ArticleNAR genomics and bioinformatics2021
Computational deconvolution to estimate cell type-specific gene expression from bulk data.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- Transcriptomics of type 1 diabetes progression: a validation study in newly diagnosed patients.EBioMedicine · 2026Article
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors.Journal of proteome research · 2026Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.PLoS computational biology · 2025Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues.Bioinformatics advances · 2024Article
- Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer.Human genomics · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- Smoking induces shifts in cellular composition and transcriptome within the bronchial mucus barrier.Respirology (Carlton, Vic.) · 2023Article
- A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis.Journal of translational medicine · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.bioRxiv : the preprint server for biology · 2022Article
- Estimating cell type-specific differential expression using deconvolution.Briefings in bioinformatics · 2022Article
- IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.PLoS computational biology · 2021Article
- 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
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.
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