Bioinformatics Techniques in Microarray Research: Applied Microarray Data Analysis Using R and SAS Software
Bioinformatics Techniques in Microarray Research: Applied Microarray Data Analysis Using R and SAS Software
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DOI：
10.1007/978-1-60761-820-1_25
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
Papapanou, Panos N.
中科院分区：
文献类型：
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作者：
Demmer, Ryan T.;Pavlidis, Paul;Papapanou, Panos N.
探索疾病的潜在生物学机制对于许多目的都是有用的，例如除了为正在进行的危险因素研究提供信息之外，还可以开发新的治疗方式。 DNA 微阵列技术是一种相对较新的新颖方法，用于进行全基因组基因表达研究，以确定以前未知的与疾病相关的生物途径。微阵列实验产生的大量数据不利于传统的分析方法。除了分析挑战之外，还存在与结果的解释和呈现相关的同样重要的问题。本章概述了分析微阵列数据的适当技术，该技术还可以产生给定实验中具有差异表达的顶级基因列表。顶级基因列表的衍生物可以用作呈现研究结果的起点。该列表还可作为与增强结果解释和呈现相关的其他技术的基础。本章描述的所有分析都可以使用相对有限的计算资源来执行，例如具有至少 2.0 GB 内存和 2.0 GHz 处理速度的笔记本电脑。
Exploration of the underlying biological mechanisms of disease is useful for many purposes such as the development of novel treatment modalities in addition to informing on-going risk factor research. DNA-microarray technology is a relatively recent and novel approach to conducting genome-wide gene expression studies to identify previously unknown biological pathways associated with disease. The copious data arising from microarray experiments is not conducive to traditional analytical approaches. Beyond the analytical challenges, there are equally important issues related to the interpretation and presentation of results. This chapter outlines appropriate techniques for analyzing microarray data in a fashion that also yields a list of top genes with differential expression in a given experiment. Derivatives of the top genes list can be used as a starting point for the presentation of study results. The list also serves as the basis for additional techniques related to enhanced interpretation and presentation of results. All analyses described in this chapter can be performed using relatively limited computational resources such as a lap top PC with at least 2.0 GB of memory and 2.0 GHz of processing speed.