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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573296
• 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.

在复杂的时间过程中，鉴定具有不同表达谱的基因并根据这些差异对它们进行排名非常具有挑战性，并且仅进行了几乎没有方法论研究。由于测量值是从同一生物体，组织，细胞或培养物中获取的，因此出现了此类数据中的挑战，因为不同时间点的表达水平通常是相关的。此外，基因表达值的时间依赖性通常是感兴趣的，并且通常是激发问题的生物学问题。时间课程基因组实验所特有的另一个挑战是基因特异性高维度，在这种挑战中，通常可用于奇异基因特异性协方差矩阵的时间点少的复制。这是所有微型实验常见的全球高维质问题的补充。高维数据不仅限于高通量基因组实验。此类数据也出现在广泛的应用中，包括神经研究和信号处理。该研究计划的目的是为高维时课程数据提供推理和诊断程序，重点是时间课程基因组数据。我们建议提供一个统一的框架，以用于差异分析（Gmanova）模型，其中包括ANOVA和MANOVA作为特殊情况。我们将使用Gmanova模型为高维时课程数据提供适度的测试统计信息。该模型结合了内部（跨时间点）相关性和时间顺序。此外，分析中包含时间，作为一个继续变量。我们将使用詹姆斯·斯坦（James-Stein）和经验贝叶斯收缩方法来调节协方差矩阵。我们将提供基于GMANOVA的调节方法，用于基因滤波，基因排名和鉴定具有不同表达谱的基因，以进行时间课程微阵列实验。对于生物学上有趣的候选遗传标记，我们将提供平均表达谱的可能性比率估计。我们还将提供适度的残差，可用于验证模型假设并识别极端观察。