Abstract
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.