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DTSTAMP:20250808T055046Z
DESCRIPTION:Title: Multi-Study Approaches for Assessing Reproducibility.\n
 \nAbstract: https://rdevito.github.io/web/\n\n\nBiostatistics and computat
 ional biology are increasingly facing the urgent challenge of efficiently 
 dealing with a large amount of experimental data. In particular\, high-thr
 oughput assays are transforming the study of biology\, as they generate a 
 rich\, complex\, and diverse collection of high-dimensional data sets. Thr
 ough compelling statistical analysis\, these large data sets lead to disco
 veries\, advances and knowledge that were never accessible before\, via co
 mpelling statistical analysis. Building such systematic knowledge is a cum
 ulative process which requires analyses that integrate multiple sources\, 
 studies\, and technologies. The increased availability of ensembles of stu
 dies on related clinical populations\, technologies\, and genomic features
  poses four categories of important multi-study statistical questions: 1) 
 To what extent is biological signal reproducibly shared across different s
 tudies? 2) How can this global signal be extracted? 3) How can we detect a
 nd quantify local signals that may be masked by strong global signals? 4) 
 How do these global and local signals manifest differently in different da
 ta types?\n	\n	We will answer these four questions by introducing novel clas
 ses of methodologies for the joint analysis of different studies. The goal
  is to separately identify and estimate 1) common factors reproduced acros
 s multiple studies\, and 2) study-specific factors. We present different m
 edical and biological applications. In all the cases\, we clarify the bene
 fits of a joint analysis compared to the standard methods. Our methods cou
 ld accelerate the pace at which we can combine unsupervised analysis acros
 s different studies\, and understand the cross-study reproducibility of si
 gnal in multivariate data.\n\n \n\nZoom: https://mcgill.zoom.us/j/84499453
 174?pwd=dVc5RkYreVlpV3BnQjNhU244VzJoQT09\n\n \n\nSite web : www.mcgill.ca/
 epi-biostat-occh/news-events/seminars/biostatistics\n\n \n
DTSTART:20220209T203000Z
DTEND:20220209T213000Z
SUMMARY:Roberta De Vito (Brown University)
URL:/mathstat/channels/event/roberta-de-vito-brown-uni
 versity-337391
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