ArticleScientific reports2016
Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- CDState Resolves Malignant Cell Heterogeneity from Bulk Tumor RNA Sequencing Data.Cancer research · 2026Article
- IECP: iterative equilibration of cell-type expression profiles improves accuracy of reference-free deconvolution.Bioinformatics (Oxford, England) · 2026Article
- Molecular mechanism leading to human coronary atherosclerosis assessed by proteomic analysis and RNA sequences.European heart journal · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Non-negative matrix factorization and deconvolution as a dual simplex problem.Genome biology · 2026Article
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.Nature communications · 2025Article
- ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for cell type deconvolution.NAR genomics and bioinformatics · 2025Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.Bioinformatics (Oxford, England) · 2024Article
- CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.Transactions of the American Clinical and Climatological Association · 2023Article
- FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.Interdisciplinary sciences, computational life sciences · 2022Article
- Comparative assessment and novel strategy on methods for imputing proteomics data.Scientific reports · 2022Article
- COT: an efficient and accurate method for detecting marker genes among many subtypes.Bioinformatics advances · 2022Article
- MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method.Computational intelligence and neuroscience · 2022Review
- Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection.JVS-vascular science · 2022Article
- stochprofML: stochastic profiling using maximum likelihood estimation in R.BMC bioinformatics · 2021Article
- Data-driven detection of subtype-specific differentially expressed genes.Scientific reports · 2021Article
- A Small Fraction of Progenitors Differentiate Into Mature Adipocytes by Escaping the Constraints on the Cell Structure.Frontiers in cell and developmental biology · 2021Article
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10 authors.
Funding
Abstract
Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.
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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.