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Improved methods for the analysis of circadian rhythmsin correlated gene expression data |
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| รหัสดีโอไอ | |
| Creator | 1. Wannapa Pukdee 2. Orathai Polsen 3. Mohamed Fazil Baksh |
| Title | Improved methods for the analysis of circadian rhythmsin correlated gene expression data |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2561 |
| Journal Title | Songklanakarin Journal of Science and Technology |
| Journal Vol. | 40 |
| Journal No. | 3 |
| Page no. | 692 |
| Keyword | correlated gene expression data, de-trending method, nonlinear regression |
| URL Website | http://rdo.psu.ac.th/sjstweb/index.php |
| ISSN | 0125-3395 |
| Abstract | Circadian clocks regulate biological behaviours, such as sleeping and waking times, that recur naturally on anapproximately 24-hour cycle. These clocks tend to be influenced by a variety of external factors, sometimes to the extent that itcan have an impact on health. As an example in pharmacology, the effects of chemicals on the circadian rhythm in patients canbe key to clarifying the relationship of drug efficacy and toxicity with dosing times. While pre-clinical experiments conducted toelucidate these effects may produce correlated data measured over time, such as gene expression profiles, existing methods forfitting parametric nonlinear regression models are, however, inadequate and can lead to unreliable, inconsistent parameterestimates and invalid inference. De-trending is widely used as a pre-processing step to address non-stationarity in the data, beforefitting models based on the assumption of independence. However, as it is unclear that this approach properly accounts for thecorrelation structure, alternative methods that specifically model the correlation in the data based on conditional least squares anda two-stage estimation procedure are proposed and evaluated. A simulation study covering a wide range of scenarios and modelsshows that the proposed methods are more efficient and robust against model mis-specification than de-trending and,furthermore, they reduced estimation bias in the circadian period and provide more reliable confidence intervals. |
