ArticleNature communications2021
Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Advancing the applications of liquid biopsies in oncology.Nature reviews. Genetics · 2026Review
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Statescope: an integrative deconvolution framework for discovering cell states in tumors.Nature communications · 2026Article
- Prognostic value of peri-operative circulating tumour DNA levels estimated by cell-free DNA methylation in patients with resectable colorectal liver metastases.EBioMedicine · 2026Article
- Identification of a biologically coherent three gene immune signature predictive of immunotherapy benefit in gastric adenocarcinoma.Discover oncology · 2026Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- BASIN: Bayesian mAtrix variate normal model with Spatial and sparsIty priors in Non-negative deconvolution.ArXiv · 2025Article
- OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.Cell genomics · 2025Article
- From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology.Molecular ecology · 2025Review
- Leveraging chromatin packing domains to target chemoevasion in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.Nature communications · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- VarNMF: non-negative probabilistic factorization with source variation.Bioinformatics (Oxford, England) · 2024Article
- Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.BMC genomics · 2024Article
- Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.Genome biology · 2024Article
- Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.Cell reports methods · 2024Article
- Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.Future oncology (London, England) · 2024Review
- A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing.bioRxiv : the preprint server for biology · 2023Article
- A Comprehensive Overview of RNA Deconvolution Methods and Their Application.Molecules and cells · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.
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