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Liu W, Teng Z, Li Z, Chen J. CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data. Interdiscip Sci 2024:10.1007/s12539-024-00633-y. [PMID: 38778003 DOI: 10.1007/s12539-024-00633-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 05/25/2024]
Abstract
Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.
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Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
| | - Zhijie Teng
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 412002, China
| | - Jing Chen
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
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2
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Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
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3
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Kamimoto K, Stringa B, Hoffmann CM, Jindal K, Solnica-Krezel L, Morris SA. Dissecting cell identity via network inference and in silico gene perturbation. Nature 2023; 614:742-751. [PMID: 36755098 PMCID: PMC9946838 DOI: 10.1038/s41586-022-05688-9] [Citation(s) in RCA: 144] [Impact Index Per Article: 144.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 12/28/2022] [Indexed: 02/10/2023]
Abstract
Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.
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Affiliation(s)
- Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Blerta Stringa
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Christy M Hoffmann
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Lilianna Solnica-Krezel
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine in St Louis, St Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA.
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Zainal-Abidin RA, Harun S, Vengatharajuloo V, Tamizi AA, Samsulrizal NH. Gene Co-Expression Network Tools and Databases for Crop Improvement. PLANTS (BASEL, SWITZERLAND) 2022; 11:1625. [PMID: 35807577 PMCID: PMC9269215 DOI: 10.3390/plants11131625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/05/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement.
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Affiliation(s)
- Rabiatul-Adawiah Zainal-Abidin
- Biotechnology and Nanotechnology Research Centre, Malaysian Agricultural Research and Development Institute (MARDI), Serdang 43400, Selangor, Malaysia; (R.-A.Z.-A.); (A.-A.T.)
| | - Sarahani Harun
- Centre for Bioinformatics Research, Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia;
| | - Vinothienii Vengatharajuloo
- Centre for Bioinformatics Research, Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia;
| | - Amin-Asyraf Tamizi
- Biotechnology and Nanotechnology Research Centre, Malaysian Agricultural Research and Development Institute (MARDI), Serdang 43400, Selangor, Malaysia; (R.-A.Z.-A.); (A.-A.T.)
- Department of Plant Science, Kulliyyah of Science, International Islamic Universiti Malaysia (IIUM), Jalan Sultan Ahmad Shah, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia
| | - Nurul Hidayah Samsulrizal
- Department of Plant Science, Kulliyyah of Science, International Islamic Universiti Malaysia (IIUM), Jalan Sultan Ahmad Shah, Bandar Indera Mahkota, Kuantan 25200, Pahang, Malaysia
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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:bbaa190. [PMID: 34020546 PMCID: PMC8138892 DOI: 10.1093/bib/bbaa190] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/19/2020] [Accepted: 07/24/2020] [Indexed: 12/13/2022] Open
Abstract
Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bang Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bahadir Pehlivan
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
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Zhang Y, Chang X, Liu X. Inference of gene regulatory networks using pseudo-time series data. Bioinformatics 2021; 37:2423-2431. [PMID: 33576787 DOI: 10.1093/bioinformatics/btab099] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/18/2021] [Accepted: 02/10/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific data set. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. RESULTS Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. AVAILABILITY The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuelei Zhang
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
| | - Xiao Chang
- Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China
| | - Xiaoping Liu
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310012, China.,School of Mathematics and Statistics, Shandong University, Weihai, Shandong, 264209, China
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Xie J, Yin Y, Yang F, Sun J, Wang J. Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision. Interdiscip Sci 2021; 13:91-102. [PMID: 33439459 DOI: 10.1007/s12539-020-00415-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 11/30/2022]
Abstract
Deciphering regulatory patterns of neural stem cell (NSC) differentiation with multiple stages is essential to understand NSC differentiation mechanisms. Recent single-cell transcriptome datasets became available at individual differentiation. However, a systematic and integrative analysis of multiple datasets at multiple temporal stages of NSC differentiation is lacking. In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is essential to all differentiation paths, but it plays a different role in each path when regulating other genes. Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths.
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Affiliation(s)
- Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yiting Yin
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fuzhang Yang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiamin Sun
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiao Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
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8
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Hie B, Peters J, Nyquist SK, Shalek AK, Berger B, Bryson BD. Computational Methods for Single-Cell RNA Sequencing. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-012220-100601] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.
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Affiliation(s)
- Brian Hie
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Joshua Peters
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
| | - Sarah K. Nyquist
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Alex K. Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
- Department of Chemistry, Institute for Medical Engineering & Science (IMES), and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bryan D. Bryson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
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9
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Kontio JAJ, Rinta-Aho MJ, Sillanpää MJ. Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection. Genetics 2020; 215:597-607. [PMID: 32414870 PMCID: PMC7337083 DOI: 10.1534/genetics.120.303186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 05/11/2020] [Indexed: 11/18/2022] Open
Abstract
Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods.
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Affiliation(s)
- Juho A J Kontio
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
| | - Marko J Rinta-Aho
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, Biocenter Oulu, University of Oulu, 90014, Finland
- Infotech Oulu, University of Oulu, 90014, Finland
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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; 17:246-254. [PMID: 29342231 PMCID: PMC6063279 DOI: 10.1093/bfgp/elx046] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
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Affiliation(s)
- Mark W E J Fiers
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
| | - Liesbeth Minnoye
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Sara Aibar
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Zeynep Kalender Atak
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
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