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Analysis of time-course microarray data: Comparison of common tools

机译：时间课程微阵列数据分析：常用工具的比较

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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.
机译：高吞吐量时间序列数据具有研究生物系统的活力的特殊价值。然而，对这种复杂数据的解释可能是具有挑战性的。本研究的目的是比较最近开发的常见算法用于检测时间 - 过程微阵列数据中的差异表达基因。使用不同的措施，如灵敏度，特异性，预测值和相关信令途径，我们发现利马，时间库和GPREGE对分析数据集的分析具有相当好的性能，其中仅随着时间的推移。然而，利马具有能够报告显着切断的额外优势，使其成为更实用的工具。此外，利用LiMMA和TTCA可以令人满意地用于所有实验组的时间序列数据的数据集。这些发现可以帮助调查人员选择用于检测差异表达基因的适当工具，作为时间课程大数据解释的初始步骤。

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