Statistical Methods for Ultrahigh-dimensional Biomedical Data

描述：这项建议开发了新的统计方法，从高通量数据中选择一小群分子，例如来自生物医学研究的微阵列、蛋白质组和下一代序列，特别是对于自闭症研究和脑瘤。它侧重于开发有效的方法和有效的统计工具来控制假发现率和测试对一组分子的治疗效果，用于在变量中存在错误、内生性和重尾错误分布的情况下进行特征选择和模型建立，以及用于预测临床结果和了解分子机制。它开发半参数和非参数模型，以减少建模偏差和增加特征。为理解遗传网络、建立统计模型和进行推理，进一步发展了估计大协方差矩阵的方法。它引入了多元独立筛选和条件独立筛选技术，以减少变量筛选中的假阴性和假阳性，并为一系列统计模型发展了可计算的最优惩罚似然方法。通过理论研究和仿真研究，对每种方法的优缺点进行了细致的分析。将开发相关软件。来自正在进行的自闭症研究、脑瘤和其他生物医学研究的数据集将使用新开发的方法进行分析，结果将得到进一步的生物学证实和调查。这些研究成果将对生物医学研究的高通量数据的统计分析以及理解自闭症、脑瘤和其他疾病的分子机制产生重大影响。

DESCRIPTION: This proposal develops novel statistical methods to select a small group of molecules from high-throughput data such as microarray, proteomic, and next generation sequence from biomedical research, especially for autism studies and brain tumors. It focuses on developing efficient methods and valid statistical tools for controlling false discovery rate an testing treatment effects on a group of molecules, for feature selection and model building in presence of errors-in-variables, endogeneity, and heavy-tail error distributions, and for predicting clinical outcomes and understanding molecular mechanisms. It develops semiparametric and nonparametric models to reduce modeling biases and to augment features. It furthers the developments on estimating large covariance matrices for understanding genetic network, statistical model building and inferences. It introduces multivariate independence screening and conditional independence screening techniques to reduce false negatives and false positives in variable screening, and develops computable and optimal penalized likelihood methods for an array of statistical models. The strength and weakness of each proposed method will be critically analyzed via theoretical investigations and simulation studies. Related software will be developed. Data sets from ongoing autism research, brain tumor, and other biomedical studies will be analyzed using the newly developed methods and the results will be further biologically confirmed and investigated. The research findings will have strong impact on statistical analysis of high throughput data for biomedical research and on understanding molecular mechanisms of autism, brain tumors, and other diseases.