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Related Experiment Video

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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

Comparing T cell receptor repertoires using optimal transport.

Branden J Olson^1,2, Stefan A Schattgen³, Paul G Thomas³

¹Department of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Plos Computational Biology

|December 8, 2022

Summary

Comparing T cell receptor (TCR) repertoires is challenging. This study introduces a novel, efficient optimal transport method using Sinkhorn distance and TCRdist to accurately compare TCR repertoires, preserving distributional information.

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Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

T cell receptor (TCR) repertoire comparison is crucial but complex.
Existing methods often lose significant distributional information.
There is a need for advanced, information-preserving comparison techniques.

Purpose of the Study:

To introduce a novel nonparametric approach for comparing empirical TCR repertoires.
To leverage optimal transport methods for robust TCR repertoire analysis.
To provide a simpler, more informative alternative to current comparison strategies.

Main Methods:

Application of the Sinkhorn distance, a fast optimal transport algorithm.
Utilization of TCRdist, a recently developed metric for TCR sequence comparison.
Nonparametric comparison of empirical TCR repertoire distributions.

Main Results:

The proposed method successfully identifies significant differences between distinct TCR distributions.
The approach demonstrates effectiveness across several case studies.
Performance is competitive with more complex existing methods.

Conclusions:

The Sinkhorn distance combined with TCRdist offers an efficient and informative way to compare TCR repertoires.
This method retains valuable distributional information lost in simpler approaches.
The pipeline is computationally efficient and requires minimal modeling assumptions.