Comparative assessment of physiochemical metrics for the clustering of adaptive immune receptor repertoires

A continued challenge in the analysis of adaptive immune receptor repertoires (AIRRs) is the prediction of antigen reactivity from primary sequence data. Many algorithms infer antigen-specific responses by measuring sequence similarity between receptors. Similarity is often scored using tools for protein alignment such as the BLOSUM matrices. However, these metrics were designed to identify homology in genomic proteins, not VDJ-recombined immune receptors. Comparison of these metrics against other approaches is underexplored. We used matrix factorization to make physiochemical-based alternatives which may improve performance. We evaluated these metrics by clustering 383 simulated and biological repertoires using traditional and physiochemical-based scoring matrices. While physiochemical and traditional scoring had similar efficacy, the membership of antigen-specific clusters varied. Lastly, we inferred antigen-specific immune responses in pancreatic cancer and rheumatoid arthritis. Results varied depending on the matrix used, emphasizing a poor consensus among methods. Despite equivocal performance, physiochemical factors can increase the interpretability of clustered repertoires. These results suggest that analysts must carefully consider characteristics of sequence similarity measures to apply the most appropriate methods to their data. We facilitate further exploration of scoring metrics by centralizing AIRR clustering and physiochemical sequence characterization in a software tool called Homolig (Homol-Ig) associated with our analyses.