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A novel statistical method for decontaminating T-cell receptor sequencing data.

Authors :
Li, Ruoxing
Altan, Mehmet
Reuben, Alexandre
Lin, Ruitao
Heymach, John V
Tran, Hai
Chen, Runzhe
Little, Latasha
Hubert, Shawna
Zhang, Jianjun
Li, Ziyi
Source :
Briefings in Bioinformatics; Jul2023, Vol. 24 Issue 4, p1-8, 8p
Publication Year :
2023

Abstract

The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume 'clean' TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
24
Issue :
4
Database :
Complementary Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
166742682
Full Text :
https://doi.org/10.1093/bib/bbad230