In microarray technology, a number of critical steps are required to convert the raw measurements into the data relied upon by biologists and clinicians. These data manipulations, referred to as preprocessing, influence the quality of the ultimate measurements and studies that rely upon them. Standard operating procedure for microarray researchers is to use preprocessed data as the starting point for the statistical analyses that produce reported results. This has prevented many researchers from carefully considering their choice of preprocessing methodology. Furthermore, the fact that the preprocessing step affects the stochastic properties of the final statistical summaries is often ignored. In this paper we propose a statistical framework that permits the integration of preprocessing into the standard statistical analysis flow of microarray data. This general framework is relevant in many microarray platforms and motivates targeted analysis methods for specific applications. We demonstrate its usefulness by applying the idea in three different applications of the technology.
A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment
Fully scalable online-preprocessing algorithm for short oligonucleotide microarray atlases
University of Helsinki（赫尔辛基大学）；Wageningen University（瓦赫宁根大学）；European Bioinformatics Institute（欧洲生物信息学研究所）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；University of Turku（图尔库大学）；Turku Centre for Biotechnology（图尔库生物技术中心）
Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor
跨样品的基因的不同转录物的存在可以通过全转录组微阵列来分析。由于大量的数据和在进行实际分析之前采用的各种各样的预处理和过滤步骤，从已发表的微阵列数据再现结果是一个挑战。为了保证方法学发展的坚实基础，将新方法的结果与以前的结果进行比较，确保所有分析对其他研究人员来说是完全可重复的至关重要。我们在这里给出了一个详细的工作流程，关于如何在R/Bioconductor中仅在探针和概率水平上对GeneChip Human Exon 1.0 ST Array进行可重复的分析，并根据其易用性选择软件包。为了验证所提出的工作流程的使用，我们使用各种统计方法分析了公开数据集中的差异剪接和差异基因表达。我们相信这项研究将为其他研究人员提供一种在不同注释水平上访问基因表达数据的简单方法，并提供开发自己的工具以用于GeneChip Human Exon 1.0 ST Array的可重复分析所需的足够细节。
