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Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data |
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| Authors: | Veronica Vinciotti Xiaohui Liu Rolf Turk Emile J de Meijer Peter AC 't Hoen |
| Institution: | (1) Department of Information Systems and Computing, Brunel University, UB8 3PH Uxbridge, UK;(2) Center for Human and Clinical Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, Netherlands;(3) Leiden Institute of Advanced Computer Science, Leiden University, PO Box 9512, 2300 RA Leiden, Netherlands;(4) Department of Physiology and Biophysics, Present affiliation: Howard Hughes Medical Institute, Iowa City, Iowa, USA |
| Abstract: | BackgroundThe identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T 2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. |
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