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SUMMARY:Modelling heterogeneity in gene expression using the matrix-variat
 e  normal distribution - Anestis Touloumis\,  EBI\, Cambridge
DTSTART:20121002T133000Z
DTEND:20121002T143000Z
UID:TALK39604@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:In this talk\, we consider the problem of modeling gene expres
 sion levels \nwhen measurements are made on multiple samples taken from th
 e same \nindividual. The primary biological goal is to assess the heteroge
 neity \nof the expression levels within each individual and\, subsequently
 \, \nbetween all individuals. This type of data may arise in cancer studie
 s \nwith multiple subsamples taken from each tumor. From a statistical \np
 erspective our primary concern is the accurate estimation of the \nassocia
 tion structure of the correlated samples and of the genes. For this purpos
 e\, we apply the matrix-variate normal distribution\, which allows us to e
 stimate separately the covariance/correlation matrix among the correlated 
 subsamples and the genes. We derive covariance estimators and discuss thei
 r properties\, and we develop test statistics for the sphericity and ident
 ity hypothesis testing of the covariance matrices. Finally\, we illustrate
  the above using a real data set from a cancer study. This is a joint work
  with John Marioni and Simon Tavaré. \n
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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