SCeQTL

SCeQTL implements expression Quantitative Trait Loci (eQTL) analysis as an R package on single-cell parallel sequencing data to associate genotypes with gene expression at single-cell resolution.

Key Features:

• Zero-Inflated Negative Binomial Regression: Uses zero-inflated negative binomial regression to model sparsity and over-dispersion in single-cell RNA sequencing data.
• Identification of eQTLs: Identifies associations between genotypes and gene expression phenotypes at single-cell resolution.
• Distinguishing Gene-Expression Differences: Differentiates between two types of gene-expression differences across genotype groups.
• Versatility with Grouping Factors: Detects gene expression variation associated with other biological grouping factors such as cell lineages or cell types.

Scientific Applications:

• Gene regulation: Analyzes genetic effects on gene regulation and expression variability at single-cell resolution.
• Cellular heterogeneity: Characterizes genetic contributions to cellular heterogeneity within complex tissues.
• Developmental biology: Applies to developmental biology for studying genotype–expression relationships across lineages.
• Cancer research: Dissects genotype-associated expression differences among tumor cell subpopulations.
• Systems genetics: Maps eQTLs across heterogeneous cell populations to inform systems-level genetic analyses.

Methodology:

SCeQTL applies zero-inflated negative binomial regression to accommodate excess zeros and over-dispersion in single-cell RNA sequencing data for robust eQTL detection.