ReviewBriefings in bioinformatics2021
The effect of tissue composition on gene co-expression.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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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Who cites it
14 citing papers in PubMed.
- Testicular Gap (Animals : an open access journal from MDPI · 2026Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Novel insights into post-myocardial infarction cardiac remodeling through algorithmic detection of cell-type composition shifts.PLoS genetics · 2025Article
- The cellular substrate of evolutionary novelty.Current biology : CB · 2025Review
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Article
- Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution.Nature ecology & evolution · 2023Article
- Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis.Computational and structural biotechnology journal · 2023Article
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- Monosomy X in isogenic human iPSC-derived trophoblast model impacts expression modules preserved in human placenta.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Addressing the mean-correlation relationship in co-expression analysis.PLoS computational biology · 2022Article
- Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data.Genome biology · 2022Article
- Gene network modeling via TopNet reveals functional dependencies between diverse tumor-critical mediator genes.Cell reports · 2021Article
- Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.Cell systems · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.
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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.