Progression of oral carcinomas revealed by spectral reordering of a bipartite graph
© Kalna et al; licensee BioMed Central Ltd. 2007
Published: 8 May 2007
Spectral, singular value decomposition-based, algorithms for dimension reduction and clustering are known to be useful in a range of areas of science and engineering. Motivation for this work is in the analysis of microarray data from a number of different samples leading to a natural bipartite graph framework. Spectral bi-partitioning of microarray data was for the first time considered in . In this work we generalise the ideas in  in order to present further theoretical support and investigate a multiclass dataset with the aim of revealing a complex picture of oral cancer progression.
Materials and methods
A simple and informative derivation of a spectral algorithm for reordering a weighted bipartite graph is presented. We start with a discrete optimization problem then add constraints and relax it into a tractable continuous analogue. Natural data preprocessing is a part of the algorithm. Singular vectors can be used not only for gene/sample reordering but also for identifying informative genes. The data set pertains to the gene expression profile of different cell cultures (samples) isolated from normal oral tissue (N) and biopsies from different stages of oral cancer: dysplasias (D), primary (P), metastasised (M) and recurrencent cancers (R).
Immortality is a dominant factor influencing the overall gene expression profiles of both cancers and dysplasias and outcome data related to the different carcinoma cultures indicates that immortality is associated with poorer prognosis. Analysis of an additional data set shows gene expression changes consistently associated with immortality can be identified in vivo as well as in vitro.
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