缅北强奸

Title: Inference of perturbed immune repertoire dynamics using high-throughput sequencing.

Abstract: High-throughput sequencing provides access to expression-level detail of cell populations. Identifying signal in this data is nevertheless a challenge: intrinsic variability, vast subsampling, and indirect access together make difficult the reliable and accurate inference of the changes in population size due to environmental perturbations. Here, in the context of antigen-perturbed immune cell repertoires, we present a generative model of observed sequence count pairs. For pairs of replicates, our model captures the natural variability in the system, giving reproducible behaviour across donors. Using the replicate model as a baseline, we then learn the parameters of a prior distribution of the ratio of a clone's frequency pair for pairs of repertoires sampled at different time points. After validating the model on synthetic repertoire data, we infer the posterior distribution of this ratio for every observed clone. Finally, we use point estimates obtained from the posterior to identify candidate responding clones, which are confirmed in a subsequent functional assay. Our method can be used in the clinic to track disease-specific clones expanding or contracting after infection, vaccination, or therapy.