Two-sample inference for sequence-derived feature distributions from deep sequencing data

Abstract

Many biological contexts involve heterogeneous populations of related molecular variants within a single individual. For example, some viral infections such as HIV-1 exhibit substantial diversity within a single host. Advances in sequencing technology now make it possible to characterize such populations by generating up to thousands of genetic sequences from a biological sample. In many settings, each sequence can be mapped to a numeric feature, such as Hamming distance to a reference sequence. This collection of feature values defines a one-dimensional distribution that characterizes individual-level viral diversity. When comparing these features across independent groups, past analyses have typically reduced each individual's data to a single summary value, such as the mean or mode. We propose a framework that retains the full individual-level distribution as the object of analysis. Drawing on existing tools from distributional data analysis, our framework uses Wasserstein barycenters to summarize the observed individual-level distributions within groups into a single representative distribution. These group barycenters are then compared using differences in quantiles and tested for equality. A practical challenge is that the number of sequences obtained, known as sequencing depth, can vary across biological samples. Through simulation studies, we examine when low sequencing depth can distort inference and present practical strategies for addressing these issues. We then illustrate the framework using data from the HIV-1 Antibody Mediated Prevention efficacy trials and a human B cell receptor sequencing study of vaccine-elicited immune responses. By using the entire collection of sequences for each individual, our approach can reveal group differences that may be obscured by single-number summary analyses.