PMID: 10802651
  33. van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19(4): 575-92. PMID: 28077403
  34. Ruan Y, Li Y, Liu Y, Zhou J, Wang X, Zhang W. Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm. Exp Ther Med 2019; 17(5): 4139-43. doi: 10.3892/etm.2019.7410 PMID: 30988790
  35. Hastie T, Tibshirani R. Discriminant adaptive nearest neighbor classification and regression. Adv Neural Inf Process Syst 1995; 8.
  36. Luscombe NM, Madan Babu M, Yu H, Snyder M, Teichmann SA, Gerstein M. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 2004; 431(7006): 308-12. doi: 10.1038/nature02782 PMID: 15372033
  37. Kouno T, de Hoon M, Mar JC, et al. Temporal dynamics and transcriptional control using single-cell gene expression analysis. Genome Biol 2013; 14(10): R118. doi: 10.1186/gb-2013-14-10-r118 PMID: 24156252
  38. Ahmed A, Xing EP. Recovering time-varying networks of dependencies in social and biological studies. Proc Natl Acad Sci USA 2009; 106(29): 11878-83. doi: 10.1073/pnas.0901910106 PMID: 19570995
  39. Kim HJ, Osteil P, Humphrey SJ, et al. Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning. Nucleic Acids Res 2020; 48(4): 1828-42. doi: 10.1093/nar/gkz1179 PMID: 31853542
  40. Yang B, Bao W, Chen B. PGRNIG: novel parallel gene regulatory network identification algorithm based on GPU. Brief Funct Genomics 2022; 21(6): 441-54. doi: 10.1093/bfgp/elac028 PMID: 36064791

Дополнительные файлы

Доп. файлы
Действие
1. JATS XML
Скачать

© Bentham Science Publishers, 2024