ArticleBMC bioinformatics2020
SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.Genes · 2026Article
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Multi-omics Data Integration.Advances in experimental medicine and biology · 2026Review
- scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs.Biology · 2025Article
- A brief guide to analyzing expression quantitative trait loci.Molecules and cells · 2024Review
- Expression quantitative trait locus studies in the era of single-cell omics.Frontiers in genetics · 2023Review
- Optimizing expression quantitative trait locus mapping workflows for single-cell studies.Genome biology · 2021Article
- scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets.BMC genomics · 2021Article
- The single-cell eQTLGen consortium.eLife · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundWith the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expression phenotypes at single-cell level by eQTL analysis on single-cell data. New method is needed for such tasks due to special characteristics of single-cell sequencing data.
resultsWe developed an R package SCeQTL that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages or cell types.
conclusionsThe SCeQTL method is capable for eQTL analysis on single-cell data as well as detecting associations of gene expression with other grouping factors. The R package of the method is available at https://github.com/XuegongLab/SCeQTL/.
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