eQTLsingle

eQTLsingle identifies expression quantitative trait loci (eQTLs) at single-cell resolution from scRNA-seq data without requiring paired genomic data, enabling discovery of cell-type-specific genetic effects on gene expression.

Key Features:

• R package: Implemented as an R package for analysis of scRNA-seq-derived genotype–phenotype associations.
• Single-Cell Resolution: Detects eQTLs at the individual cell level to capture cellular heterogeneity not observable in bulk RNA-seq.
• No Genomic Data Required: Uses scRNA-seq data alone to detect genetic variants and associate them with expression without paired DNA sequencing.
• Mutation Detection from scRNA-seq: Identifies genetic mutations directly from single-cell RNA sequencing reads.
• Zero-Inflated Negative Binomial (ZINB) Modeling: Models gene expression with a ZINB distribution to account for overdispersion and excess zeros in scRNA-seq data.
• Discovery of Cell-Type-Specific eQTLs: Enables detection of cell-type- and context-specific eQTLs, including hundreds of tumor-related eQTLs in glioblastoma and gliomasphere datasets.
• Revealing Regulatory Mechanisms: Facilitates analysis of regulatory mechanisms underlying variant-associated gene expression differences at single-cell resolution.

Scientific Applications:

• Cancer genomics: Identification of cell-type-specific tumor-related eQTLs, demonstrated on glioblastoma and gliomasphere scRNA-seq datasets.
• Developmental biology: Dissection of cell-type-specific regulatory variants affecting gene expression during development.
• Immunology: Investigation of immune cell–specific eQTLs to study genetic effects on immune cell gene regulation.

Methodology:

Detects genetic mutations directly from scRNA-seq data and models gene expression across genotypes using a zero-inflated negative binomial (ZINB) model to identify associations between variants and expression.