ArticleGenome research2018
Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.
Article in Genome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
143 citing papers in PubMed, 1 synthesis or guideline pooled it, 259 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Robust and generalizable CNV detection for single-cell sequencing assays.Nucleic acids research · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy.Journal of ovarian research · 2026Review
- Phylogenetic tree inference from single-cell RNA sequencing data with SCITE-RNA.Genome biology · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- miRNA-lncRNA Cross-Regulation Landscape in Cancer: From Molecular Mechanisms to Therapeutic and Diagnostic Applications.Cancers · 2026Review
- Inferring tumor absolute copy number and clonal substructure from single-cell chromatin accessibility.Briefings in bioinformatics · 2026Article
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.Cancer research · 2026Article
- Multiomic, Histologic, and scRNA-seq Profiling of Pleural Mesothelioma Reveals Negative Prognosis Associated With a Novel Uncommitted Molecular Phenotype.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Integrative modeling of read depth and B-allele frequency improves single-cell copy number calling from targeted DNA sequencing panels.bioRxiv : the preprint server for biology · 2026Article
- Alternative splicing events show high prognostic values and indicate potential core genes in acute myeloid leukemia.Clinical and experimental medicine · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- Helicobacter pylori infection is causally associated with extra-gastric tumors: a Mendelian randomization study.Discover oncology · 2026Article
- Integrative Modeling of Read Depth and B-Allele Frequency Improves Single-Cell Copy Number Calling from Targeted DNA Sequencing Panels.Computational and structural biotechnology journal · 2026Article
- De Novo Detection of Clonal Structure and Evolution in Single-Cell and Spatial Transcriptomes.International journal of molecular sciences · 2025Article
83 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
Characterization of intratumoral heterogeneity is critical to cancer therapy, as the presence of phenotypically diverse cell populations commonly fuels relapse and resistance to treatment. Although genetic variation is a well-studied source of intratumoral heterogeneity, the functional impact of most genetic alterations remains unclear. Even less understood is the relative importance of other factors influencing heterogeneity, such as epigenetic state or tumor microenvironment. To investigate the relationship between genetic and transcriptional heterogeneity in a context of cancer progression, we devised a computational approach called HoneyBADGER to identify copy number variation and loss of heterozygosity in individual cells from single-cell RNA-sequencing data. By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct the underlying subclonal architecture. By examining several tumor types, we show that HoneyBADGER is effective at identifying deletions, amplifications, and copy-neutral loss-of-heterozygosity events and is capable of robustly identifying subclonal focal alterations as small as 10 megabases. We further apply HoneyBADGER to analyze single cells from a progressive multiple myeloma patient to identify major genetic subclones that exhibit distinct transcriptional signatures relevant to cancer progression. Other prominent transcriptional subpopulations within these tumors did not line up with the genetic subclonal structure and were likely driven by alternative, nonclonal mechanisms. These results highlight the need for integrative analysis to understand the molecular and phenotypic heterogeneity in cancer.
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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.