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Microarray Data Analysis for Transcriptome Profiling

  • Ming-an Sun
  • Xiaojian Shao
  • Yejun Wang
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1751)

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.

Key words

Microarray Normalization Clustering Differential expression Bioconductor Limma GeneFilter 

Notes

Acknowledgments

This work was supported by a Natural Science Funding of Shenzhen (JCYJ201607115221141) and a Shenzhen Peacock Plan fund (827-000116) to YW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Ming-an Sun
    • 1
  • Xiaojian Shao
    • 2
    • 3
  • Yejun Wang
    • 4
  1. 1.Epigenomics and Computational Biology LabBiocomplexity Institute of Virginia TechBlacksburgUSA
  2. 2.Department of Human GeneticsMcGill UniversityMontréalCanada
  3. 3.The McGill University and Génome Québec Innovation CentreMontréalCanada
  4. 4.Department of Cell Biology and Genetics, School of Basic MedicineShenzhen University Health Science CenterShenzhenChina

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