FastGxC: A powerful and computationally efficient software for context-specific eQTL mapping in single-cell omics data
FastGxC was originally developed for single-cell data, where each individual contributes gene expression measurements across multiple cell types.
However, it can also be applied to bulk RNA-seq data when the same individuals are profiled across multiple tissues or conditions.
In both settings, FastGxC models repeated samples from each individual, removing shared noise and enabling more accurate detection of context-specific genetic effects.
FastGxC is also robust to missing data —for example, when certain individuals or genes are missing in some cell types or tissues.
Please read the BioRxiv preprint for more details.
Please see the Wiki for a tutorial on how to install and run FastGxC on simulated and real data.
Brunilda Balliu - original R implementation of FastGxC; Lena Krockenberger - integration of FastGxC R implementation and simulation code into R-package format; Anthony Zhao - treeBH integration; Charlie Wang - tensorQTL implementation and Wiki tutorial;