Generalized multivariate analysis of variance (GMANOVA) models for high dimensional data

高维数据的广义多变量方差分析 (GMANOVA) 模型

• 批准号: 402477-2011
• 负责人: Hamid, Jemila
• 金额: $ 1.24万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=529586
• 关键词: Generalized; multivariate; analysis; variance; GMANOVA

Identifying genes with different expression profiles and ranking them according to these differences in complex time course genomic experiments is very challenging and only few methodological research has been conducted. Challenges in such data arise because expression levels at different time point are often correlated since measurements are taken from same organism, tissue, cell or culture. Moreover, time dependency of gene expression values are usually of interest and often are the biological question that motivates the problem. Another challenge unique to time course genomic experiments is the gene-specific high dimensionality where fewer replications than the time points are often available leading to singular gene-specific covariance matrices. This is in addition to the global high-dimensionality problem common to all microarry experiments. High-dimensional data is not limited to high-throughput genomic experiments. Such data also arises in a wide range of applications including neurological research and signal processing. The Objective of this research program is to provide inference and diagnostic procedures for high dimensional time course data with a focus on time course genomic data. We propose to provide a unified framework for Generalized Analysis of Variance (GMANOVA) models that includes ANOVA and MANOVA as special cases. We will provide moderated test statistics for high-dimensional time course data using GMANOVA models. The model incorporates the within (across time points) correlations and the temporal ordering. Moreover, time is included in the analysis as a continues variable. We will use James-Stein and empirical Bayes Shrinkage approaches to moderate the covariance matrix. We will provide moderated GMANOVA based approaches for gene filtering, gene ranking and identifying genes with different expression profiles for time course microarray experiments. For biologically interesting candidate genetic markers, we will provide moderated likelihood ratio estimates of the mean expression profile. We will also provide moderated residuals that can be used for validating model assumptions and identifying extreme observations.

在复杂的时间进程基因组实验中，识别具有不同表达谱的基因并根据这些差异对它们进行排序是非常具有挑战性的，并且只进行了很少的方法学研究。这些数据中的挑战出现是因为不同时间点的表达水平通常是相关的，因为测量是从相同的生物体、组织、细胞或培养物中进行的。此外，基因表达值的时间依赖性通常是令人感兴趣的，而且往往是引发问题的生物学问题。时间进程基因组实验的另一个独特挑战是特定于基因的高维度，在这种高维度中，可用的重复数通常比时间点更少，从而产生单一的特定于基因的协方差矩阵。这是对所有微阵列实验普遍存在的全球高维问题的补充。高维数据并不局限于高通量基因组实验。这些数据还出现在广泛的应用中，包括神经学研究和信号处理。该研究计划的目标是为高维时间进程数据提供推理和诊断程序，重点是时间进程基因组数据。我们建议为广义方差分析(GMANOVA)模型提供一个统一的框架，该模型包括ANOVA和MANOVA作为特例。我们将使用GMANOVA模型为高维时间进程数据提供适度的测试统计。该模型结合了时间内(跨时间点)的相关性和时间顺序。此外，时间作为一个连续变量包括在分析中。我们将使用James-Stein和经验贝叶斯收缩方法来调节协方差矩阵。我们将为时间进程微阵列实验提供基于GMANOVA的适度方法，用于基因过滤、基因排序和识别具有不同表达谱的基因。对于生物学上有意义的候选遗传标记，我们将提供平均表达谱的适度似然比估计。我们还将提供可用于验证模型假设和识别极端观察的适度残差。