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FastGxC is a computationally efficient R package for mapping context-specific eQTLs from repeated expression measurements across multiple contexts (e.g., tissues or cell types). It was originally developed for single-cell RNA-seq data but can also be applied to bulk RNA-seq when individuals are profiled across multiple tissues or conditions.
FastGxC consists of five main steps:
Install the FastGxC R package using devtools::install_github("BalliuLab/FastGxC").
Use simulate_data() to generate synthetic data for testing the pipeline.
The function generates genotype and expression data as well as SNP and gene location files required for FastGxC eQTL mapping.
Decompose gene expression into a context-shared component and context-specific components using decomposition_step().
This step prepares the data for eQTL mapping by separating global from tissue/cell-type-specific expression variation.
Use eQTL_mapping_step() to identify SNP–gene associations.
FastGxC uses MatrixEQTL to map shared and specific eQTLs. Run this function once for shared, and loop over contexts for specific mappings.
Use treeQTL_step() to adjust for multiple testing across all contexts, genes, and SNPs.
FastGxC supports both 3-level and 4-level FDR correction using the TreeQTL package.