| چکیده انگلیسی مقاله |
Development of high-throughput techniques has provided opportunity to better understand the complexity of biological phenomena. These methods are especially valuable when they are used to generate time-series data that can reflect the dynamic nature of the processes. The interpretation of such complex data requires special considerations and appropriate analysis tools [1]. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Limma[2,3], timecourse[4], EDGE[5], BETR[6], and gprege[7] R packages were compared using both biological and synthetic one-sample microarray datasets in which only test group is followed over time. Also, limma, BETR, and TTCA[8] were compared for the analysis of two-sample datasets with time-series data for all experimental groups. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of one-sample datasets. However, limma has the additional advantage of being able to report significance cut off (i.e. p-value threshold), making it a more practical tool. In addition, limma and TTCA both can be satisfactorily used for two-sample data. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data. |
