探索下一代测序和微阵列数据元分析的可行性。
Exploring the feasibility of next-generation sequencing and microarray data meta-analysis.
作者信息
Wu Po-Yen, Phan John H, Wang May D
机构信息
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. pwu33@ gatech.edu
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7618-21. doi: 10.1109/IEMBS.2011.6091877.
Emerging next-generation sequencing (NGS) technology potentially resolves many issues that prevent widespread clinical use of gene expression microarrays. However, the number of publicly available NGS datasets is still smaller than that of microarrays. This paper explores the possibilities for combining information from both microarray and NGS gene expression datasets for the discovery of differentially expressed genes (DEGs). We evaluate several existing methods in detecting DEGs using individual datasets as well as combined NGS and microarray datasets. Results indicate that analysis of combined NGS and microarray data is feasible, but successful detection of DEGs may depend on careful selection of algorithms as well as on data normalization and pre-processing.
新兴的下一代测序（NGS）技术有可能解决许多阻碍基因表达微阵列在临床广泛应用的问题。然而，公开可用的NGS数据集数量仍少于微阵列数据集。本文探讨了结合微阵列和NGS基因表达数据集的信息来发现差异表达基因（DEG）的可能性。我们评估了几种使用单个数据集以及NGS和微阵列组合数据集检测DEG的现有方法。结果表明，对NGS和微阵列数据进行组合分析是可行的，但成功检测DEG可能取决于算法的精心选择以及数据归一化和预处理。
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