Microarray Data Analysis for Transcriptome Profiling

Ming-An Sun; Xiaojian Shao; Yejun Wang

doi:10.1007/978-1-4939-7710-9_2

Microarray Data Analysis for Transcriptome Profiling

Methods Mol Biol. 2018:1751:17-33. doi: 10.1007/978-1-4939-7710-9_2.

Authors

Ming-An Sun¹, Xiaojian Shao^{2

3}, Yejun Wang⁴

Affiliations

¹ Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA. mingansun@gmail.com.
² Department of Human Genetics, McGill University, Montréal, Canada.
³ The McGill University and Génome Québec Innovation Centre, Montréal, QC, Canada.
⁴ Department of Cell Biology and Genetics, School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China.

PMID: 29508287
DOI: 10.1007/978-1-4939-7710-9_2

Abstract

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.

Keywords: Bioconductor; Clustering; Differential expression; GeneFilter; Limma; Microarray; Normalization.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods*
Data Analysis*
Gene Expression Profiling / methods*
Humans
Oligonucleotide Array Sequence Analysis / methods*
Software