ArticleNature communications2019
Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Pilot proteomics study of three different human femur sites and autologous serum samples.Bone reports · 2026Article
- CDState Resolves Malignant Cell Heterogeneity from Bulk Tumor RNA Sequencing Data.Cancer research · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Chronic interferon signaling at baseline is associated with acquired resistance to immunotherapy in metastatic melanoma.Cell reports. Medicine · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Genotype epigenome phenotype integration reveals peripheral immune contributions to type I bipolar disorder.Nature communications · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Deconvolution Methods to Link Multi-Omics Data to Cell Type-Specific Extracellular Vesicle Abundances.Proteomics · 2026Review
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 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
- Cell type inference in cell-free nucleic acid liquid biopsy.Nature biotechnology · 2025Review
- From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology.Molecular ecology · 2025Review
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for cell type deconvolution.NAR genomics and bioinformatics · 2025Article
- Robustness and resilience of computational deconvolution methods for bulk RNA sequencing data.Briefings in bioinformatics · 2025Article
- Cell type-specific weighting-factors to solve solid organs-specific limitations of single cell RNA-sequencing.PLoS genetics · 2024Article
- Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.BMC genomics · 2024Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Changes in bulk transcriptional profiles of heterogeneous samples often reflect changes in proportions of individual cell types. Several robust techniques have been developed to dissect the composition of such mixed samples given transcriptional signatures of the pure components or their proportions. These approaches are insufficient, however, in situations when no information about individual mixture components is available. This problem is known as the complete deconvolution problem, where the composition is revealed without any a priori knowledge about cell types and their proportions. Here, we identify a previously unrecognized property of tissue-specific genes - their mutual linearity - and use it to reveal the structure of the topological space of mixed transcriptional profiles and provide a noise-robust approach to the complete deconvolution problem. Furthermore, our analysis reveals systematic bias of all deconvolution techniques due to differences in cell size or RNA-content, and we demonstrate how to address this bias at the experimental design level.
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