Classification of the states of human adaptive immune systems by analyzing immunoglobulin and t cell receptors using immunexplorer

Abstract

The behavior and actions of the human adaptive immune system and its key players, namely B and T cells, are often hard to understand in their entirety. We here present a workflow for modelling the states of adaptive immune systems by analyzing B and T cell receptor repertoires using next-generation sequencing data. For our workflow, we have blood and kidney tissues from diseased patients, who suffered from different kidney diseases (e.g., renal carcinoma), and healthy proband. A set of features based on clonal expansion and diversity of immunoglobulins and T cell receptor next-generation sequencing data, isolated from patients, are calculated. Using different machine learning methods such as support vector machines, random forests, artificial neural networks and genetic programming in HeuristicLab, we are able to classify and distinguish between healthy and diseased individuals up to 80% accuracy using ImmunExplorer.