Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes
Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molec...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Research |
| Online Access: | https://spj.science.org/doi/10.34133/research.0743 |
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| author | Xinzhe Huang Luonan Chen Xiaoping Liu |
| author_facet | Xinzhe Huang Luonan Chen Xiaoping Liu |
| author_sort | Xinzhe Huang |
| collection | DOAJ |
| description | Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molecular mechanisms underlying cellular function. Despite substantial advancements, challenges persist in GRN inference, particularly in dynamic rewiring, inferring causality, and context specificity. To tackle these issues, we present single cell-specific causal network (SiCNet), a novel causal network construction method that utilizes single-cell gene expression profiles and a causal inference strategy to construct molecular regulatory networks at a single-cell level. Additionally, SiCNet utilizes cell-specific network information to construct network outdegree matrix (ODM), enhancing the performance of cell clustering. It also enables the construction of context-specific GRNs to identify key regulators of fate transitions for diverse processes such as cellular reprogramming and development. Furthermore, SiCNet can delineate the intricate dynamic regulatory processes involved in development, providing deep insights into the mechanisms governing cellular transitions and the gene regulation across developmental stages. |
| format | Article |
| id | doaj-art-4467e70be22f4bd89ade51e90c5dee63 |
| institution | Kabale University |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-4467e70be22f4bd89ade51e90c5dee632025-08-20T03:27:44ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0743Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological ProcessesXinzhe Huang0Luonan Chen1Xiaoping Liu2Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, 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, Hangzhou 310024, 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, Hangzhou 310024, China.Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molecular mechanisms underlying cellular function. Despite substantial advancements, challenges persist in GRN inference, particularly in dynamic rewiring, inferring causality, and context specificity. To tackle these issues, we present single cell-specific causal network (SiCNet), a novel causal network construction method that utilizes single-cell gene expression profiles and a causal inference strategy to construct molecular regulatory networks at a single-cell level. Additionally, SiCNet utilizes cell-specific network information to construct network outdegree matrix (ODM), enhancing the performance of cell clustering. It also enables the construction of context-specific GRNs to identify key regulators of fate transitions for diverse processes such as cellular reprogramming and development. Furthermore, SiCNet can delineate the intricate dynamic regulatory processes involved in development, providing deep insights into the mechanisms governing cellular transitions and the gene regulation across developmental stages.https://spj.science.org/doi/10.34133/research.0743 |
| spellingShingle | Xinzhe Huang Luonan Chen Xiaoping Liu Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes Research |
| title | Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes |
| title_full | Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes |
| title_fullStr | Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes |
| title_full_unstemmed | Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes |
| title_short | Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes |
| title_sort | constructing cell specific causal networks of individual cells for depicting dynamical biological processes |
| url | https://spj.science.org/doi/10.34133/research.0743 |
| work_keys_str_mv | AT xinzhehuang constructingcellspecificcausalnetworksofindividualcellsfordepictingdynamicalbiologicalprocesses AT luonanchen constructingcellspecificcausalnetworksofindividualcellsfordepictingdynamicalbiologicalprocesses AT xiaopingliu constructingcellspecificcausalnetworksofindividualcellsfordepictingdynamicalbiologicalprocesses |