New statistical methods for medical signals and images

医学信号和图像的新统计方法

• 批准号: 8186445
• 负责人: Iain M Johnstone
• 金额: $ 38.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-10 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8186445
• 关键词: Address; Algorithms; Anatomy; Basic Science; Biological; Biological Assay; Brain; Brain imaging; Cell physiology; Clinical Trials; Computer software; Confidence Intervals; Data; Data Set; Disease; Electroencephalography; Electromagnetics; Emerging Technologies; Evaluation; Exons; Functional Magnetic Resonance Imaging; Gene Expression; Genes; Genetic Transcription; Genome; Genomics; Goals; Grant; Image; Imaging Techniques; Individual; Lead; Log-Linear Models; Magnetic Resonance Imaging; Measurement; Measures; Medical; Methodology; Methods; Modeling; Pattern; Phenotype; Plant Roots; Positioning Attribute; Procedures; RNA; RNA Sequences; RNA analysis; Recovery; Research; Research Personnel; Resolution; Sample Size; Sampling; Scientist; Screening procedure; Signal Transduction; Solutions; Source; Statistical Methods; Techniques; Technology; Testing; Time; Uncertainty; Work; base; comparative; design; interest; model development; novel; practical application; predictive modeling; research study; response; theories; tool

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.

描述(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，它可以生成个体样本中整个基因组的重要部分的活动图像。现在出现了许多其他基因组分析方法，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)被广泛用于研究大脑和解剖学中的皮质活动。这类数据的一个共同特征是，单个病例是高维的，变量、基因、体素或采样时间的数量很大。通常，测量的数量远远大于案例的数量，而且各组成部分之间通常存在相关性--这两种情况都给统计分析带来了重大挑战。这项正在进行的三名调查员赠款的广泛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主线是开发模型和方法，从这些新兴技术中提取最大信息，并指导科学家解释结果。续签将通过四个具体目标来解决这些目标。研究人员将研究：1)使用泊松对数线性模型对RNA-Seq比较实验的显著性分析，以及一种估计错误发现率的新方法。用于检测差异表达基因的准确和稳健的方法对于有效地将RNA-SEQ用于科学研究是必不可少的；以及2)使用正则化技术从EEG数据估计皮质信号，并开发快速、实用的算法，希望以前所未有的空间和时间分辨率估计源活动；以及3)多变量测试的功率和样本大小计算，尤其是利用随机矩阵理论的统计应用中的最新进展来开发和评估功率近似，使其在软件中可用；并促进更广泛的评估和使用多变量方法；以及4)估计应用于现代基因组数据集的子集回归算法的错误发现率。提出了一种逐步通过回归解路径的序贯方法。这项工作将帮助物理和医学科学家从大规模数据集中建立有效和可解释的预测模型。我们将把我们的统计工具应用到公开可用的软件中，遵循在这笔赠款的早期周期中建立的模式，在这种模式下，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了广泛使用。 公共卫生相关性：统计方法，如本项目中将要开发的方法，是帮助医学研究人员发现和验证可能导致新疗法的新的基础科学结果(例如，成像和基因组学)的基本工具。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在当前研究中收集的大量数据，同时还准确地描述结论中的不确定程度。