Fundamental patterns underlying gene expression profiles: Simplicity from complexity

Abstract

Analysis of previously published sets of DNA microarray gene expression data by singular value decomposition has uncovered underlying patterns or ''characteristic modes'' in their temporal profiles. These patterns contribute unequally to the structure of the expression profiles. Moreover, the essential features of a given set of expression profiles are captured using just a small number of characteristic modes. This leads to the striking conclusion that the transcriptional response of a genome is orchestrated in a few fundamental patterns of gene expression change. These patterns are both simple and robust, dominating the alterations in expression of genes throughout the genome. Moreover, the characteristic modes of gene expression change in response to environmental perturbations are similar in such distant organisms as yeast and human cells. This analysis reveals simple regularities in the seemingly complex transcriptional transitions of diverse cells to new states, and these provide insights into the operation of the underlying genetic networks.T he recent development of DNA microarray technology has enabled the genome-wide measurement of temporal changes in gene expression levels (1, 2). Analysis of the expression patterns obtained with large gene arrays has revealed the existence of groups or ''clusters'' of genes with similar expression patterns (3-6). Not surprisingly, gene clusters often contain genes that encode proteins required for a common function, and, hence, co-clustering has been helpful in identifying the functions of unknown gene products. However, such cluster analyses provide little insight into the relationships among groups of co-regulated genes or the behavior of biological networks as a whole.In this paper, we report the results of subjecting several large published gene expression data sets to singular value decomposition (SVD), a standard and straight-forward analytic procedure. We show that highly complex sets of gene expression profiles can be represented by a small number of ''characteristic modes'' that capture the temporal patterns of gene expression change. These modes are somewhat analogous to the characteristic vibration modes of a tuned violin string. The tone produced by the vibrating string can be entirely specified by the contributions of its characteristic vibration modes. We show here that a gene expression profile, similarly, can be precisely represented by specifying the magnitude and sign of the contribution of each of its characteristic modes. This type of ''spectral'' analysis yields a hierarchical interpretation of the expression data and provides insights into the nature and behavior of genetic networks. MethodsThe mathematical analysis is carried out straightforwardly by using SVD (7). The gene expression data of n genes, each measured at m discrete time points, may be written as an n ϫ m matrix, A. Following the procedures outlined in ref. 6, we have polished the data by requiring that the rows and columns have a zero mean by subtracting the mean values of the raw...

Molecular characterization of adenocarcinoma and squamous carcinoma of the uterine cervix using microarray analysis of gene expression

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“…Quantitative simulation of these time dependent gene expression patterns meets with difficulties due to incomplete knowledge of the underlying regulatory mechanisms. However, statistical methods have been successfully applied, such as cluster analysis of time-dependent gene expression patterns for identifying functionally related proteins [5][6][7][8][9][10].It has been stressed that even without detailed knowledge of gene regulatory mechanisms phenotype properties can be rationalized by evolutionary optimization principles [11]. The basis of this approach is the hypothesis that a permanent change of phenotype properties due to mutation and selection leads to an optimal adaptation of an organism to given environmental conditions.…”

“…Quantitative simulation of these time dependent gene expression patterns meets with difficulties due to incomplete knowledge of the underlying regulatory mechanisms. However, statistical methods have been successfully applied, such as cluster analysis of time-dependent gene expression patterns for identifying functionally related proteins [5][6][7][8][9][10].…”