Methods development for "Omics" data

“组学”数据的方法开发

• 批准号: 10260283
• 负责人: Alison Motsinger-Reif
• 金额: $ 80.39万
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10260283
• 关键词: Address; Affect; Biochemical Process; Biological; Cell Line; Cell physiology; Chemical Structure; Chemicals; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Data; Descriptor; Development; Diabetes Mellitus; Dimensions; Dose; Drug Combinations; Drug Synergism; Experimental Designs; Expression Profiling; Gene Expression; Genes; Genetic; Genomics; Knowledge; Lasso; Linear Regressions; Lipids; Logistic Models; Machine Learning; Methods; Modeling; Performance; Physiological Processes; Principal Component Analysis; Property; Quantitative Trait Loci; Radiation; Single Nucleotide Polymorphism; Stress; Students; Testing; Time; Toxic effect; Trees; Work; acquired drug resistance; base; cancer therapy; cardiovascular risk factor; curve fitting; deep learning; doctoral student; experimental study; gene environment interaction; gene interaction; genome wide association study; graduate student; high dimensionality; improved; metabolomics; method development; neural network; novel strategies; prevent; response; simulation; statistics; structured data; trait

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.

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重点是改进全基因组关联研究、高通量代谢组学数据和综合基因组学研究的计算和​​统计方法。 与我最近毕业的学生江涛博士一起，我们基于有信心的错误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限。通过应用来自单标记/变量分析的相关或独立 p 值的经验分布来估计界限，其中使用二级显着性检验（更高的批评统计量）。 Lasso 中调整参数的上限是根据假零假设比例的下限来决定的。因此，由于 的上限较低，因此调整范围较窄。非零估计的最终决策将包含更多的变量，使得修改后的GWAS的功效高于或等于原始稀疏组Lasso。我们使用模拟实验和来自糖尿病临床试验中控制心血管风险行动的脂质性状遗传学的真实数据应用来证明我们的方法的性能。 开发了 R 包。 目前仿制方法的应用使用线性回归模型，并且仅针对模型函数中存在的变量进行变量选择。在江涛博士和李媛媛博士的一个项目中，我们通过增强树扩展了机器学习中仿制品的使用，这种方法取得了成功，并广泛应用于不需要模型函数先验知识的问题。我们开发了一种新颖的策略，使用带有提升树模型的knockoff方法，在没有先验模型拓扑知识的情况下进行变量选择。我们通过使用基于树的模型将当前的仿制方法扩展到无模型变量选择。我们测试了这些方法并将其与原始仿制方法进行了比较，了解它们控制 I 类错误和功率的能力。在模拟测试中，我们比较了树模型重要性测试统计的性质和性能。 几十年来，联合药物治疗一直是癌症治疗的支柱，并已被证明可以减少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。我们与我的学生 Jun Ma 一起开发了一种新的深度学习方法，通过整合细胞系的基因表达谱和化学结构数据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度。然后，我们通过神经网络传播低维数据来预测药物协同值。降维的使用极大地减少了计算时间，而不会损失准确性。 此外，我最近毕业的博士生马军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程度的生物、化学或辐射应激影响的细胞、生化和生理过程中。非线性剂量反应关系广泛存在于受不同程度的生物、化学或辐射应激影响的细胞、生化和生理过程中。因此，我们建议使用 EA 对一系列潜在反应模型功能形式的剂量反应建模。这种新方法不仅可以拟合最常用的非线性剂量反应模型（例如指数模型和3、4、5参数Logistic模型），而且可以在不做模型假设的情况下选择最佳模型，这在高通量曲线拟合的情况下特别有用。开发了一个 R 包来实现该方法。 目前正在进行的项目正在构建用于检测基因-环境相互作用的方法，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。