Clustering of gene expression patterns is a well-studied technique for elucidating trends
across large numbers of transcripts and for identifying likely co-regulated genes. Even
the best clustering methods, however, are unlikely to provide meaningful results if too
much of the data is unreliable. With the maturation of microarray technology, a wealth
of research on statistical analysis of gene expression data has encouraged researchers to
consider error and uncertainty in their microarray experiments, so that experiments are
being performed increasingly with repeat spots per gene per chip and with repeat
experiments. One of the challenges is to incorporate the measurement error information
into downstream analyses of gene expression data, such as traditional clustering
In this study, a clustering approach is presented which incorporates both gene expression
values and error information about the expression measurements. Using repeat expression
measurements, the error of each gene expression measurement in each experiment condition
is estimated, and this measurement error information is incorporated directly into the
clustering algorithm. The algorithm, CORE (Clustering Of Repeat Expression data), is
presented and its performance is validated using statistical measures. By using error
information about gene expression measurements, the clustering approach is less sensitive
to noise in the underlying data and it is able to achieve more accurate clusterings.
Results are described for both synthetic expression data as well as real gene expression
data from Escherichia coli and Saccharomyces cerevisiae.
The additional information provided by replicate gene expression measurements is a valuable
asset in effective clustering. Gene expression profiles with high errors, as determined
from repeat measurements, may be unreliable and may associate with different clusters,
whereas gene expression profiles with low errors can be clustered with higher specificity.
Results indicate that including error information from repeat gene expression measurements
can lead to significant improvements in clustering accuracy.