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Paper text:
a variety of high - throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes .
one challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time .
we propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays .
we demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions .
this general strategy can be applied in the analysis of data sets of thousands of genes under different conditions .
a variety of high - throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes . 
 common methods for analysis of these large data sets can be problematic . 
 one challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time . 
 we propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays . 
 we demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions . 
 this general strategy can be applied in the analysis of data sets of thousands of genes under different conditions .