Sohler Florian, Zimmer Ralf
Department of Informatics, Ludwig-Maximilians-Universität, München, Germany.
Bioinformatics. 2005 Sep 1;21 Suppl 2:ii115-22. doi: 10.1093/bioinformatics/bti1120.
Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data.
We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.
尽管总体上在从表达数据中识别调控关系方面已取得进展，但只有少数方法专注于使用单一测量来检测诸如活跃通路等生物学机制。当仅有少量测量数据时，例如在研究特殊细胞类型或条件时，这一点尤为重要。在此，我们提出一种方法，用于在以网络和功能注释形式给出先验知识的情况下，对表达数据测试用户指定的假设（通路查询）。基于此方法，我们开发了一种评分函数来识别活跃的转录因子或激酶，从而朝着解释测量的表达数据迈出了第一步。
我们将该算法应用于罗塞塔酵母综合数据集，发现在许多情况下结果与生物学知识一致。我们能够证实，通过我们的方法识别出的转录因子以及在较小程度上的激酶，在各自基因敲除所影响的生物学过程中发挥着重要作用。此外，我们表明，推断活性的相关性可以为转录因子的物理相互作用或合作提供证据，而单纯表达数据的相关性则无法做到这一点。
