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 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. The package was tested on different repertoire sequencing technologies and datasets. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
NoisET: Noise learning and Expansion detection of T-cell receptors with Python