Code and Results for: Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping

This repository contains analysis code, input data, functional genomic annotations, and derived results from the jaxQTL study. See jaxQTL paper: Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping. The preprint is on medRxiv: https://www.medrxiv.org/content/10.1101/2025.01.18.25320755v2 Files are organized into four archives to facilitate reproducibility and selective downloading. jaxQTL_analysis.tar.gzAnalysis scripts used to reproduce the analyses and figures in the jaxQTL study, including cis-eQTL mapping, simulations, replication, fine-mapping, colocalization, functional enrichment, mashr, S-LDSC, and computational benchmarking analyses. The code is also available on https://github.com/mancusolab/jaxqtl_analysis). jaxQTL_data_inputs.tar.gzInput data used in the jaxQTL analyses, including OneK1K metadata, external eQTL and GWAS summary statistics, gene information, and other input datasets used for downstream analyses. jaxQTL_data_annotation.tar.gzFunctional genomic annotation data used in the jaxQTL study, including cell-type-specific ENCODE epigenomic tracks, promoter capture Hi-C data, and other regulatory annotations used for functional enrichment and interpretation of sc-eQTLs. jaxQTL_results.tar.gzDerived results from the jaxQTL analyses, including cis-eQTL mapping, simulations, fine-mapping, colocalization, replication, functional enrichment, S-LDSC, and other downstream analysis outputs used to generate the results and figures in the manuscript.