Sun Ming-An, Shao Xiaojian, Wang Yejun
Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA.
Department of Human Genetics, McGill University, Montréal, Canada.
Methods Mol Biol. 2018;1751:17-33. doi: 10.1007/978-1-4939-7710-9_2.
Microarray data have vastly accumulated in the past two decades. Due to the high-throughput characteristic of microarray techniques, it has transformed biological studies from specific genes to transcriptome level, and deeply boosted many fields of biological studies. While microarray offers great advantages for expression profiling, on the other hand it faces a lot challenges for computational analysis. In this chapter, we demonstrate how to perform standard analysis including data preprocessing, quality assessment, differential expression analysis, and general downstream analyses.
在过去二十年中，微阵列数据大量积累。由于微阵列技术的高通量特性，它已将生物学研究从特定基因层面转变到转录组水平，并极大地推动了生物学研究的许多领域。虽然微阵列在表达谱分析方面具有很大优势，但另一方面，它在计算分析上面临诸多挑战。在本章中，我们将演示如何进行标准分析，包括数据预处理、质量评估、差异表达分析以及一般的下游分析。
