ArticleGenomics, proteomics & bioinformatics2021
scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets.
Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A novel synonymous TSC2 mutation in a Chinese family leads to tuberous sclerosis type 2 by disrupting Normal pre-mRNA splicing.Journal, genetic engineering & biotechnology · 2026Article
- Condition-Associated Pattern Extraction and Recovery From Multi-Condition Single-Cell RNA-seq Data With CAPER.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrative Transcriptomics and Machine Learning Reveal the Association of CBX4 with Inflammation in Ulcerative Colitis as a Potential Epigenetic Regulator.Biomedicines · 2026Article
- Genetic burden across core genes of the PI3K-AKT-mTOR pathway is associated with susceptibility to microscopic polyangiitis: a Chinese cohort study.Frontiers in immunology · 2026Article
- Assessing tissue-specific gene expression of essential genes from human and mouse.Briefings in bioinformatics · 2025Article
- SpaIM: single-cell spatial transcriptomics imputation via style transfer.Nature communications · 2025Article
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- Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment.Communications biology · 2023Article
- Systemic analysis identifying PVT1/DUSP13 axis for microvascular invasion in hepatocellular carcinoma.Cancer medicine · 2023Article
- The microRNA target site profile is a novel biomarker in the immunotherapy response.Frontiers in oncology · 2023Article
- scSTEM: clustering pseudotime ordered single-cell data.Genome biology · 2022Article
- Quantitative proteomic profiling of hepatocellular carcinoma at different serum alpha-fetoprotein level.Translational oncology · 2022Article
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- Circular RNA SOX5 promotes the proliferation and inhibits the apoptosis of the hepatocellular carcinoma cells by targeting miR-502-5p/synoviolin 1 axis.Bioengineered · 2022Article
- Clinical Diagnostic and Prognostic Potential ofFrontiers in oncology · 2022Article
- DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.Briefings in bioinformatics · 2021Article
- scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.Nature communications · 2021Article
- Advanced Single-cell Omics Technologies and Informatics Tools for Genomics, Proteomics, and Bioinformatics Analysis.Genomics, proteomics & bioinformatics · 2021Article
- Comprehensive Analyses of the Expression, Genetic Alteration, Prognosis Significance, and Interaction Networks of mFrontiers in genetics · 2021Article
- Identification and Validation of Hub Genes Associated with Bladder Cancer by Integrated Bioinformatics and Experimental Assays.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In gene expression profiling studies, including single-cell RNAsequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.