ArticleNature communications2018
Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed.
- Genome-wide association studies ofInfection and immunity · 2026Article
- Nucleotide-resolution mapping of regulatory elements via allelic readout of tiled base editing.Nature communications · 2026Article
- Application of RNA markers in forensic body fluid analysis: from specificity and stability to polymorphism.International journal of legal medicine · 2026Review
- Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants.Fundamental research · 2026Article
- Review
- GAUDI: interpretable multi-omics integration with UMAP embeddings and density-based clustering.Nature communications · 2025Article
- JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization.BMC genomics · 2025Article
- High resolution tissue and cell type identification via single cell transcriptomic profiling.PloS one · 2025Article
- Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells.Diagnostics (Basel, Switzerland) · 2024Review
- Enhlink infers distal and context-specific enhancer-promoter linkages.Genome biology · 2024Article
- Discovering mechanisms of human genetic variation and controlling cell states at scale.Trends in genetics : TIG · 2024Review
- Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution.Genome biology · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- scSNV-seq: high-throughput phenotyping of single nucleotide variants by coupled single-cell genotyping and transcriptomics.Genome biology · 2024Article
- An update review of the application of single-cell RNA sequencing in pregnancy-related diseases.Frontiers in endocrinology · 2024Review
- Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.Bioinformatics (Oxford, England) · 2023Article
- XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples.BMC genomics · 2022Article
- Genomic Analysis of MSM Rectal Chlamydia trachomatis Isolates Identifies Predicted Tissue-Tropic Lineages Generated by Intraspecies Lateral Gene Transfer-Mediated Evolution.Infection and immunity · 2022Article
- EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps.Science advances · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We develop a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship.
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.