# A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data

## TL;DR

This paper introduces a new method to accurately assess T cell receptor diversity in sequencing data by addressing library size differences across samples.

## Contribution

The novel 'multi-bin' rarefying approach improves alpha diversity estimation by partitioning samples and controlling library size confounding.

## Key findings

- The overall rarefying approach fails to control library size confounding effects.
- The multi-bin method achieves better type-I error control and higher statistical power in association tests.
- Simulations using real-world data validate the superiority of the multi-bin approach over existing normalization strategies.

## Abstract

T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed.

In this paper, we develop an innovative “multi-bin” rarefying approach that partitions samples into multiple bins according to their library sizes, conducts rarefying within each bin for alpha diversity calculations, and performs meta-analysis across bins. Extensive simulations using real-world data highlight the inadequacy of the overall rarefying approach in controlling the confounding effect of library size. Our method proves robust in addressing library size confounding, outperforming competing normalization strategies by achieving better-controlled type-I error rates and enhanced statistical power in association tests.

The code is available at https://github.com/mli171/MultibinAlpha. The datasets are freely available at https://doi.org/10.21417/B7001Z and https://doi.org/10.21417/AR2019NC.