Abstract:

Association between ion channel functional subtype and its genes expression is important for exploring function of ion channel, annotating function of an unknown subtype and probing into molecular mechanism of ion channel diseases. In this study, we began with noise reduction by standardizing original micro-array data, which consisted of human and mouse gene expression profiles, and then we employed principle component analysis (PCA) together with fuzzy C-mean clustering algorithm to analyze the pre-processed gene expression profiles. PCA is applied to rebuild the feature space of human gene in 21 dimensions as well as the feature space of mouse gene in 26 dimensions. Using this method we largely reduced computational complexity without losing much information involved in the original data. Subsequently, fuzzy C-mean clustering was used to classify the ion channel genes of human and mouse in their reduced feature space. In the end, four ion channel functional subtypes, such as potassium ion channels, calcium ion channel, chloride ion channel, and recep-tor-mediated ion channel were clustered in both human and mouse gene feature space. We applied two statistic ways to conduct significance test of the findings. In one way, we randomly sampled the data for each functional subtype of the ion channel genes and recorded the true positive rate. As a result, in both human and mouse gene feature spaces, genes that belong to one functional subtype were more likely to be clustered together than expected by chance. In the other way, we performed Kappa test and used the functional subtypes as gold standard. The result showed that consistency between the ion channel gene clusters and the ion channel gene subtypes was significantly high for both human and mouse. These results indicate that ion channel genes within the same functional subtype tend to be co-expressed at least at the mRNA-level.