A Computational Pipeline for Stratifying Autoimmune Patients Using Binary Antibody Data

Abstract

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: O_LIModule 1: Exploratory screening using statistical association tests. C_LIO_LIModule 2: Quantification of overall immunological similarity and un-certainty. C_LIO_LIModule 3: Prediction modeling and validation against chance. C_LI We demonstrate the methods 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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/670596v4_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@7c6df3org.highwire.dtl.DTLVardef@116ac5eorg.highwire.dtl.DTLVardef@18e76dcorg.highwire.dtl.DTLVardef@1d6b71_HPS_FORMAT_FIGEXP M_FIG C_FIG