scQTLtools

scQTLtools facilitates end-to-end single-cell expression quantitative trait locus (eQTL) analysis, from preprocessing to visualization, for dissecting genotype-expression relationships in heterogeneous cellular populations.

Key Features:

• Flexible Input Formats: Supports Seurat and SingleCellExperiment objects, and handles both binary and three-class genotype encodings.
• Data Preprocessing: Provides dedicated functions for gene expression normalization, SNP filtering, and gene filtering.
• Statistical Models: Implements linear regression, Poisson regression, and zero-inflated negative binomial regression to accommodate diverse data characteristics.
• eQTL Mapping: Supports cis-eQTL mapping with dedicated functions for association testing between SNPs and gene expression.
• Visualization: Enables versatile result visualization of SNP-gene pairs, including positive and negative associations between genotype and gene expression.

Scientific Applications:

• Cell-Type-Specific eQTL Discovery: Identifies regulatory variants with cell-type-specific effects that are often missed by bulk eQTL analyses, as demonstrated in human acute myeloid leukemia scRNA-seq data.
• Genotype-Expression Relationship Dissection: Enables analysis of how genotype-expression relationships vary across heterogeneous cellular populations at single-cell resolution.

Methodology:

scQTLtools performs eQTL mapping using linear regression, Poisson regression, or zero-inflated negative binomial regression on normalized single-cell gene expression data with filtered SNPs and genes.