Methods for diversity and overlap analysis in T-cell receptor populations

Area of Science:

• Ecology
• Bioinformatics
• Statistics

Background:

• Analyzing diversity and similarity in biological systems is crucial for understanding ecological and molecular populations.
• Existing statistical methods struggle with high-dimensional data, missing cells, and low counts, common in T-cell receptor (TCR) studies.
• Information-theoretic and geometric approaches offer potential for improved analysis.

Purpose of the Study:

• To develop novel empirical approaches for analyzing diversity and similarity (overlap) in biological and ecological systems.
• To address limitations in analyzing two-way contingency tables with missing data and low counts, inspired by mammalian T-cell receptor (TCR) population studies.
• To propose new diversity and overlap measures that can naturally weight rare and abundant species.

Main Methods:

• Development of new diversity and overlap measures based on information-theoretic and geometric principles.
• Application of Good-Turing sample-coverage correction for consistent estimation.
• Derivation of novel consistent estimates for Shannon entropy and the Morisita-Horn index.

Main Results:

• Proposed novel, consistent estimators for diversity and overlap measures.
• Demonstrated the ability of new measures to up-weight or down-weight rare and abundant species effectively.
• Provided consistent estimates for Shannon entropy and Morisita-Horn index using Good-Turing correction.

Conclusions:

• The novel methods provide accurate and consistent estimates for diversity and overlap in complex biological systems.
• The proposed approaches are particularly valuable for analyzing high-dimensional molecular data, such as T-cell receptor (TCR) populations.
• The study offers improved statistical tools for empirical analysis in ecology and bioinformatics.