# Allele-specific immune gene quantification and expression analysis in single-cell RNA-seq data

## TL;DR

This paper introduces a new computational method to analyze immune gene expression at the allele level in single-cell RNA-seq data, offering insights into genetic diversity in immune molecules.

## Contribution

A novel computational methodology for allele-specific expression quantification of immune genes in scRNA-seq data.

## Key findings

- The method enables interactive exploration of immune gene expression across multiple immunogenetic annotation layers.
- Validation on multiple scRNA-seq datasets shows performance across different experimental setups.
- The approach reveals loss of HLA expression in tumor cells and differential HLA allele expression in immune cell subtypes.

## Abstract

Immune molecules such as human leukocyte antigens (HLAs) or killer Ig-like receptors are encoded in the most genetically diverse loci of the human genome. Many of these immune genes exhibit remarkable allelic diversity across populations. Here, we present a novel computational methodology for allele-specific expression quantification of immune genes in single-cell RNA sequencing (scRNA-seq) data. Our quantification method features a novel R/Bioconductor data structure that can handle expression data across multiple immunogenetic annotation layers and enables interactive exploration of immune gene expression. We validate our methodology on multiple scRNA-seq datasets to demonstrate its performance across different experimental setups. We illustrate how these new tools allow us to study loss of HLA expression in tumor cells and to discover differential HLA allele expression in specific immune cell subtypes. By capturing such allele-specific expression patterns and their variation, our methodology offers novel insights into human immunogenomic diversity.