ArticleBioinformatics (Oxford, England)2019
Cell-level somatic mutation detection from single-cell RNA sequencing.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
34 citing papers in PubMed, 55 citations in OpenAlex.
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- De Novo Detection of Clonal Structure and Evolution in Single-Cell and Spatial Transcriptomes.International journal of molecular sciences · 2025Article
- Clinical application of single-cell RNA sequencing in disease and therapy.Clinical and translational medicine · 2025Review
- TRANS: a prediction model for EGFR mutation status in NSCLC based on radiomics and clinical features.Respiratory research · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- scTML: a pan-cancer single-cell landscape of multiple mutation types.Nucleic acids research · 2025Article
- scVAR: integrating genomics and transcriptomics from single-cell RNA-seq -insights from leukemia case studies.Frontiers in genetics · 2025Article
- Somatic mutations in autoinflammatory and autoimmune disease.Nature reviews. Rheumatology · 2024Review
- Structured testing of genetic association with mixed clinical outcomes.Genetic epidemiology · 2024Article
- Single-cell lineage tracing with endogenous markers.Biophysical reviews · 2024Review
- De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.Genome medicine · 2023Article
- Personalized tumor combination therapy optimization using the single-cell transcriptome.Genome medicine · 2023Article
- STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer.Genome biology · 2023Article
- Applications of single-cell RNA sequencing in drug discovery and development.Nature reviews. Drug discovery · 2023Review
- Identifying Aging and Alzheimer Disease-Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex.Neurology. Genetics · 2023Article
- SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.Bioinformatics (Oxford, England) · 2023Article
- Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma.Frontiers in pharmacology · 2023Review
- Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing.Clinical and translational medicine · 2022Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationBoth single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.
resultsMutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method-called SCmut-to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity. AVAILABILITY AND IMPLEMENTATION: The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
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.