This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2006
Research
Significance analysis of microarray transcript levels in time series experiments
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* Corresponding author: Claudio Cobelli cobelli@dei.unipd.it
1 Information Engineering Department, University of Padova, 35131 Padova, Italy
2 Endocrinology Division, Mayo Clinic, Rochester, Minnesota 55905, USA
BMC Bioinformatics 2007, 8(Suppl 1):S10 doi:10.1186/1471-2105-8-S1-S10
Published: 8 March 2007Abstract
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 expressed if the area exceeds a threshold based on a model of the experimental error. These two methods are compared to Method 3, recently proposed in the literature, which exploits splines fit to compare time series profiles. Application of the three methods to synthetic data indicates that Method 2 outperforms the other two both in Precision and Recall when short time series are analyzed, while Method 3 outperforms the other two for long time series.
Conclusion
These results help to address the choice of the algorithm to be used in data-poor time series expression study, depending on the length of the time series.