Statistical Analysis of Gene Expression Data

Abstract

Microarray technology is a useful tool to understand the differences between groups of genes and observing their expressiveness levels. In this thesis, two genes of the tomato plants named colourless non-ripening (CNR) and apetala2 (AP2) over eight different time breakpoints and four different types for each gene have been analysed and studied. Statistical methods are used to link difference(s) in the ripening of the different tomato varieties to the differences in observed patterns of oscillations of CNR and AP2. These methods are Principal Component Analysis (PCA), time series prediction models and k-means clustering. The objective of the research thesis is to understand systematic gene differential expression over time in the tomato ripening process for different tomato varieties. Additionally, the gene expression levels of different mutant types are compared between the two genes. For overall gene comparison, AP2 is a more important gene in tomato ripening experiments as compared to CNR gene.