ReviewBriefings in bioinformatics2015
An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.
Review in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed.
- Simulation-guided pan-cancer analysis identifies a novel regulator of CpG island hypermethylation heterogeneity.Briefings in bioinformatics · 2025Article
- Unveiling the impact of interferon genes on the immune microenvironment of triple-negative breast cancer: identification of therapeutic targets.Frontiers in bioinformatics · 2025Article
- Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.Frontiers in genetics · 2025Article
- Uncovering the Heterogeneity of Signaling Pathways in Skin Cutaneous Melanoma: Insights into Prognostic Values and Immune Interactions.Clinical, cosmetic and investigational dermatology · 2025Article
- Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.Journal of pathology and translational medicine · 2024Article
- Identification of AURKA as a Biomarker Associated with Cuproptosis and Ferroptosis in HNSCC.International journal of molecular sciences · 2024Article
- Predicting drug response through tumor deconvolution by cancer cell lines.Patterns (New York, N.Y.) · 2024Article
- Constructed Risk Prognosis Model Associated with Disulfidptosis lncRNAs in HCC.International journal of molecular sciences · 2023Article
- Creation of a Prognostic Model Using Cuproptosis-Associated Long Noncoding RNAs in Hepatocellular Carcinoma.International journal of molecular sciences · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- A Clinician's Guide to Bioinformatics for Next-Generation Sequencing.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2023Review
- Metastatic colorectal adenocarcinoma tumor purity assessment from whole exome sequencing data.PloS one · 2023Article
- Tumor microenvironment: barrier or opportunity towards effective cancer therapy.Journal of biomedical science · 2022Review
- Estimating cell type-specific differential expression using deconvolution.Briefings in bioinformatics · 2022Article
- Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma.Frontiers in immunology · 2022Article
- Identification of Signature Genes and Characterizations of Tumor Immune Microenvironment and Tumor Purity in Lung Adenocarcinoma Based on Machine Learning.Frontiers in medicine · 2022Article
- 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
- Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma.Frontiers in genetics · 2021Article
- Identification of Four Genes as Prognosis Signatures in Lung Adenocarcinoma Microenvironment.Pharmacogenomics and personalized medicine · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.