ArticleBMC bioinformatics2018
Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues.Bioinformatics advances · 2024Article
- A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders.NAR genomics and bioinformatics · 2021Article
- Computational deconvolution to estimate cell type-specific gene expression from bulk data.NAR genomics and bioinformatics · 2021Article
- Article
- CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.PLoS computational biology · 2019Article
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4 authors.
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Abstract
backgroundTowards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes.
resultsWe present Deblender, a flexible complete deconvolution tool operating in semi-/unsupervised mode based on the user's access to known marker gene lists and information about cell/tissue composition. In case of no prior knowledge, global gene expression variability is used in clustering the mixed data to substitute marker sets with cluster sets. In addition, we integrate a model selection criterion to predict the number of constituent cell/tissue types. Moreover, we provide a tailored algorithmic scheme to estimate mixture proportions for realistic experimental cases where the number of involved cell/tissue types exceeds the number of mixed samples. We assess the performance of Deblender and a set of state-of-the-art existing tools on a comprehensive set of benchmark and patient cancer mixture expression datasets (including TCGA).
conclusionOur results corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization. Deblender is implemented in MATLAB and is available from ( https://github.com/kondim1983/Deblender/ ).
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