{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stratification-of-patient-trajectories-using","title":"Stratification of patient trajectories using covariate latent variable models","arxiv_id":"1610.08735","date":"2016-10-27","proceeding":null,"authors":["Kieran R. Campbell","Christopher Yau"],"abstract":"Standard models assign disease progression to discrete categories or stages\nbased on well-characterized clinical markers. However, such a system is\npotentially at odds with our understanding of the underlying biology, which in\nhighly complex systems may support a (near-)continuous evolution of disease\nfrom inception to terminal state. To learn such a continuous disease score one\ncould infer a latent variable from dynamic \"omics\" data such as RNA-seq that\ncorrelates with an outcome of interest such as survival time. However, such\nanalyses may be confounded by additional data such as clinical covariates\nmeasured in electronic health records (EHRs). As a solution to this we\nintroduce covariate latent variable models, a novel type of latent variable\nmodel that learns a low-dimensional data representation in the presence of two\n(asymmetric) views of the same data source. We apply our model to TCGA\ncolorectal cancer RNA-seq data and demonstrate how incorporating\nmicrosatellite-instability (MSI) status as an external covariate allows us to\nidentify genes that stratify patients on an immune-response trajectory.\nFinally, we propose an extension termed Covariate Gaussian Process Latent\nVariable Models for learning nonparametric, nonlinear representations. An R\npackage implementing variational inference for covariate latent variable models\nis available at http://github.com/kieranrcampbell/clvm.","url_abs":"http://arxiv.org/abs/1610.08735v2","url_pdf":"http://arxiv.org/pdf/1610.08735v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stratification-of-patient-trajectories-using","repo_url":"https://github.com/kieranrcampbell/clvm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}