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 two-way 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.
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