  Clustering of gene expression time series gives insight into which genes may
be coregulated, allowing us to discern the activity of pathways in a given
microarray experiment. Of particular interest is how a given group of genes
varies with different model conditions or genetic background. Amyotrophic
lateral sclerosis (ALS), an irreversible diverse neurodegenerative disorder
showed consistent phenotypic differences and the disease progression is
heterogeneous with significant variability. This paper demonstrated about
finding some significant gene expression profiles and its associated or
co-regulated cluster of gene expressions from four groups of data with
different genetic background or models conditions. Gene enrichment score
analysis and pathway analysis of judicially selected clusters lead toward
identifying features underlying the differential speed of disease progression.
Gene ontology overrepresentation analysis showed clusters from the proposed
method are less likely to be clustered just by chance. In this paper, we
develop a new clustering method that allows each cluster to be parameterised
according to whether the behaviour of the genes across conditions is correlated
or anti-correlated. Our proposed method unveil the potency of latent
information shared between multiple model conditions and their replicates
during modelling gene expression data.

Quantitative Methods