Sivachenko Andrey Y, Yuryev Anton, Daraselia Nikolai, Mazo Ilya
Ariadne Genomics, Inc., 9430 Key West avenue, Suite 113, Rockville, MD 20850, USA.
J Bioinform Comput Biol. 2007 Apr;5(2B):429-56. doi: 10.1142/s0219720007002795.
Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.
基于微阵列的组织、细胞和疾病状态以及环境条件和治疗反应的表征提供了包含大量宝贵信息的全基因组快照。然而，数据中缺乏内在结构以及存在强噪声使得提取和解释这些信息以及制定和优先考虑与领域相关的假设成为艰巨任务。需要与不同类型的生物数据进行整合，以便将表达测量置于生物学上有意义的背景中。本文讨论了微阵列数据解释中的一些方法，重点是分子网络信息的使用。展示了统计程序，这些程序将表达数据叠加到从科学文献中挖掘的转录调控网络上，旨在选择下游具有显著表达变化模式的转录调节因子。建议进行考虑网络拓扑和转录调控效应迹象的测试。使用两个不同的表达数据集对这些方法进行了说明，比较了性能，并讨论了预测的生物学相关性。
