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采样曲线和图像的形式出现。例如，基因表达阵列是一种现在广泛使用的技术，可生成个体样本中整个基因组的重要部分的活动图像。现在正在出现许多其他基因组检测方法，包括用于测量 RNA 丰度的高通量测序（“RNA-seq”）。同样，电磁脑成像技术（MRI、fMRI 和 EEG）也广泛用于研究大脑皮层活动和解剖学。此类数据的一个共同特征是个案是高维的，变量、基因、体素的数量或采样次数都很大。通常，测量的数量远大于案例的数量，并且各个组成部分之间通常存在相关性，这两者都给统计分析带来了重大挑战。这项持续的三名研究人员资助的主要目标是开发新的和修改现有的统计技术，以加强对这些数据的分析和解释。我们新项目的一个共同点是开发模型和方法，从这些新兴技术中提取最大的信息，并指导科学家解释结果。 此次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）使用泊松对数线性模型和估计错误发现率的新程序对 RNA-Seq 比较实验进行显着性分析。准确而稳健的检测差异表达基因的方法对于有效利用 RNA-seq 进行科学研究至关重要； 2）使用'1正则化技术估计来自脑电图数据的皮质信号，并开发快速、实用的算法，为以前所未有的空间和时间分辨率估计源活动提供了希望； 3) 多变量检验的功效和样本量计算，特别是利用随机矩阵理论统计应用的最新进展来开发和评估功效近似值，使其在软件中可用；促进更广泛的评估和使用多元方法； 4）应用于现代基因组数据集的子集回归算法的错误发现率估计。提出了一种逐步执行回归解决方案路径的顺序方法。这项工作将帮助物理和医学科学家从大规模数据集中构建有效且可解释的预测模型。 我们将按照本次资助早期周期中建立的模式，将我们的统计工具应用到公开可用的软件中，其中我们的软件包已在斯坦福大学和世界各地的医学研究人员中广泛使用。 公共卫生相关性：本项目中开发的统计方法是帮助医学研究人员发现和验证新的基础科学成果（例如成像和基因组学）的重要工具，这些成果可以带来新的疗法。它们还有助于设计和分析新疗法的临床研究，以便以最有效的方式利用当前研究中收集的大量数据，同时准确描述结论的不确定程度。

DESCRIPTION (provided by applicant): Medical and biological data often come in the form of sampled curves and images. For example, gene expression arrays are a now widespread technology producing images of the activity of a significant part of a whole genome in a sample of individuals. Many other genomic assays are now emerging, including high-throughput sequencing ("RNA-seq") for measuring RNA abundance. Similarly, electromagnetic brain imaging techniques (MRI, fMRI and EEG) are widely used to study cortical activity in the brain and anatomy. A common feature of such data is that the individual case is high-dimensional, with the number of variables, genes, voxels, or sampling times being large. Often the number of measurements is much larger than the number of cases and there are usually correlations among the components-both raise major challenges for statistical analysis. The broad aim of this ongoing three-investigator grant is to develop new and modify existing statistical techniques to enhance the analysis and interpretation of these data. A common thread in our new projects is the development of models and methods to extract maximal information from these emerging technologies, and to guide the scientist in interpretation of the results. The renewal will address these goals through four Specific Aims. The investigators will study: 1) the Significance analysis of RNA-Seq comparative experiments using Poisson log linear models and a novel procedure to estimate the false discovery rate. Accurate and robust methods for detecting differentially expressed genes are essential for effective use of RNA-seq for scientific research; and 2) the estimation of cortical signals from EEG data using '1 regularization techniques and develop fast, practical, algorithms that offer hope of estimating source activity at a spatial and temporal resolution not seen before; and 3) Power and sample size calculations for multivariate tests, and in particular use recent advances in the statistical application of random matrix theory to develop and evaluate power approximations, make them available in software; and promote more widespread evaluation and use of multivariate methods; and 4) the estimation of the False Discovery Rate for subset regression algorithms applied to modern genomic datasets. A sequential method is proposed that steps through a path of regression solutions. This work will help physical and medical scientists to build effective and interpretable predictive models from large scale datasets. We will implement our 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. PUBLIC HEALTH RELEVANCE: Statistical methods such as those to be developed in this project are essential tools to help medical re- searchers discover and validate new basic science results (for example in imaging and genomics) that can lead to new therapies. They aid also in the design and analysis of clinical investigations of new treatments so as to use in the most efficient manner the large amount of data collected in current research, while also accurately describing the degree of uncertainty in the conclusions.