ArticlePloS one2019
ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials.PLoS medicine · 2020Trial
- Carotid plaque macrophage burden and inflammatory lipid-associated macrophage markers predict secondary major adverse cardiovascular events after endarterectomy.European heart journal · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Characterization, immune cell phenotyping and differential gene expression analysis of brain metastases and primary breast cancer samples.Scientific reports · 2026Article
- Immune Cell Type-Specific DNA Methylation Regions Associate With 24-Hour Blood Pressure Regulation in Black People.Journal of the American Heart Association · 2026Article
- Article
- Optimized RNA sequencing deconvolution illustrates the impact of obesity and weight loss on cell composition of human adipose tissue.Obesity (Silver Spring, Md.) · 2025Article
- Oncolytic immunotherapy with nivolumab in muscle-invasive bladder cancer: a phase 1b trial.Nature medicine · 2025Article
- Cell-type deconvolution for bulk RNA-seq data using single-cell reference: a comparative analysis and recommendation guideline.Briefings in bioinformatics · 2024Article
- Multi-omics reveal immune microenvironment alterations in multiple myeloma and its precursor stages.Blood cancer journal · 2024Article
- Characterization of driver mutations identifies gene signatures predictive of prognosis and treatment sensitivity in multiple myeloma.The oncologist · 2024Article
- Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease.NAR genomics and bioinformatics · 2024Review
- Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.Nature communications · 2024Article
- Benchmarking of methods for DNA methylome deconvolution.Nature communications · 2024Article
- Article
- Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland.Biology of sex differences · 2022Article
- Genetic variation of putative myokine signaling is dominated by biological sex and sex hormones.eLife · 2022Article
- Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome With Aging and Periodontitis.Frontiers in oral health · 2022Article
- Gingival transcriptomics of follicular T cell footprints in progressing periodontitis.Clinical and experimental immunology · 2021Article
- Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes.NAR genomics and bioinformatics · 2021Article
Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
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