Preprocessing and Analysis Tools for Contemporary Microarray Applications

描述(申请人提供)：经过十多年对实验和数据分析技术的改进，微阵列技术有望成为个性化基因组学时代的工具。事实上，领先的制造商Affymetrix最近通过了FDA对高通量基因图谱试剂的第一次批准。微阵列在FDA批准的乳腺癌复发检测的成功开发中也起到了至关重要的作用，这使得识别手术后有远处复发风险的患者成为可能。此外，在所有引用微阵列的Pub Med出版物中，约有一半是在过去两年发表的。因此，我们预计，随着新的基因组技术的成熟，学术界和工业界的实验室将在几年内继续依赖这些技术，制造商将继续快速开发新产品。所有微阵列数据分析都是从将原始测量数据转换为生物学家和临床医生所依赖的数据和汇总统计数据开始的。这第一步被称为预处理， 对最终测量的质量和依赖它们的研究结果有巨大的影响。我们的团队此前已经证明，与阵列制造商默认提供的特别数据分析算法相比，统计方法可以提供巨大的改进。我们被广泛引用的统计方法和我们广泛使用的软件实施证明了我们工作的成功。虽然基因表达一直是最流行的微阵列应用，但最近，该技术已被用于测量不同的基因组终点，包括基因型、拷贝数变异、转录因子结合位点和包括DNA甲基化在内的几个表观遗传标记。在第一个资助期，我们小组致力于了解偏见和系统性错误，这些偏差和系统错误可能会模糊结果，阻碍发现，并促成不可重现的发现。我们积累了专业知识并开发了成功的数据分析工具，可以有效地对原始数据进行预处理，使这项技术成为翻译研究和临床应用的首选。然而，从基础研究到临床研究的这种转变将产生新的统计学挑战，我们的方法学在一定程度上促进了微阵列的成功，将在由微阵列技术驱动的前景光明的下一阶段研究中发挥重要作用。我们的目标是开发下一代的预处理和分析工具，重点是翻译应用。为了实现这一目标，目前的建议有以下具体目标：开发侧重于批量效应去除的单阵列预处理方法，开发满足三个迫切需求的微阵列分析工具，以及开发用于检测差异甲基化区域的通用凹凸搜索方法。

DESCRIPTION (provided by applicant): After more than a decade of improvements to experimental and data analysis techniques, microarray technology is poised to become instrumental in the era of personalized genomics. In fact, Affymetrix, a leading manufacturer, recently achieved the first FDA clearance of high-throughput gene profiling reagents. Microarrays were also crucial in the successful development of an FDA approved breast cancer recurrence assay - making it possible to identify patients at risk of distant recurrence following surgery. Moreover, approximately one half of all Pub Med publications citing microarrays were published during the last two years. We therefore expect laboratories in academia and industry to continue relying on these technologies for several years as newer genomic technologies mature, and that manufacturers will continue to develop new products at a rapid pace. All microarray data analyses begin by converting raw measures into the data and summary statistics relied upon by biologists and clinicians. This first step, referred to as preprocessing, has an enormous influence on the quality of the ultimate measurements and results from studies that rely upon them. Our group has previously demonstrated that statistical methodology can provide great improvements over ad hoc data analysis algorithms offered as defaults by array manufacturers. Our highly cited statistical methodology and our widely used software implementations demonstrate the success of our work. While gene expression has been the most popular microarray application, recently, the technology has been used to measure diverse genomic endpoints including genotype, copy number variants, transcription factor binding sites, and several epigenetic marks, including DNA methylation. During the first funding period, our group was dedicated to understanding the bias and systematic errors which can obscure results, thwart discovery, and contribute to findings that are not reproducible. We have amassed expertise and developed successful data analysis tools to effectively preprocess raw data, making the technology prime for translational research and clinical applications. However, this transition from basic to clinical research will generate new statistical challenges and our methodology, which has partly facilitated the success of microarrays, will play an important role in the promising next period of research driven by microarray technology. Our goal is to develop the next generation of preprocessing and analysis tools with an emphasis on translational applications. Toward this goal, the current proposal has the following specific aims: developing single array preprocessing methodology with emphasis on batch effect removal, developing microarray analysis tools for three urgent needs, and developing generalized bump hunting methodology for detecting differentially methylated regions.