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Related Experiment Video

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scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs.

Xiaofeng Wu1, Xin Huang1, Pinjing Chen1

  • 1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Biology
|July 29, 2025
PubMed
Summary

scQTLtools is a new R package for single-cell eQTL analysis. It helps find cell-type-specific gene expression regulation missed by bulk analysis, offering a flexible framework for complex genomic data.

Keywords:
BioconductorRcis-regulatory variantseQTL identificationgenotype–expression associationsingle-cell RNA-seqsingle-cell eQTL analysis

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) allows for expression quantitative trait locus (eQTL) analysis at cellular resolution.
  • Existing tools for scRNA-seq eQTL analysis have limitations in functionality, compatibility, and scalability for sparse data.

Purpose of the Study:

  • To develop a comprehensive R/Bioconductor package, scQTLtools, for end-to-end single-cell eQTL analysis.
  • To provide a robust, flexible, and user-friendly framework for dissecting genotype-expression relationships in heterogeneous cellular populations.

Main Methods:

  • scQTLtools supports flexible input formats (Seurat, SingleCellExperiment) and genotype encodings.
  • It includes functions for normalization, filtering, eQTL mapping, and visualization.
  • Implements linear, Poisson, and zero-inflated negative binomial regression models for diverse data.

Main Results:

  • Applied to human acute myeloid leukemia scRNA-seq data, scQTLtools identified cell-type-specific eQTLs.
  • Revealed both positive and negative associations between genotype and gene expression.
  • Demonstrated the ability to uncover regulatory variation missed by bulk eQTL analyses.

Conclusions:

  • scQTLtools offers a powerful solution for single-cell eQTL analysis, enhancing the discovery of cell-type-specific regulatory elements.
  • The toolkit facilitates a deeper understanding of genotype-phenotype relationships in complex tissues.
  • Future development aims to extend capabilities to trans-eQTL detection.