Journal Article

DZNE-2025-00786

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.

Lian, B. ; Zhang, H. ; Wang, T. ; Wang, Y. ; Shang, X. ; Aziz, N. A.DZNE* ; Hu, J. (Last author)DZNE*

2025
Oxford University Press Oxford [u.a.]

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Please use a persistent id in citations: doi:10.1093/bib/bbaf309

Abstract: Gene regulation varies across different cell types and developmental stages, leading to distinct cellular roles across cellular populations. Investigating cell type-specific gene coexpression is therefore crucial for understanding gene functions and disease pathology. However, reconstructing gene coexpression networks from single-cell transcriptome data is challenging due to artifacts, noise, and data sparsity. Here, we present an efficient method for inference of gene coexpression networks via variance decomposition analysis (GCNVDA) to explore the underlying gene regulatory mechanisms from single-cell transcriptome data. Our model incorporates multiple sources of variability, including a random effect term $G$ to capture gene-level variance and a random effect term $E$ to account for residual errors. We applied GCNVDA to three real-world single-cell datasets, demonstrating that our method outperforms existing state-of-the-art algorithms in both sensitivity and specificity for identifying tissue- or state-specific gene regulations. Furthermore, GCNVDA facilitates the discovery of functional modules that play critical roles in key biological processes such as embryonic development. These findings provide new insights into cell-specific regulatory mechanisms and have the potential to significantly advance research in developmental biology and disease pathology.

Keyword(s): Single-Cell Analysis: methods (MeSH) ; Gene Regulatory Networks (MeSH) ; Algorithms (MeSH) ; Transcriptome (MeSH) ; Gene Expression Profiling: methods (MeSH) ; Humans (MeSH) ; Computational Biology: methods (MeSH) ; Animals (MeSH) ; gene coexpression networks ; gene functional modules ; linear mixed model ; single-cell RNA sequencing

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Contributing Institute(s):

1. Population & Clinical Neuroepidemiology (AG Aziz)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Appears in the scientific report 2025

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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