MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning
Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to ident...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/24/3/2595 |
| _version_ | 1797624279446061056 |
|---|---|
| author | Yongqing Zhang Maocheng Wang Zixuan Wang Yuhang Liu Shuwen Xiong Quan Zou |
| author_facet | Yongqing Zhang Maocheng Wang Zixuan Wang Yuhang Liu Shuwen Xiong Quan Zou |
| author_sort | Yongqing Zhang |
| collection | DOAJ |
| description | Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition. |
| first_indexed | 2024-03-11T09:40:36Z |
| format | Article |
| id | doaj.art-23105993d5d84290a974ce2a93a02725 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T09:40:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-23105993d5d84290a974ce2a93a027252023-11-16T16:59:32ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-01-01243259510.3390/ijms24032595MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-LearningYongqing Zhang0Maocheng Wang1Zixuan Wang2Yuhang Liu3Shuwen Xiong4Quan Zou5School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610051, ChinaRegulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition.https://www.mdpi.com/1422-0067/24/3/2595meta-learninggene regulator network inferencestructural equation modelbi-level optimization |
| spellingShingle | Yongqing Zhang Maocheng Wang Zixuan Wang Yuhang Liu Shuwen Xiong Quan Zou MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning International Journal of Molecular Sciences meta-learning gene regulator network inference structural equation model bi-level optimization |
| title | MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning |
| title_full | MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning |
| title_fullStr | MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning |
| title_full_unstemmed | MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning |
| title_short | MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning |
| title_sort | metasem gene regulatory network inference from single cell rna data by meta learning |
| topic | meta-learning gene regulator network inference structural equation model bi-level optimization |
| url | https://www.mdpi.com/1422-0067/24/3/2595 |
| work_keys_str_mv | AT yongqingzhang metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning AT maochengwang metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning AT zixuanwang metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning AT yuhangliu metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning AT shuwenxiong metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning AT quanzou metasemgeneregulatorynetworkinferencefromsinglecellrnadatabymetalearning |