Chapter 6: Gene Expression Pattern Discovery

Differentially expressed genes are treated as the major signal to understand how a stress results in a phenomenon change in a species. Scientists hypothesised that it is the genetic signature that ultimately causes the observed phenomenon change. The major objective of gene expression pattern discovery is thus to discover differentially expressed genes. In this chapter, the approaches for gene expression pattern discovery will be introduced for two major types of data, i.e., the microarray gene expression data and the RNA-seq sequencing count data. Gene expression pattern discovery may encounter the challenges of the outlier problem or the subpopulation problem. Therefore this chapter will also introduce the alternatives for gene expression pattern discovery. Moreover, insufficient replicate number is the common issue in gene expression pattern discovery. The dual-scale Gaussian mixture algorithm for discovering differentially expressed genes in a data set with insufficient replicates will be introduced in this chapter.