Robust Estimates of Overall Immune-Repertoire Diversity from High-Throughput Measurements on Samples

Abstract

The diversity of a persons B- and T-cell repertoires is both clinically important and a key measure of immunological complexity. However, diversity is hard to estimate by current methods, due to inherent uncertainty in the number of B- and T-cell clones that will be missing from a blood or tissue sample by chance (the missing-species problem), inevitable sampling bias, and experimental noise. To solve this problem we developed Recon, a modified maximum-likelihood method that outputs the overall diversity of a repertoire from measurements on a sample. Recon outputs accurate, robust estimates by any of a vast set of complementary diversity measures, including species richness and entropy, at fractional repertoire coverage. It also outputs error bars and power tables, allowing robust comparisons of diversity between individuals and over time. We apply Recon to in silico and experimental immune-repertoire sequencing datasets as proof of principle for measuring diversity in large, complex systems.