Methods development for "Omics" data

在处理高维生物数据时进行适当的变量和统计推断存在许多挑战。 我今年的方法开发工作集中在改进全基因组关联研究，高通量代谢组学数据和整合基因组学研究的计算和统计方法。 与我最近毕业的学生Tao Jiang博士一起，我们基于置信度下假零假设比例的估计下界，开发了稀疏组Lasso中正则化参数的新上界。通过应用来自单个标记/变量分析的依赖或独立p值的经验分布来估计界限，其中使用二级显著性检验，即更高的批评统计量。Lasso中的调整参数的上界对应于虚假零假设比例的下界。因此，调谐范围是窄的，因为上界较低。非零估计的最终决策将包含更多变量，使得修改后的GWAS的功效高于或等于原始稀疏组Lasso。我们证明了我们的方法的性能，使用模拟实验和真实的数据应用在脂质性状遗传学的行动，以控制心血管疾病的风险，糖尿病临床试验。 开发了一个R包。 当前的敲除方法的应用使用线性回归模型，并且仅对模型函数中存在的变量进行变量选择。在与Tao Jiang博士和Yuanyuan Li博士的一个项目中，我们扩展了使用提升树进行机器学习的仿制品，这是成功的，广泛用于不需要模型函数先验知识的问题。我们开发了一种新的策略进行变量选择，没有事先模型拓扑知识，使用敲除方法与提升树模型。我们通过使用基于树的模型将当前的敲除方法扩展到无模型变量选择。我们测试并比较了这些方法与原始的敲除方法控制I型错误和功率的能力。在模拟测试中，我们比较了树模型的重要性检验统计量的性质和性能。 几十年来，联合药物治疗一直是癌症治疗的支柱，并已被证明可以降低宿主毒性并防止获得性耐药性的发展。因此，开发计算方法来预测药物协同作用并指导实验设计以发现合理的治疗组合至关重要。与我的学生Jun Ma一起，我们开发了一种新的深度学习方法，通过整合来自细胞系和化学结构数据的基因表达谱来预测协同药物组合。具体来说，我们使用主成分分析来降低化学描述符数据和基因表达数据的维数。然后，我们通过神经网络传播低维数据来预测药物协同作用值。降维的使用大大减少了计算时间，而不损失精度。 此外，我最近毕业的博士生Jun Ma博士致力于开发一种解决非线性剂量反应关系挑战的方法。非线性剂量-反应关系广泛存在于受不同水平的生物、化学或辐射应激影响的细胞、生物化学和生理过程中。非线性剂量-反应关系广泛存在于受不同水平的生物、化学或辐射应激影响的细胞、生物化学和生理过程中。因此，我们建议使用EA的剂量-反应模型的范围内的潜在的反应模型的功能形式。这种新方法不仅可以拟合最常用的非线性剂量-反应模型（如指数模型和3-，4-和5-参数logistic模型），而且还可以选择最佳模型，如果没有模型假设，这是特别有用的情况下，高通量曲线拟合。开发了一个R软件包来实现该方法。 目前正在进行的项目建立在检测基因-环境相互作用的方法上，使用方差QTL来优先考虑单核苷酸多态性以检测基因-基因相互作用。

There are a number of challenges in conducting proper variable and statistical inference in working across high dimensional biological data. My methods develop work this year has focused improving computational and statistical approaches for genome-wide association studies, high throughput metabolomics data, and integrative genomics studies. With my recently graduated student Dr. Tao Jiang, we developed a new upper bound of the regularization parameter in sparse group Lasso based on an estimated lower bound of the proportion of false null hypotheses with confidence. The bound is estimated by applying the empirical distribution of dependent or independent p-values from single marker/variable analysis, where a second-level significance testing, the higher criticism statistic, is used. An upper bound of the tuning parameter in Lasso is decided corresponding to the lower bound of the proportion of false null hypotheses. Thus, the tuning range is narrow since the upper bound of is lower. The final decision of non-zero estimates will contain more variables so that the power of modified GWAS is higher than or equal to the original sparse group Lasso. We demonstrate the performance of our method using both simulation experiments and a real data application in lipid trait genetics from the Action to Control Cardiovascular Risk in Diabetes clinical trial. An R package was developed. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. In a project with Dr. Tao Jiang, and with Dr. Yuanyuan Li, we extended the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. We developed a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extended the current knockoff method to model-free variable selection through the use of tree-based models. We tested and compared these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compared the properties and performance of importance test statistics of tree models. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy. With my student Jun Ma, we developed a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. The use of dimension reduction dramatically decreases the computation time, without losing accuracy. Additionally, my recently graduated PhD student Dr. Jun Ma we worked on developing an approach that addresses challenges in nonlinear dose-response relationships. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose-response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. An R package to implement the method was developed. Ongoing projects building onto methods for detecting gene-environment interactions are currently ongoing, using variance QTLs to prioritize single nucleotide polymorphisms for detecting gene-gene interactions.