Integrative Analysis of Cross-Platform Microarray Data

微阵列基因表达谱在许多实验室进行，从而产生快速的数据 公共储存库中的积累。然而，由于不同技术平台的存在和 缺乏标准的实验方案，数据集之间的系统差异经常超过能力 统计正规化。目前，迫切需要方法论来整合跨平台 微阵列数据。这项建议满足了这一需求。我们的目标是开发新的计算和 整合跨平台微阵列数据的统计方法。具体地说，我们将(1)检测复发 跨多个微阵列数据集的表达模式；(2)执行功能和转录注释 对于多个基因组；(3)预测高等真核基因的转录调控因子 关于蛋白质-DNA结合位点的信息；以及(4)识别作为疾病特征的遗传网络。 使用我们的方法，我们能够提取任何基因组的更多数量级的信息 这其中有大量的微阵列数据。我们将执行“特定于上下文”的功能和 酵母(S.cerevisiae)、蠕虫(C.elegans)、蝇(D. 植物(A.thaliana)、小鼠(M.musculus)、大鼠(R.norveicus)和人(H.seniens)。那 就是，我们将有条件地注释基因的功能/调节，这取决于哪组其他基因 它们相互作用，并在何种条件下发生这种相互作用。在发布我们的 根据预测结果，我们将在每个注释中附加必要的上下文信息。最后，我们会 为生物学家开发跨平台综合分析软件ARRAYMINE 微阵列数据。我们的算法和软件将极大地促进海量的 现有的微阵列数据，减少生成新数据的必要性，并提高我们对 在各种扰动下的细胞功能和网络。

Microarray gene expression profiling is performed in many laboratories, resulting in the rapid data accumulation in public repositories. However, due to the existence of different technology platforms and the lack of standard experimental protocols, systematic variation among data sets often exceeds the capability of statistical normalization. Currently, there is an urgent need for methodology to integrate cross-platform microarray data. This proposal addresses this need. We aim at developing novel computational and statistical methods to integrate cross-platform microarray data. Specifically, we will (1) detect recurrent expression patterns across many microarray datasets; (2) perform functional and transcriptional annotation for multiple genomes; (3) predict transcription regulators for higher eukaryotic genes without prior information on protein-DNA binding sites; and (4) identify genetic networks that are signatures of diseases. Using our approach, we are in a position to extract an order of magnitude more information for any genome for which massive microarray data is available. We will perform "context-specific" functional and transcriptional annotation for the genomes of yeast (S. cerevisiae), worm (C. elegans), fly (D. melanogaster), plant (A. thaliana), mouse (M. musculus), rat (R. norvegicus) and human (H. sapiens). That is, we will conditionally annotate the functions/regulations of genes, depending on which set of other genes they are interacting with and under which sets of conditions such interactions occur. When releasing our prediction results, we will attach to each annotation the necessary context information. Finally, we will develop a software package ARRAYMINEfor biologists to perform integrative analysis of cross-platform microarray data. Our algorithms and software will significantly facilitate the re-use of the vast amount of existing microarray data, reduce the necessity to generate new data, and improve our understanding of cellular functions and networks under a variety of perturbations.