基因表达和表型数据的综合分析
Integrative analysis of gene expression and phenotype data
摘要：
将基因型与表型联系起来是现代遗传学的根本目标。通过综合分析，我们专注于研究基因表达数据和表型数据之间的联系。我们提出了三种方法。1)表型的固有复杂性使得高通量表型分析成为非常困难和费力的过程。我们提出了一种利用基因表达相似性的自动多维分析方法。大规模的分析表明，我们的方法可以提供强大的剖析，揭示不同的表型方面的样品。这种分析技术还能够在训练数据中给出的表型信息之外进行内插和外推。它可以用于许多应用，包括促进实验设计和检测混杂因素。2)表型关联分析问题由于样本量小、维数高而变得复杂。因此，从训练数据中获得的表型相关基因子集对训练样本的选择非常敏感，并且构造的样本表型分类器往往具有较差的泛化性能。为了消除这些障碍，我们提出了一种新的方法，产生序列越来越歧视性的基因簇组合。我们在模拟和真实数据集上的实验都显示了鲁棒和准确的分类性能。3)许多复杂的表型，如癌症，不仅是基因表达的产物，而且是基因相互作用的产物。我们提出了一种综合方法来寻找在不同表型条件下激活的基因网络模块。使用我们的方法，我们发现了癌症亚型特异性网络模块，以及这些模块协调的方式。特别是，我们检测到一个乳腺癌特异性肿瘤抑制网络模块与枢纽基因，PDGFRL，这可能在这个模块中发挥重要作用。
英文摘要：
The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and laborious process. We propose a method of automated multi-dimensional profiling which uses gene expression similarity. Large-scale analysis show that our method can provide robust profiling that reveals different phenotypic aspects of samples. This profiling technique is also capable of interpolation and extrapolation beyond the phenotype information given in training data. It can be used in many applications, including facilitating experimental design and detecting confounding factors. 2) Phenotype association analysis problems are complicated by small sample size and high dimensionality. Consequently, phenotype-associated gene subsets obtained from training data are very sensitive to selection of training samples, and the constructed sample phenotype classifiers tend to have poor generalization properties. To eliminate these obstacles, we propose a novel approach that generates sequences of increasingly discriminative gene cluster combinations. Our experiments on both simulated and real datasets show robust and accurate classification performance. 3) Many complex phenotypes, such as cancer, are the product of not only gene expression, but also gene interaction. We propose an integrative approach to find gene network modules that activate under different phenotype conditions. Using our method, we discovered cancer subtype-specific network modules, as well as the ways in which these modules coordinate. In particular, we detected a breast-cancer specific tumor suppressor network module with a hub gene, PDGFRL, which may play an important role in this module.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
