New Statistical Methods for Medical Signals and Images

 描述（由申请人提供）：医学和生物数据通常以信号的形式出现，包括序列和图像。在生物医学环境中，微阵列、高通量测序、蛋白质阵列和许多其他测定被广泛使用。同样，电磁脑成像技术（MRI，fMRI和EEG/MEG）用于研究大脑和解剖学中的皮质活动。这些数据的性质给统计工作带来了重大挑战。 分析：具体来说，测量的数量往往比病例的数量大得多，而且各组成部分之间存在相关性。这项正在进行的三名研究人员赠款的主要目的是开发和研究统计技术，以加强对这些数据的分析和解释。我们在新项目中的重点是开发模型和方法，以从这些新兴技术中提取最大信息，并作为统计学家，指导科学家对结果进行有效解释。 更新将通过四个具体目标来实现这些目标。研究人员将研究：1。用于比较内部预测因子与外部预测因子的选择后推断。对于基因组和其他“组学”数据，使用来自选择后推断的新工具，将经验生物标志物特征与标准临床预测因子（诸如身高、体重和年龄）进行有效的统计比较; 2.通过CAPP-seq进行癌症检测的统计方法。用于确定基因组的哪些连续区域（“瓦片”）应该被测序的统计和计算方法，以搜索针对早期癌症检测的癌症突变; 3. 病毒学和遗传学中高维本征结构的新设置。基于突变序列数据的病毒学疫苗设计的特征向量估计方法;通过适应统计随机矩阵理论的最新进展来理解数量遗传学中大方差分量矩阵特征值分布的统计工具; 4. MRI数据的局部平滑模型。通过使用利用成像域空间结构的模型来提高定量和扩散MRI的灵敏度和分辨率。研究人员将与他们的学生一起工作，将新的统计工具应用到医学上可用的软件中，遵循本基金早期周期建立的模式，其中我们的软件包在斯坦福大学和世界各地的医学研究人员中得到广泛使用。

 DESCRIPTION (provided by applicant): Medical and biological data often come in the form of signals, including sequences, and images. In the biomedical setting, microarrays, high-throughput sequencing, protein arrays and many other assays are in widespread use. Similarly, electromagnetic brain imaging techniques (MRI, fMRI and EEG/MEG) are used to study cortical activity in the brain and anatomy. The nature of these data brings major challenges for statistical analysis: specifically the number of measurements is often much larger than the number of cases, and there are correlations among the components. The broad aim of this ongoing three-investigator grant is to develop and study statistical techniques that enhance the analysis and interpretation of these data. Our focus in the new projects is the development of models and methods to extract maximal information from these emerging technologies, and as statisticians, to guide the scientist in valid interpretation of the results. The renewal will address these goal through four Specific Aims. The investigators will study: 1. Post-Selection Inference for comparing internal to external predictors. For genomic and other "-omic" data, valid statistical comparison of empirical biomarker signatures to standard clinical predictors such as height, weight, and age, using new tools from post-selection inference; 2. Statistical Methods for cancer detection via CAPP-seq. Statistical and computational approaches for determining which contiguous regions ("tiles") of the genome should be sequenced, in the search for cancer mutations directed toward earlier cancer detection; 3. New settings for high dimensional Eigen structure in virology and genetics. Eigenvector estimation methods for vaccine design in virology based on mutation sequence data; statistical tools for understanding the distribution of the eigenvalues of large variance component matrices in quantitative genetics by adapting recent advances in statistical random matrix theory; 4. Locally smooth models for MRI data. Improving the sensitivity and resolution of quantitative and diffusion MRI by using models that exploit the spatial structure of the imaging domain. Working together, and with their students, the investigators will implement the new statistical tools into publically available software, following a pattern established in earlier cycles of this grant, in which our packages have found wide use among medical researchers both at Stanford and around the world.