ArticleComputational and structural biotechnology journal2020
Joint learning of multiple gene networks from single-cell gene expression data.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.Statistics in medicine · 2025Article
- AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.Bioinformatics (Oxford, England) · 2025Article
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Joint gene network construction by single-cell RNA sequencing data.Biometrics · 2023Article
- Kernelized multiview signed graph learning for single-cell RNA sequencing data.BMC bioinformatics · 2023Article
- BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.Microorganisms · 2023Article
- BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data.bioRxiv : the preprint server for biology · 2023Article
- Inferring cancer common and specific gene networks via multi-layer joint graphical model.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance.
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Registered trials
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