BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260616T042941Z UID:db28621b-76e0-4069-86c3-713799f0c0da DTSTART:20190916T090000Z DTEND:20190916T170000Z DESCRIPTION:Educators:\nOliver Stegle (HD-HuB)\n\nDate:\n16.09.2019\n\nLoca tion:\nHeidelberg\, Germany\n\nContents:\nThis tutorial provides an introd uction to Multi-Omics Factor Analysis (MOFA)\, a novel unsupervised framew ork for the integration of multi-omic data sets (Argelaguet et al\, Molecu lar Systems Biology. 2018). Intuitively\, MOFA can be viewed as a versati le and statistically rigorous generalization of principal component analys is to multi-omics data. Given multiple ‘omics data types on overlapping sets of samples\, MOFA infers a low-dimensional data representation in ter ms of (hidden) factors. These learnt factors represent the driving sources of variation across data modalities\, thus facilitating the identificatio n of molecular phenotypes and disease subgroups.\nIn the first part of the tutorial I will give a 30-minute presentation to explain the model\, its applications and limitations. The second part will consist on a hands-on a ctivity where we will use two real-case data sets to show how MOFA can be used for integrative analysis. The first data set will be a large study of blood cancer patients (Dietrich\, J Clin Invest. 2018)\, and the second w ill be a single-cell multi-omics data set (Angermueller\, Nature Methods. 2016). The attendants are also encouraged to bring their own multi-omics d ata sets.\n\nKeywords:\nMOFA\, R\,\n\nTools:\nMOFA\, R\,\n\nPrerequisites: \nA working knowledge of R is expected.\n\nThe tutorial requires the insta llation of the following software:\n\n• R>\;=3.4 + Rstudio\n• Python >\;=2.7\n• MOFA R package (+ dependencies)\n• MOFAdata R package (+ dependencies)\n• mofapy python package (+ dependencies) LOCATION:Heidelberg SUMMARY:MOFA Workshop - GCB 2019 URL;VALUE=URI:https://www.denbi.de/training/684-mofa-workshop-gcb-2019 END:VEVENT END:VCALENDAR