Analysis of gene expression profiles: class discovery and leaf ordering

We approach the class discovery and leaf ordering problems using spectral graph partitioning methodologies. For class discovery or clustering, we present a min-max cut hierarchical clustering method and show it produces subtypes quite close to human expert labeling on the lymphoma dataset with 6 classes. On optimal leaf ordering for displaying the gene expression data, we present a sequential ordering method that can be computed in O(n2) time which also preserves the cluster structure. We also show that the well known statistic methods such as F-statistic test and the principal component analysis are very useful in gene expression analysis.

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