Biology subjects

Grigaityte, K.

Publications and source records attributed to Grigaityte, K..

3 recordsLinked to original sources

bioRxiv2019

Bayesian inference of power law distributions

Observed data from many research disciplines, ranging from cellular biology to economics, often follow a particular long-tailed distribution known as a power law. Despite the ubiquity of natural power laws, inferring the exact form of the distribution from sampled data remains challenging. The possible presence of multiple generative processes giving rise to an unknown weighted mixture of distinct power law distributions in a single dataset presents additional challenges. We present a probabilistic solution to these issues by developing a Bayesian inference approach, with Markov chain Monte Carlo sampling, to accurately estimate power law exponents, the number of mixtures, and their weights, for both discrete and continuous data. We determine an objective prior distribution that is invariant to reparameterization of parameters, and demonstrate its effectiveness to accurately infer exponents, even in the low sample limit. Finally, we provide a comprehensive and documented software package, written in Python, of our Bayesian inference methodology, freely available at https://github.com/AtwalLab/BayesPowerlaw.

bioinformatics↗

bioRxiv2018

T-cell receptor αβ chain pairing is associated with CD4+ and CD8+ lineage specification

While a highly diverse T-cell receptor (TCR) repertoire is the hallmark of a healthy adaptive immune system, relatively little is understood about how the CD4+ and CD8+ TCR repertoires differ from one another. We here utilize high-throughput single T-cell sequencing to obtain approximately 100,000 TCR {beta} chain pairs from human subjects, stratified into CD4+ and CD8+ lineages. We reveal that substantial information about T-cell lineage is encoded by V{beta} gene pairs and, to a lesser extent, by several other TCR features such as CDR3 length and charge. We further find that the strength of association between the {beta} chain and T-cell lineage is surprisingly weak, similar in strength to that of the chain. Using machine learning classifiers to predict T-cell lineage from TCR features, we demon-strate that {beta} chain pairs are significantly more informative than individual chains alone. These findings provide unprecedented insight into the CD4+ and CD8+ TCR repertoires and highlight the importance of {beta} chain pairing in TCR function and specificity.

immunology↗

bioRxiv2017

Single-cell sequencing reveals αβ chain pairing shapes the T cell repertoire

A diverse T cell repertoire is a critical component of the adaptive immune system, providing protection against invading pathogens and neoplastic changes, relying on the recognition of foreign antigens and neoantigen peptides by T cell receptors (TCRs). However, the statistical properties and function of the T cell pool in an individual, under normal physiological conditions, are poorly understood. In this study, we report a comprehensive, quantitative characterization of the T cell repertoire from over 1.9 million cells, yielding over 200,000 high quality paired {beta} sequences in 5 healthy human subjects. The dataset was obtained by leveraging recent biotechnology developments in deep RNA sequencing of lymphocytes via single-cell barcoding in emulsion. We report non-random associations and non-monogamous pairing between the and {beta} chains, lowering the theoretical diversity of the T cell repertoire, and increasing the frequency of public clones shared among individuals. T cell clone size distributions closely followed a power law, with markedly longer tails for CD8+ cytotoxic T cells than CD4+ helper T cells. Furthermore, clonality estimates based on paired chains from single T cells were lower than that from single chain data. Taken together, these results highlight the importance of sequencing {beta} pairs to accurately quantify lymphocyte receptor diversity.

immunology↗