Workshop on Systems Biology, Fri, 09 Nov, 2007
Speaker: Peter Sykacek
Abstract
Biological assays are often carried out on tissues that contain many
cell lineages and active pathways. Microarray data produced using such
material therefore reflect superimpositions of biological processes.
Analysing such data for shared gene function by means of well matched
assays may help to provide a better focus on specific cell types and
biological processes. The identification of genes that behave similarly in
different biological systems also has the potential to reveal new
insights into preserved biological mechanisms.
This talk proposes a hierarchical Bayesian model allowing
integrated analysis of several microarray data sets for shared gene
function. Each transcript is associated with an indicator variable
that selects whether binary class labels are predicted from expression
values or by a classifier which is common to all transcripts. Each
indicator selects the component models for all involved data sets
simultaneously. A quantitative measure of shared gene function is
obtained by inferring a probability measure over these indicators.
Through experiments on synthetic data we illustrate potential
advantages of this Bayesian approach over a standard method. A shared
analysis of matched microarray experiments covering a) a cycle of
mouse mammary gland development and b) the process of in vitro
endothelial cell apoptosis is proposed as a biological gold standard.
Several useful sanity checks are introduced during data analysis and
we confirm the prior biological belief that shared apoptosis events
occur in both systems. We conclude that a Bayesian analysis for
shared gene function has the potential to reveal new biological
insights, unobtainable by other means.
URL: www.ricam.oeaw.ac.at/specsem/ssqbm/participants/abstracts/index.php
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