ArticleNature communications2023
Cell-type-specific co-expression inference from single cell RNA-sequencing data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
53 citing papers in PubMed.
- Single nucleus RNA sequencing identifies PLXNA4 upregulation in end-stage arrhythmogenic right ventricular cardiomyopathy.iScience · 2026Article
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.Briefings in bioinformatics · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.Genome research · 2026Article
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- From gene correlations to cell clusters: COTAN improved scRNA-seq analysis.NAR genomics and bioinformatics · 2026Article
- Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop.bioRxiv : the preprint server for biology · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genome-wide investigation of synthetic rescue interactions in Alzheimer's disease implicates glial lipid and sterol metabolism.Alzheimer's research & therapy · 2026Article
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- scGPD: single-cell informed gene panel design for targeted spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Charting cell-type-specific positive genetic interaction at single-cell resolution for lung adenocarcinoma.NPJ precision oncology · 2026Article
- UVB-induced genotoxic stress activates the DNA damage response and innate immune pathways in sea urchin coelomocytes.Frontiers in immunology · 2026Article
- A core stemness-associated module reveals PLK1, NUF2, KIF23, CDCA8, TOP2A, CENPF, AURKA, and ASPM as key genes in rectal cancer.European journal of medical research · 2025Article
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional Similarity.bioRxiv : the preprint server for biology · 2025Article
- Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimer's Disease.Acta neuropathologica communications · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer's disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.
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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 Socratic graph.