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LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data
MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172039/ https://www.ncbi.nlm.nih.gov/pubmed/37079737 http://dx.doi.org/10.1093/bioinformatics/btad256 |
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author | Li, Lingyu Sun, Liangjie Chen, Guangyi Wong, Chi-Wing Ching, Wai-Ki Liu, Zhi-Ping |
author_facet | Li, Lingyu Sun, Liangjie Chen, Guangyi Wong, Chi-Wing Ching, Wai-Ki Liu, Zhi-Ping |
author_sort | Li, Lingyu |
collection | PubMed |
description | MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF. |
format | Online Article Text |
id | pubmed-10172039 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101720392023-05-12 LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data Li, Lingyu Sun, Liangjie Chen, Guangyi Wong, Chi-Wing Ching, Wai-Ki Liu, Zhi-Ping Bioinformatics Original Paper MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF. Oxford University Press 2023-04-20 /pmc/articles/PMC10172039/ /pubmed/37079737 http://dx.doi.org/10.1093/bioinformatics/btad256 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Li, Lingyu Sun, Liangjie Chen, Guangyi Wong, Chi-Wing Ching, Wai-Ki Liu, Zhi-Ping LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title | LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title_full | LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title_fullStr | LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title_full_unstemmed | LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title_short | LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data |
title_sort | logbtf: gene regulatory network inference using boolean threshold network model from single-cell gene expression data |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172039/ https://www.ncbi.nlm.nih.gov/pubmed/37079737 http://dx.doi.org/10.1093/bioinformatics/btad256 |
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