ReviewNature methods2024
Challenges and perspectives in computational deconvolution of genomics data.
Review in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
What it found
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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.
The trial behind it
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
46 citing papers in PubMed, 61 citations in OpenAlex.
- Towards universal deconvolution of tissue-level multiomics for translational medicine.Clinical and translational medicine · 2026Article
- SlotDeconv: spatial transcriptomics deconvolution via diversity-constrained prototype learning and spatial refinement.Bioinformatics (Oxford, England) · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A scalable framework for single-cell eQTL mapping uncovers genetic regulators of meat production traits in pigs.Journal of animal science and biotechnology · 2026Article
- Placental molecular subtypes of severe preeclampsia reveal divergent aging trajectories and fetal growth outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Macrophage-enriched HMOX1 is an ischemia-reperfusion-responsive marker in the heart.Journal of thoracic disease · 2026Article
- RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.Nature communications · 2026Article
- STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression.Interdisciplinary sciences, computational life sciences · 2026Article
- A unified framework for multiomics deconvolution.Nature methods · 2026Article
- DECODE: deep learning-based common deconvolution framework for various omics data.Nature methods · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.bioRxiv : the preprint server for biology · 2026Article
- DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.Communications biology · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Opportunities for RNA sequencing in physiology: from big data to understanding homeostasis and heterogeneity.Function (Oxford, England) · 2026Review
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.Genome biology · 2026Article
- Deconvolving Phylogenetic Distance Mixtures.bioRxiv : the preprint server for biology · 2026Article
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 4 countries.
Funding
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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What OpenQuestion holds
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.