NoisET: Noise Learning and Expansion Detection of T-Cell Receptors.
J Phys Chem A
; 126(40): 7407-7414, 2022 Oct 13.
Article
en En
| MEDLINE
| ID: mdl-36178325
ABSTRACT
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability:
NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Receptores de Antígenos de Linfocitos B
/
Receptores de Antígenos de Linfocitos T
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Phys Chem A
Asunto de la revista:
QUIMICA
Año:
2022
Tipo del documento:
Article