ArticleNature communications2022
Single-cell gene fusion detection by scFusion.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 34 citations in OpenAlex.
- Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.BioData mining · 2026Article
- Fusion gene discovery in single cells from high throughput long read single cell transcriptomes.bioRxiv : the preprint server for biology · 2026Article
- AI-enhanced flexible ECG patch for accurate heart disease diagnosis, optimal wear positioning, and interactive medical consultation.National science review · 2025Article
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
- Drug resistance and tumor heterogeneity: cells and ensembles.Biophysical reviews · 2025Review
- Oncogenic gene fusions in cancer: from biology to therapy.Signal transduction and targeted therapy · 2025Review
- Deciphering IGH rearrangement complexity and detection strategies in acute lymphoblastic leukaemia.NPJ precision oncology · 2025Review
- ELLIPSIS: robust quantification of splicing in scRNA-seq.Bioinformatics (Oxford, England) · 2025Article
- Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?Annals of hematology · 2025Review
- Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA.Cell reports methods · 2025Article
- scTML: a pan-cancer single-cell landscape of multiple mutation types.Nucleic acids research · 2025Article
- Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system.Nature neuroscience · 2024Review
- Directly selecting cell-type marker genes for single-cell clustering analyses.Cell reports methods · 2024Article
- Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data.Cell reports methods · 2024Article
- A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity.Scientific data · 2024Article
- [Chinese Expert Consensus on the Clinical Practice of Non-small Cell Lung Cancer Fusion Gene Detection Based on RNA-based NGS].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Article
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- Small Molecule Inhibitors as Therapeutic Agents Targeting Oncogenic Fusion Proteins: Current Status and Clinical.Molecules (Basel, Switzerland) · 2023Review
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.
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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.