Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data.

Bioinformatics (Oxford, England) - 2 Sept 2024

Marot-Lassauzaie Valérie, Beneyto-Calabuig Sergi, Obermayer Benedikt, Velten Lars, Beule Dieter, Haghverdi Laleh

Abstract excerpt

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data are widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single-nucleotide variants (SNVs) observed in the scRNA-seq data could be used to...

Topics

Join the communities discussing this publication.

Algorithms
Lung Neoplasms
Neoplasms
RNA-Seq
Sequence Analysis, RNA

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Single-Cell Analysis

Single-Cell Gene Expression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 39163479
DOI: 10.1093/bioinformatics/btae512
PMCID: PMC11379463

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data.PMID 39163479Read ↗

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq dataSemantic proximity Read ↗

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.Semantic proximity Read ↗

SCANBIT facilitates identification of tumor cell populations in scRNAseq data using pseudobulked SNV callsSemantic proximity Read ↗

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing dataSemantic proximity Read ↗

A wealth of novel cell-specific expressed SNVs from tumor and normal scRNA-seq datasetsSemantic proximity Read ↗

SIngle cell level Genotyping Using scRna Data (SIGURD)Semantic proximity Read ↗

SIngle cell level Genotyping Using scRna Data (SIGURD).Semantic proximity Read ↗

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.Semantic proximity Read ↗

Testing for phylogenetic signal in single-cell RNA-seq dataSemantic proximity Read ↗

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencingSemantic proximity Read ↗

Accurate <i>de novo</i> detection of somatic mutations in high-throughput single-cell profiling data setsSemantic proximity Read ↗

Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancerSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.