Variance component mixture modelling for longitudinal T-cell receptor clonal dynamics

Music City Center 

Studies of T cells and their clonally unique receptors have shown promise in elucidating the association between immune response and human disease. Methods to identify T-cell receptor clones which expand or contract in response to certain therapeutic strategies have so far been limited to longitudinal pairwise comparisons of clone frequency with multiplicity adjustment. Here we develop a more general mixture model approach for arbitrary follow-up and missingness which partitions dynamic longitudinal clone frequency behavior from static. While it is common to mix on the location or scale parameter of a family of distributions, the model takes a different approach, mixing on the parameterization itself, the dynamic component allowing for a variable, Gamma-distributed Poisson mean parameter over longitudinal followup, while the static component mean is time invariant. We leverage Gamma-Poisson conjugacy to evaluate the model with respective component posterior predictive distributions and develop an EM-algorithm to estimate the empirical Bayes hyperparameters and component membership. We demonstrate the model in simulation and in a prostate cancer patient cohort.

mixture model

hierarchical model

Bayesian conjugacy

EM algorithm

T-cell receptor 

IMS