ReviewGenome biology2023
Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.
Review in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis and in-silico functional characterization of the SNCA variant rs356220 in Parkinson's disease.Scientific reports · 2025Pooled it
- A transcriptional signature of resting mast cells is associated with improved disease outcome in HRGenes and immunity · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.BMC biology · 2026Article
- Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution.Cell reports methods · 2026Article
- Blood Biochemical Responses to Acute Exercise: Findings from the Molecular Transducers of Physical Activity Consortium (MoTrPAC).bioRxiv : the preprint server for biology · 2026Article
- Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- Cellular heterogeneity and multicellular mechanisms in the pathogenesis of late-life depression: insights from single-cell and spatial multi-omics.Frontiers in immunology · 2026Review
- Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cortical GABAergic Neuron Dysregulation in Schizophrenia Is Age Dependent.Biological psychiatry global open science · 2026Article
- From stasis to systematics: deciphering the pathophysiology of secondary lymphedema through omics.Frontiers in immunology · 2026Review
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- Single-cell transcriptomics of the myeloid milieu reveals an angiogenic niche in triple-negative breast cancer.Experimental & molecular medicine · 2025Article
- Assessment and applications of joint profiling of single-cell chromatin accessibility and transcriptome.Briefings in bioinformatics · 2025Review
- Cell-cell communication dysregulation in tuberous sclerosis complex cortical tubers and focal cortical dysplasia.Acta neuropathologica communications · 2025Article
- Predicting emergent phenotypes from single cell populations using CELLECTION.bioRxiv : the preprint server for biology · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- HIDE: hierarchical cell-type deconvolution.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution 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 OpenQuestion graph.