Clustering-Based Techniques for Big Data Analysis of Gene Expression

Abstract

Proper investigation of cancer has always been of foremost importance for its accurate forecasting, thereby aiding the correct cure. Microarray-based gene expression profiling is being practised for this purpose making it one of the leading research interests for discovering gene clusters accountable for a particular behavior. Big data analytics provides an efficient way to seek facts about the biological processes inherent from this microarray data. Previously, many attempts have been made to achieve this using numerous clustering approaches, but the results were quite deviating from the reality. In this work, we have attempted to discover potential and accurate gene indicators from the gene expression data by using a well-known quantitative measure called quantum clustering. The characteristic feature of this concept is that the total estimate of clusters formed is not predetermined but is determined depending on the nature of the data. As the concept is established on the grounds that a cluster is formed by density wise spaces, where the center is formed based on the density maxima point, this motivated us to detect those clusters which may be engaged in a certain biological process. The clustering approach becomes privileged in that extremely dense spaces are inherently detected and combined to produce arbitrarily shaped clusters without regarding the dimension of the space. For the purpose of comparing the results obtained, we have also applied a non-parametric measure, namely, the mean shift clustering on the gene expression data. For validation purpose, we used DAVID to check the significance of the clusters created. Results show that the genes so discovered are highly indicative in the pursuit of rare diseases.