  We present a new approach for the analysis of genome-wide expression data.
Our method is designed to overcome the limitations of traditional techniques,
when applied to large-scale data. Rather than alloting each gene to a single
cluster, we assign both genes and conditions to context-dependent and
potentially overlapping transcription modules. We provide a rigorous definition
of a transcription module as the object to be retrieved from the expression
data. An efficient algorithm, that searches for the modules encoded in the data
by iteratively refining sets of genes and conditions until they match this
definition, is established. Each iteration involves a linear map, induced by
the normalized expression matrix, followed by the application of a threshold
function. We argue that our method is in fact a generalization of Singular
Value Decomposition, which corresponds to the special case where no threshold
is applied. We show analytically that for noisy expression data our approach
leads to better classification due to the implementation of the threshold. This
result is confirmed by numerical analyses based on in-silico expression data.
We discuss briefly results obtained by applying our algorithm to expression
data from the yeast S. cerevisiae.
