A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data.
Bioinformatics
; 40(7)2024 Jul 01.
Article
em En
| MEDLINE
| ID: mdl-38950175
ABSTRACT
MOTIVATION 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. RESULTS:
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. AVAILABILITY AND IMPLEMENTATION 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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Receptores de Antígenos de Linfócitos T
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos