A new DP algorithm for comparing gene expression data using geometric similarity

Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities and relationships between genes, based on their expression time series. By representing the time series as polygons and compare them, we can find relationships that are not available when the two time series are compared point-by-point. We applied our algorithm on a dataset of 343 regulatory pairs from the alpha dataset and compared them to randomly generated pairs. Using an SVM classifier, we find the optimal similarity score that separates the regulatory dataset from the random pairs. Our results show that we can detect similar pairs better than simple Pearson correlation and we outperform many of the existing methods. This method is an ongoing approach, that can be applied to finding the similarity of any data that can convert to 2D polygon. In the future, we plan to introduce this method as a new classifier.