Aug 11, 2018

Optimized Threshold Inference for Partitioning of Clones From High-Throughput B Cell Repertoire Sequencing Data

Frontiers in Immunology
Nima Nouri, Steven H Kleinstein

Abstract

During adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their B cell receptor (BCR), forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clones from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automated method to partition sequences into clonal groups based on single-linkage hierarchical clustering of the BCR junction region with length-normalized Hamming distance metric. This method could identify clonal sequences with high confidence on several benchmark experimental and simulated data sets. However, determining the threshold to cut the hierarchy, a key step in the method, is computationally expensive for large-scale repertoire sequencing data sets. Moreover, the methodology was unable to provide estimates of accuracy for new data. Here, a new method is presented that addresses this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture model fitting procedure for learning the parameters of two univariate curves which fit the bimodal di...Continue Reading

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Mentioned in this Paper

Study
Receptors, Antigen, B-Cell
Sequence Determinations
Receptors, Cell Surface
Finite Element Analysis
B-Lymphocytes
Clone
Sequencing
High Throughput Analysis
Receptors, Immunologic

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