A novel data mining approach for differential genes identification in small cancer expression data

The simple t test is the standard approach for differential gene identification but is not suited to data with low replication. Here, we propose using a multi-scale Gaussian (MSG) to improve the detection accuracy of differential cancerous genes in low replicate microarray experiment. By modelling the gene expression densities as Gaussian scale mixtures, the differential genes are then identified using the estimated density function. We use simulated data and data from GEO to demonstrate that the new algorithm compares favourably to four benchmark algorithms for cancer gene expression data with low replicate.