Differential T cell receptor clonotype abundance analysis with similarity-based counts weighting

Abstract

To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR-seq data. For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such as DESeq2 and edgeR have been developed. However, applying these methods to TCR sequencing (TCR-seq) data presents additional challenges. TCR-seq data is highly sparse, overdispersed, and contains many artificial zeros, which can lead to inflated false-positive rates when using traditional approaches. While non-parametric methods like the Wilcoxon test better control false positives, they may suffer from lower statistical power. To address these issues, we propose a novel pre-processing step for TCR-seq data using network-based local weighting based on TCR sequence similarity. This pre-processing step improves the sensitivity and reduces the false-positive rates of methods like DESeq2 and edgeR while enhancing the power of the Wilcoxon test. Through empirical analysis of both simulated and real datasets, we show that combining our pre-processing step with the Wilcoxon test achieves robust performance, outperforming traditional RNA-seq methods. This simple but powerful approach to TCR-seq analysis could help advance our understanding of adaptive immune responses.