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

## TL;DR

This paper introduces scQTLtools, a new software package for analyzing how genetic differences affect gene activity in individual cells, revealing insights into cell-type-specific genetic effects.

## Contribution

The novel contribution is scQTLtools, an R/Bioconductor package that enables comprehensive single-cell eQTL analysis with flexible input formats and multiple statistical models.

## Key findings

- scQTLtools identified eQTLs with regulatory effects that vary across cell types in human acute myeloid leukemia data.
- The tool revealed both positive and negative associations between genotype and gene expression through SNP–gene pair visualizations.

## Abstract

Every person is different, and understanding how our genes influence health and disease is a key goal of modern science. However, traditional methods often study mixed groups of cells, which can hide important genetic effects. In this study, we developed a new computer tool that helps scientists explore how genetic differences affect gene activity in individual cells. This tool makes it easier for researchers to process and analyze complex data by providing clear steps and interactive plots. We tested our tool on the dataset from a type of blood cancer and found that some genetic changes only affect certain cell types. These findings show how important it is to look at cells one by one rather than in bulk. Our tool can help researchers discover new disease-related genes and better understand how illnesses develop in different parts of the body. In the future, this may lead to more accurate diagnoses and personalized treatments for patients.

Single-cell RNA sequencing (scRNA-seq) enables expression quantitative trait locus (eQTL) analysis at cellular resolution, offering new opportunities to uncover regulatory variants with cell-type-specific effects. However, existing tools are often limited in functionality, input compatibility, or scalability for sparse single-cell data. To address these challenges, we developed scQTLtools, a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization. The toolkit supports flexible input formats, including Seurat and SingleCellExperiment objects, handles both binary and three-class genotype encodings, and provides dedicated functions for gene expression normalization, SNP and gene filtering, eQTL mapping, and versatile result visualization. To accommodate diverse data characteristics, scQTLtools implements three statistical models—linear regression, Poisson regression, and zero-inflated negative binomial regression. We applied scQTLtools to scRNA-seq data from human acute myeloid leukemia and identified eQTLs with regulatory effects that varied across cell types. Visualization of SNP–gene pairs revealed both positive and negative associations between genotype and gene expression. These results demonstrate the ability of scQTLtools to uncover cell-type-specific regulatory variation that is often missed by bulk eQTL analyses. Currently, scQTLtools supports cis-eQTL mapping; future development will extend to include trans-eQTL detection. Overall, scQTLtools offers a robust, flexible, and user-friendly framework for dissecting genotype–expression relationships in heterogeneous cellular populations.