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Pušnik Ž, Mraz M, Zimic N, Moškon M. SAILoR: Structure-Aware Inference of Logic Rules. PLoS One 2024; 19:e0304102. [PMID: 38861487 PMCID: PMC11166287 DOI: 10.1371/journal.pone.0304102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/07/2024] [Indexed: 06/13/2024] Open
Abstract
Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.
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Affiliation(s)
- Žiga Pušnik
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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2
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Wei PJ, Guo Z, Gao Z, Ding Z, Cao RF, Su Y, Zheng CH. Inference of gene regulatory networks based on directed graph convolutional networks. Brief Bioinform 2024; 25:bbae309. [PMID: 38935070 PMCID: PMC11209731 DOI: 10.1093/bib/bbae309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.
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Affiliation(s)
- Pi-Jing Wei
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Ziqiang Guo
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Zhen Gao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Zheng Ding
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Rui-Fen Cao
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Anhui, China
| | - Chun-Hou Zheng
- Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Anhui, China
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3
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Li L, Xia R, Chen W, Zhao Q, Tao P, Chen L. Single-cell causal network inferred by cross-mapping entropy. Brief Bioinform 2023; 24:bbad281. [PMID: 37544659 DOI: 10.1093/bib/bbad281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/03/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
Gene regulatory networks (GRNs) reveal the complex molecular interactions that govern cell state. However, it is challenging for identifying causal relations among genes due to noisy data and molecular nonlinearity. Here, we propose a novel causal criterion, neighbor cross-mapping entropy (NME), for inferring GRNs from both steady data and time-series data. NME is designed to quantify 'continuous causality' or functional dependency from one variable to another based on their function continuity with varying neighbor sizes. NME shows superior performance on benchmark datasets, comparing with existing methods. By applying to scRNA-seq datasets, NME not only reliably inferred GRNs for cell types but also identified cell states. Based on the inferred GRNs and further their activity matrices, NME showed better performance in single-cell clustering and downstream analyses. In summary, based on continuous causality, NME provides a powerful tool in inferring causal regulations of GRNs between genes from scRNA-seq data, which is further exploited to identify novel cell types/states and predict cell type-specific network modules.
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Affiliation(s)
- Lin Li
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Rui Xia
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Zhao
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Tao
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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4
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Lazebnik T, Simon-Keren L. Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures. Comput Biol Med 2023; 164:107221. [PMID: 37478715 DOI: 10.1016/j.compbiomed.2023.107221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 07/23/2023]
Abstract
Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.
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Affiliation(s)
- Teddy Lazebnik
- Department of Cancer Biology, Cancer Institute, University College London, London, UK.
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Nakulugamuwa Gamage H, Chetty M, Lim S, Hallinan J. MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks. PLoS One 2023; 18:e0288174. [PMID: 37418430 DOI: 10.1371/journal.pone.0288174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/21/2023] [Indexed: 07/09/2023] Open
Abstract
In systems biology, the accurate reconstruction of Gene Regulatory Networks (GRNs) is crucial since these networks can facilitate the solving of complex biological problems. Amongst the plethora of methods available for GRN reconstruction, information theory and fuzzy concepts-based methods have abiding popularity. However, most of these methods are not only complex, incurring a high computational burden, but they may also produce a high number of false positives, leading to inaccurate inferred networks. In this paper, we propose a novel hybrid fuzzy GRN inference model called MICFuzzy which involves the aggregation of the effects of Maximal Information Coefficient (MIC). This model has an information theory-based pre-processing stage, the output of which is applied as an input to the novel fuzzy model. In this preprocessing stage, the MIC component filters relevant genes for each target gene to significantly reduce the computational burden of the fuzzy model when selecting the regulatory genes from these filtered gene lists. The novel fuzzy model uses the regulatory effect of the identified activator-repressor gene pairs to determine target gene expression levels. This approach facilitates accurate network inference by generating a high number of true regulatory interactions while significantly reducing false regulatory predictions. The performance of MICFuzzy was evaluated using DREAM3 and DREAM4 challenge data, and the SOS real gene expression dataset. MICFuzzy outperformed the other state-of-the-art methods in terms of F-score, Matthews Correlation Coefficient, Structural Accuracy, and SS_mean, and outperformed most of them in terms of efficiency. MICFuzzy also had improved efficiency compared with the classical fuzzy model since the design of MICFuzzy leads to a reduction in combinatorial computation.
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Affiliation(s)
| | - Madhu Chetty
- Health Innovation and Transformation Centre, Federation University, Churchill, Victoria, Australia
| | - Suryani Lim
- Health Innovation and Transformation Centre, Federation University, Churchill, Victoria, Australia
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6
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Yang L, Lin W, Leng S. Conditional cross-map-based technique: From pairwise dynamical causality to causal network reconstruction. CHAOS (WOODBURY, N.Y.) 2023; 33:2894465. [PMID: 37276551 DOI: 10.1063/5.0144310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/08/2023] [Indexed: 06/07/2023]
Abstract
Causality detection methods based on mutual cross mapping have been fruitfully developed and applied to data originating from nonlinear dynamical systems, where the causes and effects are non-separable. However, these pairwise methods still have shortcomings in discriminating typical network structures, including common drivers, indirect dependencies, and facing the curse of dimensionality, when they are stepping to causal network reconstruction. A few endeavors have been devoted to conquer these shortcomings. Here, we propose a novel method that could be regarded as one of these endeavors. Our method, named conditional cross-map-based technique, can eliminate third-party information and successfully detect direct dynamical causality, where the detection results can exactly be categorized into four standard normal forms by the designed criterion. To demonstrate the practical usefulness of our model-free, data-driven method, data generated from different representative models covering all kinds of network motifs and measured from real-world systems are investigated. Because correct identification of the direct causal links is essential to successful modeling, predicting, and controlling the underlying complex systems, our method does shed light on uncovering the inner working mechanisms of real-world systems only using the data experimentally obtained in a variety of disciplines.
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Affiliation(s)
- Liufei Yang
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- School of Mathematical Sciences and Shanghai Centre for Mathematical Sciences, Fudan University, Shanghai 200433, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Siyang Leng
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
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Shachaf LI, Roberts E, Cahan P, Xiao J. Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM. BMC Bioinformatics 2023; 24:84. [PMID: 36879188 PMCID: PMC9990267 DOI: 10.1186/s12859-022-05047-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 11/08/2022] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. RESULTS In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. CONCLUSIONS Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction-which combines CMIA, and the KSG-MI estimator-achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.
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Affiliation(s)
- Lior I Shachaf
- Department of Biophysics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA.
| | - Elijah Roberts
- Department of Biophysics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA
- 10x Genomics, 6230 Stoneridge Mall Road, Pleasanton, CA, 94588-3260, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Department of Molecular Biology and Genetics, Institute for Cell Engineering, Johns Hopkins School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA
| | - Jie Xiao
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins School of Medicine, 725 N. Wolfe Street, WBSB 708, Baltimore, MD, 21205, USA
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8
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Abolmasoumi AH, Mohammadian M, Mili L. Robust KALMAN Filter State Estimation for Gene Regulatory Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1395-1405. [PMID: 35536813 DOI: 10.1109/tcbb.2022.3173969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This paper proposes a revised version of the robust generalized maximum likelihood (GM)-type unscented KALMAN filter (GM-UKF) for the state estimation of gene regulatory networks (GRNs) in the presence of different types of deviations from assumptions. As known, the parameters and the power of the assumed noises within the GRN model may change abruptly as a result of jump behavior and bursting process in transcription and translation phases. Moreover, there may be outlying samples among genomic measurement data. Some other outliers may also occur in the model dynamics. The outliers may be misinterpreted by the filtering method if not detected and downweighted. To deal with all such deviations, a robust GM-UKF is designed that includes some modifications to address the challenges in calculating the projection statistics in GRNs such as the nonlinear behavior and the natural distance of the states. The proposed filter is compared to four Bayesian filters, i.e., the conventional UKF, the H ∞-UKF, the downweighting UKF (DW-UKF), and a modified version of the GM-UKF, the so-called maximum-likelihood UKF(M-UKF). The outcome results demonstrate that the GM-UKF outperforms other methods for all outlier types while the H ∞-UKF is appropriate for the changes in noise powers.
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Nabuco Leva Ferreira de Freitas JA, Bischof O. Dynamic modeling of the cellular senescence gene regulatory network. Heliyon 2023; 9:e14007. [PMID: 36938415 PMCID: PMC10015196 DOI: 10.1016/j.heliyon.2023.e14007] [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: 07/15/2022] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 02/27/2023] Open
Abstract
Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.
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Affiliation(s)
- José Américo Nabuco Leva Ferreira de Freitas
- IMRB, Mondor Institute for Biomedical Research, INSERM U955 – Université Paris Est Créteil, UPEC, Faculté de Médecine de Créteil 8, rue du Général Sarrail, 94010 Créteil
- Sorbonne Université, UMR 8256, Biological Adaptation and Ageing B2A–IBPS, F-75005, Paris, France
- INSERM U1164, F-75005, Paris, France
| | - Oliver Bischof
- IMRB, Mondor Institute for Biomedical Research, INSERM U955 – Université Paris Est Créteil, UPEC, Faculté de Médecine de Créteil 8, rue du Général Sarrail, 94010 Créteil
- Corresponding author.
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Mohammadian M, Sufi Karimi H. Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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11
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Tao P, Wang Q, Shi J, Hao X, Liu X, Min B, Zhang Y, Li C, Cui H, Chen L. Detecting dynamical causality by intersection cardinal concavity. FUNDAMENTAL RESEARCH 2023. [DOI: 10.1016/j.fmre.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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12
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Liu Q, Li J, Dong M, Liu M, Chai Y. Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:399-409. [PMID: 35061589 DOI: 10.1109/tcbb.2022.3144418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.
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Jia Z, Zhang X. Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes. Front Genet 2022; 13:923339. [PMID: 36568360 PMCID: PMC9768335 DOI: 10.3389/fgene.2022.923339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.
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Affiliation(s)
- Zhigang Jia
- School of Mathematics and Statistics, Xinyang Normal University, Xinyang, China,Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China,*Correspondence: Xiujun Zhang,
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14
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Zanga A, Ozkirimli E, Stella F. A Survey on Causal Discovery: Theory and Practice. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Xuan C, Wang Y, Zhang B, Wu H, Ding T, Gao J. scBPGRN: Integrating single-cell multi-omics data to construct gene regulatory networks based on BP neural network. Comput Biol Med 2022; 151:106249. [PMID: 36335815 DOI: 10.1016/j.compbiomed.2022.106249] [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: 02/17/2022] [Revised: 10/14/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
The deterioration and metastasis of cancer involve various aspects of genomic changes, including genomic DNA changes, epigenetic modifications, gene expression, and other complex interactions. Therefore, integrating single-cell multi-omics data to construct gene regulatory networks containing more omics information is of great significance for understanding the pathogenesis of cancer. In this article, an algorithm integrating single-cell RNA sequencing data and DNA methylation data to construct a gene regulatory network based on the back-propagation (BP) neural network (scBPGRN) is proposed. This algorithm uses biweight extreme correlation coefficients to measure the correlation between factors and uses neural networks to calculate generalized weights to construct gene regulation networks. Finally, the node strength is calculated to identify the genes associated with cancer. We apply the scBPGRN algorithm to hepatocellular carcinoma (HCC) data. We construct a regulatory network and identify top-ranked genes, such as MYCBP, KLHL35, PRKCZ, and SERPINA6, as the key HCC-related genes. We analyze the top 100 genes, and the HCC-related genes are concentrated in the top 20. In addition, the single cell data is found to consist of two subpopulations. We also apply scBPGRN to two subpopulations. We analyze the top 50 genes in them, and the HCC-related genes are concentrated in the top 20. The consequences of functional enrichment analysis indicate that the gene regulatory network we have constructed is valid. Our results have been verified in several pieces of literature. This study provides a reference for the integration of single-cell multi-omics data to construct gene regulatory networks.
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Affiliation(s)
- Chenxu Xuan
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yan Wang
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Tao Ding
- School of Mathematics Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China.
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Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks. Sci Rep 2022; 12:18704. [PMID: 36333425 PMCID: PMC9636198 DOI: 10.1038/s41598-022-21957-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Abstract
Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem. Gene expression datasets consist of thousand of genes with relatively small sample sizes (i.e. are large-p-small-n). Moreover, dependencies of various orders coexist in the datasets. On the one hand transcription factor encoding genes act like hubs and regulate target genes, on the other hand target genes show local dependencies. In the field of Undirected Network Models (UNMs)-a subclass of PNMs-the Glasso algorithm has been proposed to deal with high dimensional microarray datasets forcing sparsity. To overcome the problem of the complex structure of interactions, modifications of the default Glasso algorithm have been developed that integrate the expected dependency structure in the UNMs beforehand. In this work we advocate the use of a simple score-based Hill Climbing algorithm (HC) that learns Gaussian Bayesian networks leaning on directed acyclic graphs. We compare HC with Glasso and variants in the UNM framework based on their capability to reconstruct GRNs from microarray data from the benchmarking synthetic dataset from the DREAM5 challenge and from real-world data from the Escherichia coli genome. We conclude that dependencies in complex data are learned best by the HC algorithm, presenting them most accurately and efficiently, simultaneously modelling strong local and weaker but significant global connections coexisting in the gene expression dataset. The HC algorithm adapts intrinsically to the complex dependency structure of the dataset, without forcing a specific structure in advance.
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Chen G, Liu ZP. Inferring causal gene regulatory network via GreyNet: From dynamic grey association to causation. Front Bioeng Biotechnol 2022; 10:954610. [PMID: 36237217 PMCID: PMC9551017 DOI: 10.3389/fbioe.2022.954610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/15/2022] [Indexed: 11/23/2022] Open
Abstract
Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks. Nevertheless, machine learning, especially deep learning, is generally a “black box,” which lacks interpretability. The causality has not been well recognized in GRN inference procedures. In this article, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene–gene interactions in the uncertain regulatory system. Then, we incorporate generalized multivariate Granger causality regression methods to transform the dynamic grey association into causation to generate directional regulatory links. We evaluate our model on the DREAM4 in silico benchmark dataset and real-world hepatocellular carcinoma (HCC) time-series data. We achieved competitive results on the DREAM4 compared with other state-of-the-art algorithms and gained meaningful GRN structure on HCC data respectively.
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Affiliation(s)
- Guangyi Chen
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
- Center for Intelligent Medicine, Shandong University, Jinan, Shandong, China
- *Correspondence: Zhi-Ping Liu,
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18
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Pačínková A, Popovici V. Using empirical biological knowledge to infer regulatory networks from multi-omics data. BMC Bioinformatics 2022; 23:351. [PMID: 35996085 PMCID: PMC9396869 DOI: 10.1186/s12859-022-04891-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/08/2022] [Indexed: 12/13/2022] Open
Abstract
Background Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. Results We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. Conclusions We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04891-9.
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Affiliation(s)
- Anna Pačínková
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic. .,Faculty of Informatics, Masaryk University, Botanicka 68a, Brno, Czech Republic.
| | - Vlad Popovici
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
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Pušnik Ž, Mraz M, Zimic N, Moškon M. Review and assessment of Boolean approaches for inference of gene regulatory networks. Heliyon 2022; 8:e10222. [PMID: 36033302 PMCID: PMC9403406 DOI: 10.1016/j.heliyon.2022.e10222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/22/2022] [Accepted: 08/03/2022] [Indexed: 10/25/2022] Open
Abstract
Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.
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Affiliation(s)
- Žiga Pušnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Mraz
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Nikolaj Zimic
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Moškon
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
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20
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Foo M, Dony L, He F. Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: A comparative analysis. Biosystems 2022; 219:104732. [PMID: 35781035 DOI: 10.1016/j.biosystems.2022.104732] [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: 02/25/2022] [Revised: 05/30/2022] [Accepted: 06/22/2022] [Indexed: 11/02/2022]
Abstract
Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten with Hill coefficient (Hill Function), standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach.
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Affiliation(s)
- Mathias Foo
- School of Engineering, University of Warwick, CV4 7AL, Coventry, UK.
| | - Leander Dony
- Institute of Computational Biology, Helmholtz Munich, 85764, Neuherberg, Germany; Department of Translational Psychiatry, Max Planck Institute of Psychiatry, International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, CV1 2JH, Coventry, UK.
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21
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Amirinezhad A, Salehkaleybar S, Hashemi M. Active learning of causal structures with deep reinforcement learning. Neural Netw 2022; 154:22-30. [PMID: 35843011 DOI: 10.1016/j.neunet.2022.06.028] [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: 09/01/2021] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing intervention in each step. Both networks are trained jointly via a Q-iteration algorithm. Experimental results show that the proposed method achieves competitive performance in recovering causal structures with respect to previous works, while significantly reducing execution time in dense graphs.
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Affiliation(s)
- Amir Amirinezhad
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
| | - Saber Salehkaleybar
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
| | - Matin Hashemi
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
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22
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Ying X, Leng SY, Ma HF, Nie Q, Lai YC, Lin W. Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately. RESEARCH 2022; 2022:9870149. [PMID: 35600089 PMCID: PMC9101326 DOI: 10.34133/2022/9870149] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/24/2022] [Indexed: 11/06/2022]
Abstract
Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.
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Affiliation(s)
- Xiong Ying
- School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
| | - Si-Yang Leng
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Huan-Fei Ma
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
| | - Qing Nie
- Department of Mathematics, Department of Developmental and Cell Biology, And NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697-3875, USA
| | - Ying-Cheng Lai
- School of Electrical, Computer, And Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706, USA
| | - Wei Lin
- School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
- Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
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23
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Karaaslanli A, Saha S, Aviyente S, Maiti T. scSGL: Kernelized Signed Graph Learning for Single-Cell Gene Regulatory Network Inference. Bioinformatics 2022; 38:3011-3019. [PMID: 35451460 DOI: 10.1093/bioinformatics/btac288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/10/2022] [Accepted: 04/19/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, a characteristic feature of GRNs, which are capable of accounting for both activating and inhibitory relationships in the gene network. They are also incapable of handling high proportion of zero values present in the single cell datasets. RESULTS To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to account for non-linearity of co-expression and for effective handling of highly occurring zero values. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. Performance assessment using simulated datasets demonstrates the superior performance of kernelized scSGL over existing state of the art methods in GRN recovery. The performance of scSGL is further investigated using human and mouse embryonic datasets. AVAILABILITY AND IMPLEMENTATION The scSGL code and analysis scripts are available on https://github.com/Single-Cell-Graph-Learning/scSGL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Abdullah Karaaslanli
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, US
| | - Satabdi Saha
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, US
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, US
| | - Tapabrata Maiti
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, US
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24
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Liu Y, Li L, Wang X. A nonlinear sparse neural ordinary differential equation model for multiple functional processes. CAN J STAT 2022; 50:59-85. [PMID: 35530428 PMCID: PMC9075179 DOI: 10.1002/cjs.11666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 07/06/2021] [Indexed: 11/06/2022]
Abstract
In this article, we propose a new sparse neural ordinary differential equation (ODE) model to characterize flexible relations among multiple functional processes. We characterize the latent states of the functions via a set of ordinary differential equations. We then model the dynamic changes of the latent states using a deep neural network (DNN) with a specially designed architecture and a sparsity-inducing regularization. The new model is able to capture both nonlinear and sparse dependent relations among multivariate functions. We develop an efficient optimization algorithm to estimate the unknown weights for the DNN under the sparsity constraint. We establish both the algorithmic convergence and selection consistency, which constitute the theoretical guarantees of the proposed method. We illustrate the efficacy of the method through simulations and a gene regulatory network example.
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Affiliation(s)
- Yijia Liu
- Department of Statistics, Purdue University
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California at Berkeley
| | - Xiao Wang
- Department of Statistics, Purdue University
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25
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Anand S, Muthu Kumar S, Mukherjee K, Padmanabhan P. Insight into fermentable sugar recovery process from sugarcane bagasse: in silico elucidation of enzymatic hydrolysis and techno-economic assessment. JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2022. [DOI: 10.1080/16583655.2022.2040243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Shreya Anand
- Department of Bioengineering & Biotechnology, Birla Institute of Technology, Ranchi, India
| | - Sampath Muthu Kumar
- Department of Bioengineering & Biotechnology, Birla Institute of Technology, Ranchi, India
| | - Koel Mukherjee
- Department of Bioengineering & Biotechnology, Birla Institute of Technology, Ranchi, India
| | - Padmini Padmanabhan
- Department of Bioengineering & Biotechnology, Birla Institute of Technology, Ranchi, India
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26
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On principal graphical models with application to gene network. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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27
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Abstract
Ordinary differential equation (ODE) is widely used in modeling biological and physical processes in science. In this article, we propose a new reproducing kernel-based approach for estimation and inference of ODE given noisy observations. We do not assume the functional forms in ODE to be known, or restrict them to be linear or additive, and we allow pairwise interactions. We perform sparse estimation to select individual functionals, and construct confidence intervals for the estimated signal trajectories. We establish the estimation optimality and selection consistency of kernel ODE under both the low-dimensional and high-dimensional settings, where the number of unknown functionals can be smaller or larger than the sample size. Our proposal builds upon the smoothing spline analysis of variance (SS-ANOVA) framework, but tackles several important problems that are not yet fully addressed, and thus extends the scope of existing SS-ANOVA as well. We demonstrate the efficacy of our method through numerous ODE examples.
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Affiliation(s)
- Xiaowu Dai
- Department of Economics and Simons Institute for the Theory of Computing, the University of California, Berkeley, Berkeley, CA
| | - Lexin Li
- Department of Economics and Simons Institute for the Theory of Computing, the University of California, Berkeley, Berkeley, CA
- Department of Biostatistics and Epidemiology, the University of California, Berkeley, Berkeley, CA
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28
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Chen CK. Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections. Stat Appl Genet Mol Biol 2021; 20:121-143. [PMID: 34963205 DOI: 10.1515/sagmb-2020-0054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/15/2021] [Indexed: 12/11/2022]
Abstract
The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.
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Affiliation(s)
- Chi-Kan Chen
- Department of Applied Mathematics, National Chung Hsing University, 145 Xingda Rd., South District, Taichung City, Taiwan, ROC
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29
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Hayes SMS, Sachs JR, Cho CR. From complex data to biological insight: 'DEKER' feature selection and network inference. J Pharmacokinet Pharmacodyn 2021; 49:81-99. [PMID: 34791577 PMCID: PMC8837529 DOI: 10.1007/s10928-021-09792-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2021] [Indexed: 11/12/2022]
Abstract
Network inference is a valuable approach for gaining mechanistic insight from high-dimensional biological data. Existing methods for network inference focus on ranking all possible relations (edges) among all measured quantities such as genes, proteins, metabolites (features) observed, which yields a dense network that is challenging to interpret. Identifying a sparse, interpretable network using these methods thus requires an error-prone thresholding step which compromises their performance. In this article we propose a new method, DEKER-NET, that addresses this limitation by directly identifying a sparse, interpretable network without thresholding, improving real-world performance. DEKER-NET uses a novel machine learning method for feature selection in an iterative framework for network inference. DEKER-NET is extremely flexible, handling linear and nonlinear relations while making no assumptions about the underlying distribution of data, and is suitable for categorical or continuous variables. We test our method on the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge data, demonstrating that it can directly identify sparse, interpretable networks without thresholding while maintaining performance comparable to the hypothetical best-case thresholded network of other methods.
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Affiliation(s)
- Sean M S Hayes
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ, USA.
| | - Jeffrey R Sachs
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Carolyn R Cho
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ, USA
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30
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Grimes T, Datta S. A novel probabilistic generator for large-scale gene association networks. PLoS One 2021; 16:e0259193. [PMID: 34767561 PMCID: PMC8589155 DOI: 10.1371/journal.pone.0259193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 10/14/2021] [Indexed: 11/18/2022] Open
Abstract
MOTIVATION Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators-such as GeneNetWeaver-are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. RESULTS We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used "scale-free" model is insufficient for replicating these structures. AVAILABILITY This generator is implemented in the R package "SeqNet" and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).
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Affiliation(s)
- Tyler Grimes
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
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Raharinirina NA, Peppert F, von Kleist M, Schütte C, Sunkara V. Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments. PATTERNS 2021; 2:100332. [PMID: 34553172 PMCID: PMC8441581 DOI: 10.1016/j.patter.2021.100332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 02/23/2021] [Accepted: 07/22/2021] [Indexed: 11/30/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealized due to technical and computational challenges. In this work we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: data pre-processing, feature extraction, and inference. We observed that the regulatory signature is captured in the statistical moments of scRNA-seq data and requires computationally intensive minimization solvers to extract it. Furthermore, current data pre-processing might not conserve these statistical moments. Although our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking—finding computationally feasible multi-dimensional statistics of data—is imperative for designing GRN inference methods. Single-cell RNA-seq temporal snapshot data for detecting regulation Challenges in data pre-processing, feature extraction, and network inference for GRNs Encoding of regulatory information in higher-order raw moments Non-linear least-squares inference for temporal scRNA-seq snapshot data
Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. A recent benchmark of 12 GRN methods demonstrated that the algorithms struggled to predict the ground-truth GRNs and speculated that the low performance was due to the insufficient resolution in the scRNA-seq data. Rather than proposing another method, this paper focuses on how to decompose a GRN problem into three subproblems (pre-processing, feature extraction, and inference), so that the gene regulatory information is preserved in each step. Subsequently, we discuss how to best approach each of the three subproblems.
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Affiliation(s)
| | - Felix Peppert
- Explainable A.I. for Biology, Zuse Institute Berlin, 14195 Berlin, Germany
| | - Max von Kleist
- MF1 Bioinformatics, Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
| | - Christof Schütte
- Mathematics of Complex Systems, Zuse Institute Berlin, 14195 Berlin, Germany.,Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany
| | - Vikram Sunkara
- Mathematics of Complex Systems, Zuse Institute Berlin, 14195 Berlin, Germany.,Explainable A.I. for Biology, Zuse Institute Berlin, 14195 Berlin, Germany
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Ghosh Roy G, Geard N, Verspoor K, He S. PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data. Bioinformatics 2021; 36:5187-5193. [PMID: 32697830 DOI: 10.1093/bioinformatics/btaa651] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/06/2020] [Accepted: 07/16/2020] [Indexed: 02/01/2023] Open
Abstract
MOTIVATION Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. RESULTS To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. AVAILABILITY AND IMPLEMENTATION Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gourab Ghosh Roy
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.,School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Karin Verspoor
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
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Pyne S, Anand A. Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1608-1619. [PMID: 31613774 DOI: 10.1109/tcbb.2019.2946826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs - memory-inefficiency or imposition of a constraint, known as the 'smoothly time-varying assumption'. In this article, two novel algorithms - 'an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory' (TGS-Lite) and 'TGS-Lite Plus' (TGS-Lite+) - are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets. Source Code: https://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode.
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Grimes T, Datta S. SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data. J Stat Softw 2021; 98:10.18637/jss.v098.i12. [PMID: 34321962 PMCID: PMC8315007 DOI: 10.18637/jss.v098.i12] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.
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Affiliation(s)
- Tyler Grimes
- Univeristy of Florida, Department of Biostatistics
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Cannoodt R, Saelens W, Deconinck L, Saeys Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat Commun 2021; 12:3942. [PMID: 34168133 PMCID: PMC8225657 DOI: 10.1038/s41467-021-24152-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 05/27/2021] [Indexed: 02/05/2023] Open
Abstract
We present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of computational methods. We demonstrate its potential for spearheading computational methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.
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Affiliation(s)
- Robrecht Cannoodt
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium ,Data Intuitive, Lebbeke, Belgium
| | - Wouter Saelens
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium ,grid.5333.60000000121839049Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Louise Deconinck
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | - Yvan Saeys
- grid.11486.3a0000000104788040Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
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Vatsa D, Agarwal S. PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data. PLoS One 2021; 16:e0251666. [PMID: 33989333 PMCID: PMC8121333 DOI: 10.1371/journal.pone.0251666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/30/2021] [Indexed: 11/22/2022] Open
Abstract
The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process.
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Affiliation(s)
- Deepika Vatsa
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Sumeet Agarwal
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- * E-mail: ,
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Abstract
Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.
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Hütt MT, Lesne A. Gene Regulatory Networks: Dissecting Structure and Dynamics. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11467-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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40
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Makashov AA, Myasnikova EM, Spirov AV. Fuzzy Linguistic Modeling of the Regulation of Drosophila Segmentation Genes. Biophysics (Nagoya-shi) 2021. [DOI: 10.1134/s0006350921010073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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41
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Lu J, Dumitrascu B, McDowell IC, Jo B, Barrera A, Hong LK, Leichter SM, Reddy TE, Engelhardt BE. Causal network inference from gene transcriptional time-series response to glucocorticoids. PLoS Comput Biol 2021; 17:e1008223. [PMID: 33513136 PMCID: PMC7875426 DOI: 10.1371/journal.pcbi.1008223] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 02/10/2021] [Accepted: 08/07/2020] [Indexed: 11/19/2022] Open
Abstract
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
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Affiliation(s)
- Jonathan Lu
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Ian C. McDowell
- Element Genomics, A UCB Company, Durham, North Carolina, United States of America
| | - Brian Jo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Alejandro Barrera
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Linda K. Hong
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Sarah M. Leichter
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Timothy E. Reddy
- Department of Genome Sciences, Duke University, Durham, North Carolina, United States of America
| | - Barbara E. Engelhardt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America
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Barman S, Kwon YK. A neuro-evolution approach to infer a Boolean network from time-series gene expressions. Bioinformatics 2020; 36:i762-i769. [PMID: 33381823 DOI: 10.1093/bioinformatics/btaa840] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions.In this regard, we devised a novel genetic algorithm combined with a neural network for the Boolean network inference, where a neural network is used to represent the regulatory function instead of an incomplete Boolean truth table used in the GABNI. In addition, our new method extended the range of the time-step lag parameter value between the regulatory and the target genes for more flexible representation of the regulatory function. Extensive simulations with the gene expression datasets of the artificial and real networks were conducted to compare our method with five well-known existing methods including GABNI. Our proposed method significantly outperformed them in terms of both structural and dynamics accuracy. CONCLUSION Our method can be a promising tool to infer a large-scale Boolean regulatory network from time-series gene expression data. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https://github.com/kwon-uou/NNBNI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shohag Barman
- Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh
| | - Yung-Keun Kwon
- School of IT Convergence, University of Ulsan, Ulsan 44610, Republic of Korea
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43
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Sun R, Xu Y, Zhang H, Yang Q, Wang K, Shi Y, Wang Z. Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma. Front Genet 2020; 11:595242. [PMID: 33424926 PMCID: PMC7786279 DOI: 10.3389/fgene.2020.595242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and has long been among the top three cancers that cause the most deaths worldwide. Therapeutic options for HCC are limited due to the pronounced tumor heterogeneity. Thus, there is a critical need to study HCC from a systems point of view to discover effective therapeutic targets, such as through the systematic study of disease perturbation in both regulation and metabolism using a unified model. Such integration makes sense for cancers as it links one of the dominant physiological features of cancers (growth, which is driven by metabolic networks) with the primary available omics data source, transcriptomics (which is systematically integrated with metabolism through the regulatory-metabolic network model). Here, we developed an integrated transcriptional regulatory-metabolic model for HCC molecular stratification and the prediction of potential therapeutic targets. To predict transcription factors (TFs) and target genes affecting tumorigenesis, we used two algorithms to reconstruct the genome-scale transcriptional regulatory networks for HCC and normal liver tissue. which were then integrated with corresponding constraint-based metabolic models. Five key TFs affecting cancer cell growth were identified. They included the regulator CREB3L3, which has been associated with poor prognosis. Comprehensive personalized metabolic analysis based on models generated from data of liver HCC in The Cancer Genome Atlas revealed 18 genes essential for tumorigenesis in all three subtypes of patients stratified based on the non-negative matrix factorization method and two other genes (ACADSB and CMPK1) that have been strongly correlated with lower overall survival subtype. Among these 20 genes, 11 are targeted by approved drugs for cancers or cancer-related diseases, and six other genes have corresponding drugs being evaluated experimentally or investigationally. The remaining three genes represent potential targets. We also validated the stratification and prognosis results by an independent dataset of HCC cohort samples (LIRI-JP) from the International Cancer Genome Consortium database. In addition, microRNAs targeting key TFs and genes were also involved in established cancer-related pathways. Taken together, the multi-scale regulatory-metabolic model provided a new approach to assess key mechanisms of HCC cell proliferation in the context of systems and suggested potential targets.
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Affiliation(s)
| | | | | | | | | | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
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Aziz F, Acharjee A, Williams JA, Russ D, Bravo-Merodio L, Gkoutos GV. Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference. Int J Mol Sci 2020; 21:E7886. [PMID: 33114263 PMCID: PMC7660606 DOI: 10.3390/ijms21217886] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/22/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022] Open
Abstract
Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.
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Affiliation(s)
- Furqan Aziz
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
| | - John A. Williams
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- Medical Research Council Harwell Institute, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Dominic Russ
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
| | - Laura Bravo-Merodio
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK; (F.A.); (J.A.W.); (D.R.); (L.B.-M.); (G.V.G.)
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- MRC Health Data Research UK (HDR UK), Midlands B15 2TT, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham B15 2TT, UK
- NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
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45
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Ma B, Fang M, Jiao X. Inference of gene regulatory networks based on nonlinear ordinary differential equations. Bioinformatics 2020; 36:4885-4893. [DOI: 10.1093/bioinformatics/btaa032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 12/30/2019] [Accepted: 01/15/2020] [Indexed: 01/05/2023] Open
Abstract
Abstract
Motivation
Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks.
Results
In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity.
Availability and implementation
The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Baoshan Ma
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Mingkun Fang
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Xiangtian Jiao
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
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46
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Gene regulatory network inference from sparsely sampled noisy data. Nat Commun 2020; 11:3493. [PMID: 32661225 PMCID: PMC7359369 DOI: 10.1038/s41467-020-17217-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 06/11/2020] [Indexed: 12/16/2022] Open
Abstract
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO's superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.
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47
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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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48
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Morphological analysis of Spirulina sp. NCIM 5143 and Nostoc ellipsosporum NCIM 2786 and comparative characterization of associated enzymes through in silico approach. Biologia (Bratisl) 2020. [DOI: 10.2478/s11756-020-00526-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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49
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Partial cross mapping eliminates indirect causal influences. Nat Commun 2020; 11:2632. [PMID: 32457301 PMCID: PMC7251131 DOI: 10.1038/s41467-020-16238-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/22/2020] [Indexed: 12/27/2022] Open
Abstract
Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data. It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data.
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50
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. BICORN: An R package for integrative inference of de novo cis-regulatory modules. Sci Rep 2020; 10:7960. [PMID: 32409786 PMCID: PMC7224214 DOI: 10.1038/s41598-020-63043-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA
| | - Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX, 75204, USA
| | - Andrew F Neuwald
- Institute for Genome Sciences and Department Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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