ArticleNucleic acids research2021
Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Assessing tissue-specific gene expression of essential genes from human and mouse.Briefings in bioinformatics · 2025Article
- A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.Nature aging · 2025Article
- RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study.BMC medical genomics · 2025Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Principal and independent genomic components of brain structure and function.Genes, brain, and behavior · 2024Article
- Deciphering the maize gene ZmGF14-3: implications for plant height based on co-expression networks.Frontiers in plant science · 2024Article
- COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.Bioinformatics (Oxford, England) · 2023Article
- MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.Nature communications · 2023Article
- Single-cell RNA-seq uncovers dynamic processes orchestrated by RNA-binding protein DDX43 in chromatin remodeling during spermiogenesis.Nature communications · 2023Article
- Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Plants (Basel, Switzerland) · 2023Article
- Density-based detection of cell transition states to construct disparate and bifurcating trajectories.Nucleic acids research · 2022Article
- HDAC3 controls male fertility through enzyme-independent transcriptional regulation at the meiotic exit of spermatogenesis.Nucleic acids research · 2021Article
- SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.Computational and mathematical methods in medicine · 2021Article
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9 authors.
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Abstract
With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.
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