ANALYSIS OF OLIGONUCLEOTIDE GENE EXPRESSION ARRAY DATA

高密度寡核苷酸阵列提供了一种同时监测数万个基因表达水平的方法。这项技术产生的海量数据将成为有助于阐明人类基因组计划中识别的大量基因的作用、功能和调控的关键信息来源。该项目的长期目标是开发统计方法和计算机程序，以有效分析来自高密度寡核苷酸阵列的基因表达数据。具体地说，我们的施舍如下。1)改进原始图像数据的分析方法，包括动态网格化和去模糊、细胞强度和质量分数的计算、背景和梯度校正。2)设计数组间比较的归一化程序。3)通过对大型表达数组数据库的分析，学习和积累关于探针特异性效应的统计知识。这些知识将被用来设计分析方法，这些方法将大大减少在估计和比较基因表达水平时由于探针效应而产生的变异性。这些知识还将被用来设计基于模型的方法，以评估基因的存在和差异表达。此外，当探针序列信息变得可用时，将被合并到分析中，以开发基于上述方法学研究的计算机程序。这样的程序将向研究界提供，以促进对寡核苷酸表达阵列数据的有效分析。在目前的分析方法下，寡核苷酸阵列技术对于基因表达研究的非常高的内在灵敏度还没有完全实现。该项目开发的方法将改进分析，并将极大地提高表达式数组数据的效用。

High-density oligonucleotide arrays provide a means to monitor the expression levels of tens of thousands of genes simultaneously. The massive amount of data generated by this technology will be a key source of information that will help to clarify the roles, functions and regulation of the large number of genes identified in the Human Genome Project. The long-term objective of this project is to develop statistical methods and computer programs for effective analysis of gene expression data from high-density oligonucleotide arrays. Specifically, our alms are as follow. 1) To improve on methods for the analysis of raw image data including dynamic gridding and de-blurring, computation of cell intensity and quality scores, background and gradient correction. 2) To design normalization procedures for array-to-array Comparison. 3) To learn and accumulate statistical knowledge about probe-specific effects through the analysis of a large database of expression arrays. This knowledge will be exploited to design methods of analysis that will greatly reduce variability due to probe effects in the estimation and comparison of gene expression levels. This knowledge will also be used to design model- based methods to assess gene presence and differential expression. In addition, probe sequence information will be incorporated into the analysis when it becomes available 4) To develop computer programs based on the above methodological research. Such programs will be made available to the research community to facilitate effective analysis of oligonucleotide expression array data. With current methods of analysis the very high intrinsic sensitivity of oligonucleotide array technology has not been fully realized for gene expression studies. The methods developed in this project will allow improved analysis and will greatly enhance the utility of expression array data.