On finding and interpreting patterns in gene expression data from time course experiments

Abstract

Microarrays are being widely used for studying gene activity throughout a cell cycle. A common aim is to find those genes that are expressed during specific phases in the cycle. The challenges lie in the extremely large number of genes being measured simultaneously, the relatively short length of the time course studied and the high level of noise in the data. Using a well-known yeast cell cycle data set, we compare a method being used for finding genes following a periodic time series pattern with a method for finding genes having a different phase pattern during the cell cycle. Application of two visualisation tools gives insight into the interpretation of the patterns for the genes selected by the two approaches. It is recommended that (i) more than a single approach be used for finding patterns in gene expression data from time course experiments, and (ii) visualisation be used simultaneously with computational and statistical methods to interpret as well as display these patterns.