New statistical methods for medical signals and images

医学信号和图像的新统计方法

• 批准号: 8300798
• 负责人: Iain M Johnstone
• 金额: $ 38.42万
• 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/8300798
• 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.

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，基因表达阵列是现在广泛使用的技术，其产生个体样本中整个基因组的重要部分的活性的图像。现在出现了许多其他基因组测定，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。这种数据的一个共同特征是个体情况是高维的，变量、基因、体素或采样时间的数量很大。通常测量的数量比案例的数量要大得多，并且组件之间通常存在相关性，这两者都对统计分析提出了重大挑战。这项正在进行的三名研究人员赠款的主要目的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信息，并指导科学家解释结果。 更新将通过四个具体目标来实现这些目标。研究者将研究：1）使用Poisson对数线性模型和估计错误发现率的新程序的RNA-Seq比较实验的显著性分析。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“1”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，其提供了以前所未有的空间和时间分辨率估计源活动的希望;（3）多变量检验的功效和样本量计算，特别是使用随机矩阵理论的统计应用的最新进展来开发和评估功效近似，使其在软件中可用;并促进更广泛地评估和使用多变量方法;以及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的估计。提出了一种逐步通过回归解路径的顺序方法。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医学上可用的软件中，遵循本基金早期周期建立的模式，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。