A Computational Pipeline for Stratifying Autoimmune Patients Using Binary Antibody Data
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This method describes a computational pipeline for stratifying autoimmune patient groups using exclusively binary autoantibody data. Our method addresses a methodological gap in computational immunology by providing a standardized framework for analyzing categorical serological data commonly found in electronic health records and resource-limited settings. The pipeline integrates three complementary analytical modules:
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Module 1: Exploratory screening using statistical association tests.
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Module 2: Quantification of overall immunological similarity and un-certainty.
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Module 3: Prediction modeling and validation against chance.
We demonstrate the method’s utility by applying it to two autoimmune disorders. We were successful in recapitulating established clinical relationships in these two closely linked diseases. The pipeline is implemented in Python and includes detailed configuration options for custom disease groups, autoanti-body panels and stratification variables. This method enables researchers to extract meaningful immunological patterns from underutilized binary clinical data, serving as a hypothesis-generation tool to help drive impactful exploration.
