Preprocessing and Analysis Tools for Contemporary Microarray Applications

微阵列是强大的高通量基因组学工具的一个例子， 正在彻底改变生物系统的测量。在这一领域和其他 技术，需要一些关键步骤来将原始的 生物学家和临床医生所依赖的结果。这些数据 操纵对最终测量的质量有巨大的影响 以及依赖于它们的研究。 我们的小组以前已经证明，使用现代统计 方法学可以显著提高基因表达的准确性和精确性 测量，相对于设计师引入的临时程序， 技术的制造商。目前，多家公司已将我们的 将方法导入其数据分析软件（例如GeneSpring、GeneTraffic）。 微阵列现在被用于测量各种高基因组终点 包括基因型，染色体异常，包括缺失/插入， 蛋白质结合位点、甲基化和可变剪接。每例病患的 基因组测量单位是称为探针的短寡核苷酸。 如果不适当地理解这些偏差和差异， 测量，基于探针分析的生物学推断将是 暴露了在这些新技术中，我们希望我们提出的研究能够 制定统计方法，促进类似于那些 用表达式数组实现。 近年来，对更多这类研究的需求急剧增长。 近年来，随着微阵列技术的新用途的迅速扩展。我们 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。为了实现这一目标， 目前的建议有以下具体目标： 最流行的新兴应用工具，开发预处理 服务于用户群体最迫切需要的方法， 用于群体范围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.