  Motivation: Clustering techniques are routinely applied to identify patterns
of co-expression in gene expression data. Co-regulation, and involvement of
genes in similar cellular function, is subsequently inferred from the clusters
which are obtained. Increasingly sophisticated algorithms have been applied to
microarray data, however, less attention has been given to the statistical
significance of the results of clustering studies. We present a technique for
the analysis of commonly used hierarchical linkage-based clustering called
Significance Analysis of Linkage Trees (SALT).
  Results: The statistical significance of pairwise similarity levels between
gene expression profiles, a measure of co-expression, is established using a
surrogate data analysis method. We find that a modified version of the standard
linkage technique, complete-linkage, must be used to generate hierarchical
linkage trees with the appropriate properties. The approach is illustrated
using synthetic data generated from a novel model of gene expression profiles
and is then applied to previously analysed microarray data on the
transcriptional response of human fibroblasts to serum stimulation.
