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Abstract
There is a great need to develop analytical methodology to analyze and to exploit
the information contained in gene expression data. Because of the large number of
genes and the complexity of biological networks, clustering is a useful exploratory
technique for analysis of gene expression data. Other classical techniques, such as
principal component analysis (PCA), have also been applied to analyze gene expression
data. Using different data analysis techniques and different clustering algorithms
to analyze the same data set can lead to very different conclusions. Our goal is to
study the effectiveness of principal components (PCs) in capturing cluster structure.
Specifically, using both real and synthetic gene expression data sets, we compared
the quality of clusters obtained from the original data to the quality of clusters
obtained after projecting onto subsets of the principal component axes.
Our empirical study showed that clustering with the PCs instead of the original variables
does not necessarily improve, and often degrades, cluster quality. In particular,
the first few PCs (which contain most of the variation in the data) do not necessarily
capture most of the cluster structure. We also showed that clustering with PCs has
different impact on different algorithms and different similarity metrics. Overall,
we would not recommend PCA before clustering except in special circumstances.