Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran.
Genomics. 2019 Jul;111(4):636-641. doi: 10.1016/j.ygeno.2018.03.021. Epub 2018 Mar 31.
High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. 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 datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. 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.
高通量时间序列数据对于研究生物系统的动态性具有特殊价值。然而，解释此类复杂数据具有一定的挑战性。本研究旨在比较最近开发的用于检测时间序列微阵列数据中差异表达基因的常用算法。使用不同的指标，如敏感性、特异性、预测值和相关信号通路，我们发现 limma、timecourse 和 gprege 对于仅随时间推移的实验组的数据集分析具有相当好的性能。然而，limma 具有能够报告显著截止值的额外优势，使其成为更实用的工具。此外，limma 和 TTCA 可以很好地用于具有所有实验组时间序列数据的数据集。这些发现可能有助于研究人员选择适当的工具来检测差异表达基因，作为解释时间序列大数据的初始步骤。
