Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

Abstract

Oligonucleotide arrays can provide a broad picture of the state of the cell, by monitoring the expression level of thousands of genes at the same time. It is of interest to develop techniques for extracting useful information from the resulting data sets. Here we report the application of a two-way clustering method for analyzing a data set consisting of the expression patterns of different cell types. Gene expression in 40 tumor and 22 normal colon tissue samples was analyzed with an Affymetrix oligonucleotide array complementary to more than 6,500 human genes. An efficient twoway clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues. Coregulated families of genes clustered together, as demonstrated for the ribosomal proteins. Clustering also separated cancerous from noncancerous tissue and cell lines from in vivo tissues on the basis of subtle distributed patterns of genes even when expression of individual genes varied only slightly between the tissues. Two-way clustering thus may be of use both in classifying genes into functional groups and in classifying tissues based on gene expression.

Reliable classification of two-class cancer data using evolutionary algorithms

“…The Colon data set is a collection of 62 expression measurements from Colon biopsy samples reported elsewhere (Alon et al, 1999). It contains 22 normal and 40 Colon cancer samples.…”

“…DNA microarrays (Gershon, 2002) can be used to determine which genes are being expressed in a given cell type at a particular time and under particular conditions, to compare the gene expression in two different cell types or tissue samples, to examine changes in gene expression at different stages in the cell cycle and to assign probable functions to newly discovered genes with the expression patterns of known genes. Moreover, the DNA microarray provides a global perspective of gene expression levels, which can be used in gene clustering tools (Alon et al, 1999), tissue classification methods (Golub et al, 1999), identification of new targets for therapeutic drugs (Clarke et al, 1999), etc. However, the important issue in such problems is that the availability of only a few samples compared to the large number of genes, a matter which makes the classification task more difficult.…”

Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma

“…Large-scale transcriptional profiling has so far been reported in multiple cancers including hepatocellular carcinoma. [17][18][19][20] With regard to hepatocellular carcinoma, most microarray studies have emphasized on determining the profiles that allow differentiation of stepwise progressions from the preneoplastic lesions and in defining expression patterns associated with clinical subsets such as the prediction of drug resistance. 21 Information on gene expression changes within regions of aberrant genomic loci, on the other hand, has been minimal.…”

Microarray profiling of skeletal muscle tissues from equally obese, non-diabetic insulin-sensitive and insulin-resistant Pima Indians

“…Global gene expression or transcriptional profiling has been used to identify molecular markers for various pathological states [9,10,11]. In this study we used global gene expression using oligonucleotide microarrays that included transcripts of known genes and Expressed Sequence Tags (ESTs) to identify potentially novel genes in pathways that are dysregulated at the transcript level in the skeletal muscle tissue of insulin-resistant non-diabetic Pima Indians.…”