Summary
Background: Pathway based microarray analysis is an effort to integrate microarray and pathway
data in a holistic analytical approach, looking for coordinated changes in the expression
of sets of genes forming pathways. However, it has been observed that the results
produced are often cryptic, with cases of closely related genes in a pathway showing
quite variable, even opposing expression.
Objectives: We propose a methodology to identify the state of activation of individual pathways,
based on our hypothesis that gene members of many pathways or modules exhibit differential
expression that results from their contribution to any combination of all their constituent
pathways. Therefore, the observed expression of such a gene does not necessarily imply
the activation state of a given pathway where its product participates, but reflects
the net expression resulting from its participation in all its constituent pathways.
Methods: Firstly, in an effort to validate the hypothesis, we split the genes into two groups;
single and multi-membership. We then determined and compared the proportion of differentially
expressed genes in each group, for each experiment. In addition, we estimated the
cumulative binomial probability of observing as many or more expressed genes in each
group, in each experiment, simply by chance. Second, we propose a hill climbing methodology,
aiming to maximise the agreement of gene expression per module.
Results: We detected more frequent expression of multi-membership genes and significantly
lower probabilities of observing such a high proportion of differentially expressed
multi-membership genes, as the one present in the dataset. The algorithm was able
to correctly identify the state of activation of the KEGG glycolysis and gluconeogenesis
modules, using a number of Saccharomyces cerevisiae datasets. We show that the result
is equivalent to the best solution found following exhaustive search.
Conclusions: The proposed method takes into account the multi-membership nature of genes and our
knowledge of the competitive nature of our exemplar modules, revealing the state of
activity of a pathway.
Keywords
Microarray analysis - biochemical pathways - computational biology - computing methodologies
