Dear all!

Please note this announcement of the next meeting of the Highlands Local
RSS group, which will take place in Aberdeen in 2 weeks.

As always everybody is welcome!

Claus (local group secretary)

------------------------------------------------------------------------


SPEAKER: Natalia Bochkina (Edinburgh)

TITLE: Objective and subjective approaches to analysis of differential
expression in microarray data (joint work with Alex Lewin and Sylvia
Richardson)

DATE: 30 October 2007
TIME: 4pm (Tea from 3:45)
VENUE: Strathcona Lecture Theatre, Rowett Research Institute, Greenburn
Road, AB21 9SB (See details at:
http://www.rowett.ac.uk/institute/location.html
The Strathcona lecture theatre can be found within Strathcona Hall. This
is the building to your right (opposite from the Reid
Library/Reception), when you come to Greenburn Road from the A96.)

ABSTRACT:
We consider a problem of comparing the means in two conditions (such as
disease versus healthy samples) for a large number of variables
simultaneously given a small number of replicates, with motivation
coming from analysis of gene expression data. Bayesian modelling of such
data allows to "borrow strength" across variables to stabilise variance
estimates given a small number of replicates, by using an exchangeable
hierarchical prior for variable-specific variances.

With regard to the mean, with consider two types of priors:
noninformative ("objective") and mixture priors.  To define a mixture
prior, we use prior knowledge that there are 3 groups of variables: with
equal means (the null hypothesis), with the mean in condition 1 greater
than the mean in condition 2 (overexpressed) and with the reverse
(underexpressed). Thus, we use a three component mixture distribution to
model the difference between the means, with components being an atom at
zero and gamma distributions with support on positive and on negative
semiline. We study sensitivity to the choice of prior on simulated data
and compare models with different priors using posterior predictive checks.

Since the mixture model can be sensitive to the choice of prior, we also
consider a Bayesian model with noninformative prior for the means. To
find variables with unequal means, we propose adaptive interval
hypothesis testing where the interval depends on variability of each
variable. Since we are testing a large number of hypotheses
simultaneously, we also propose an estimator of the false discovery rate
which allows to control the number of false positives. The adaptive
interval hypothesis testing can be extended to compound hypotheses, i.e.
those involving more than one parameter.

These approaches will be illustrated on gene expression data sets
produced by BAIR consortium (www.bair.org.uk).

-- 
***********************************************************************************
  Dr Claus-D. Mayer                    | http://www.bioss.ac.uk
  Biomathematics & Statistics Scotland | email: [log in to unmask]
  Rowett Research Institute            | Telephone: +44 (0) 1224 716652
  Aberdeen AB21 9SB, Scotland, UK.     | Fax: +44 (0) 1224 715349
***********************************************************************************