Preprocessing and Analysis Tools for Contemporary Microarray Applications

微阵列是功能强大的高通量基因组学工具的一个例子 正在给生物系统的测量带来革命性的变化。在这个和其他方面 技术，需要一些关键步骤来将原始数据转换为 对生物学家和临床医生所依赖的结果进行测量。这些数据 操纵对最终测量的质量有很大影响 以及依赖于它们的研究。 我们小组之前已经证明了现代统计学的使用 方法学可以显著提高基因表达的准确性和精密度 测量，相对于设计师和 这项技术的制造商。现在，许多公司都把我们的 方法输入他们的数据分析软件(如GeneSpring、GeneCommunications)。 微阵列现在被用来测量不同的高基因组终点 包括基因型，染色体异常，包括缺失/插入， 蛋白质结合部位、甲基化和选择性剪接。在每种情况下， 基因组的测量单位是被称为探针的短寡核苷酸。 如果没有适当地理解这些偏差和差异 测量，基于探针分析的生物推断将是 妥协了。在这些新技术中，我们预计我们提出的研究将 生成便于改进的统计方法，类似于 通过表达数组获得。 近年来，对这类研究的需求急剧增加。 几年来，随着微阵列技术的新用途的迅速扩展。我们的 长期目标是提高使用微阵列获得的结果的质量 通过使用改进的统计方法进行实验。为了实现这个目标， 目前的提案有以下具体目标：开展基本分析 用于最流行的新兴应用程序的工具，用于开发预处理 满足用户社区最迫切需求的方法，并制定 全人群热点检测的一般统计方法。

Microarrays are an example of powerful high throughput genomics tools that are revolutionizing the measurement of biological systems. In this and other technologies, a number of critical steps are required to convert the raw measures into the results relied upon by biologists and clinicians. These data manipulation have enormous influence on the quality of the ultimate measurements and studies that rely upon them. Our group has previously demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Various companies have now incorporated our methods into their data analysis software (e.g. GeneSpring, GeneTraffic). Microarrays are now being used to measure diverse high genomic endpoints including genotype, chromosomal abnormalities including deletions/insertions, protein binding sites, methylation, and alternative splicing. In each case, the genomic units of measurement are short oligonucleotides referred to as probes. Without appropriate understanding of the bias and variance of these measurements, biological inferences based upon probe analysis will be compromised. In these new technologies, we expect our proposed research to produce statistical methods that facilitate improvements similar to those attained with expression arrays. The need for more research of this kind has grown dramatically in recent years, with the rapid expansion of novel uses of the microarray technology. Our long-term goal is to improve the quality of results obtained using microarray experiments via the use of improved statistical methodology. Toward this goal, the current proposal has the following specific aims: to develop basic analysis tools for the most popular emerging applications, to develop preprocessing methodology to serve the most urgent needs of the user community, and to develop general statistical methodology for population wide hot-spot detection.