{"id":2918,"date":"2018-09-16T20:00:03","date_gmt":"2018-09-17T00:00:03","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2?page_id=2918&#038;preview_id=2918"},"modified":"2020-10-13T17:36:53","modified_gmt":"2020-10-13T21:36:53","slug":"l2r2","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/","title":{"rendered":"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies"},"content":{"rendered":"<div id=\"content1\">\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\">\n<p>L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm.<\/p>\n<p><a href=\"https:\/\/github.com\/BIG-S2\/L2R2\" target=\"new\" rel=\"noopener noreferrer\">Download<\/a><\/p>\n<p><strong>Citation<\/strong>:\u00a0Hongtu Zhu, Zakaria Khondker, Zhaohua Lu and Joseph G. Ibrahim. Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers. Journal of the American Statistical Association. 2014; 109 (507) 977-990.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"footer\">\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<p><!-- footer ends--><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The &hellip; <a href=\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/\" aria-label=\"Read more about L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies\">Read more<\/a><\/p>\n","protected":false},"author":55348,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2918","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The &hellip; Read more\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/\" \/>\n<meta property=\"og:site_name\" content=\"BIG-S2\" \/>\n<meta property=\"article:modified_time\" content=\"2020-10-13T21:36:53+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/\",\"name\":\"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2\",\"isPartOf\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\"},\"datePublished\":\"2018-09-17T00:00:03+00:00\",\"dateModified\":\"2020-10-13T21:36:53+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.med.unc.edu\/bigs2\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/\",\"name\":\"BIG-S2\",\"description\":\"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2","og_description":"L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The &hellip; Read more","og_url":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/","og_site_name":"BIG-S2","article_modified_time":"2020-10-13T21:36:53+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/","url":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/","name":"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies - BIG-S2","isPartOf":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/#website"},"datePublished":"2018-09-17T00:00:03+00:00","dateModified":"2020-10-13T21:36:53+00:00","breadcrumb":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.med.unc.edu\/bigs2\/l2r2\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.med.unc.edu\/bigs2\/l2r2\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.med.unc.edu\/bigs2\/"},{"@type":"ListItem","position":2,"name":"L2R2: Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies"}]},{"@type":"WebSite","@id":"https:\/\/www.med.unc.edu\/bigs2\/#website","url":"https:\/\/www.med.unc.edu\/bigs2\/","name":"BIG-S2","description":"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links_to":[],"_links_to_target":[],"_links":{"self":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2918","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/users\/55348"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/comments?post=2918"}],"version-history":[{"count":0,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2918\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/media?parent=2918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}