Inferring direction of associations between histone modifications using a neural processes-based framework
Ananthakrishnan Ganesan,
Denis Dermadi,
Laurynas Kalesinskas,
Michele Donato,
Rosalie Sowers,
Paul J. Utz,
Purvesh Khatri
Affiliations
Ananthakrishnan Ganesan
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Computational and Mathematical Engineering, School of Engineering, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Denis Dermadi
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Laurynas Kalesinskas
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Michele Donato
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Rosalie Sowers
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Paul J. Utz
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Division of Immunology and Rheumatology, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
Purvesh Khatri
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; Corresponding author
Summary: Current technologies do not allow predicting interactions between histone post-translational modifications (HPTMs) at a system-level. We describe a computational framework, imputation-followed-by-inference, to predict directed association between two HPTMs using EpiTOF, a mass cytometry-based platform that allows profiling multiple HPTMs at a single-cell resolution. Using EpiTOF profiles of >55,000,000 peripheral mononuclear blood cells from 158 healthy human subjects, we show that neural processes (NP) have significantly higher accuracy than linear regression and k-nearest neighbors models to impute the abundance of an HPTM. Next, we infer the direction of association to show we recapitulate known HPTM associations and identify several previously unidentified ones in healthy individuals. Using this framework in an influenza vaccine cohort, we identify changes in associations between 6 pairs of HPTMs 30 days following vaccination, of which several have been shown to be involved in innate memory. These results demonstrate the utility of our framework in identifying directed interactions between HPTMs.
