Multivariate multi-scale Gaussian for microarray unsupervised classification

Unsupervised classification has been one of the most popular machine learning approaches for biological data analysis such as microarray gene expression data. The simplest statistical/machine learning approach for analyzing microarray gene expression data is to predict differentially expressed genes (DEGs), which is a two-class unsupervised classification process. When there are only two experimental conditions, t test or modified t test are the matured approaches to solve the problem. However when there are more than two experimental conditions, t test or modified t test can only be used to predict homogeneously DEGs. A homogeneously DEG shows the same regulation direction across multiple stages. A heterogeneously DEG shows different regulation directions. Multivariate unsupervised classification is then an option. Bayesian clustering algorithm (mclust) has been widely used for unsupervised classification of biological data. However it is extremely slow and is less reliable when the number of stages is small. Here we introduce a similar machine learning algorithm referred to as multivariate multi-scale Gaussian (MSG) as an alternative to mclust. We show that MSG is more accurate and faster. We have validated MSG using both simulated and real data.