Significance analysis of microarray transcript levels in time series experiments

Background: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over time, a first necessary step is to select differentially expressed transcripts. A variety of methods have been proposed to this purpose; however, these methods are seldom applicable in practice since they require a large number of replicates, often available only for a limited number of samples. In this data-poor context, we evaluate the performance of three selection methods, using synthetic data, over a range of experimental conditions. Application to real data is also discussed. Results: Three methods are considered, to assess differentially expressed genes in data-poor conditions. Method 1 uses a threshold on individual samples based on a model of the experimental error. Method 2 calculates the area of the region bounded by the time series expression profiles, and considers the gene differentially express...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Barbara Di Camillo, Gianna Toffolo, Sreekumaran K. Nair, Laura J. Greenlund, Claudio Cobelli
• Keywords: BMCBI 2007, Large Number, Microarray Time, Time Series