Statistics Seminar: Peter Thomson -- Microarray analysis: a systematic approach using mixed-models and finite-mixtures

Peter C.  Thomson Veterinary Biometry, Faculty of Veterinary Science University of
Sydney 

Location: Carslaw 173 

Time: 2pm Friday, August 12, 2011 

Microarray analysis: a systematic approach using mixed-models and finite-mixtures 

Abstract: Microarrays measure the expression levels of many thousands of genes
simultaneously, and these measurements are usually perfumed on different physiological
states (e.g.  tissue types, times, strains, etc.).  A frequent goal is then to determine
which genes are differentially expressed (DE) as opposed to those that are not
differentially expressed (non-DE).  Genes are classified as being DE if their expression
levels differ "significantly" between two (or more) states, or show "unusual" behaviour
in a particular state.  A common approach is to consider one gene at a time (e.g.
t-tests or ANOVA).  However this poses a multiple testing problem which may in part be
overcome by false discovery rate (FDR) control.  

An alternative approach is to develop a model for the evaluation of all the gene
features simultaneously, and proceed as a model fitting rather than a hypothesis testing
process.  For this method, a two-stage analysis is performed.  (1) All the normalised
expression level data are analysed simultaneously using a large-scale linear mixed
model.  This model includes fixed effects to describe the physical design of the
microarrays, as well as random effects to describe the overall effects of genes (G) and
the effects of genes in different states (G.S).  (2) A two-component mixture model (DE
and non-DE) is then fitted to the BLUPs of the G.S effects.  The mixture model is fitted
using the E-M algorithm, and the process returns posterior probabilities of individual
genes being DE.  These posterior probabilities are used to classify which genes are DE,
and an FDR-type strategy may be applied to these.  However, further refinements to this
process can be made by a simultaneous fitting of a mixture of linear mixed models.  

This method will be illustrated by the analysis of a large-scale microarray study of
lactation in the tammar wallaby.