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Microarray Bioinformatics pp 87–121Cite as

Statistical Analysis of Microarray Data

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1986))

Abstract

Microarray data analysis has been one of the most important hits in the interaction between statistics and bioinformatics in the last two decades. The analysis of microarray data can be done in different ways using different tools. In this chapter a typical workflow for analyzing microarray data using R and Bioconductor packages is presented. The workflow starts with the raw data—binary files obtained from the hybridization process—and goes through a series of steps: Reading raw data, Quality Check, Normalization, Filtering, Selection of differentially expressed genes, Comparison of selected lists, and Analysis of Biological Significance. The implementation of each step in R is described through a use case that goes from raw data until the analysis of biological significance. Data and code for the analysis are provided in a github repository.

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Author information

Authors and Affiliations

  1. Statistics and Bioinformatics Unit (UEB), Vall d’Hebron Research Institute (VHIR), Barcelona, Spain

    Ricardo Gonzalo Sanz & Alex Sánchez-Pla

  2. Genetics Microbiology and Statistics Department, University of Barcelona, Barcelona, Spain

    Alex Sánchez-Pla

Authors
  1. Ricardo Gonzalo Sanz
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  2. Alex Sánchez-Pla
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Corresponding author

Correspondence to Alex Sánchez-Pla .

Editor information

Editors and Affiliations

  1. CITIC, Universidade da Coruña, A Coruña, Spain

    Verónica Bolón-Canedo

  2. CITIC, Universidade da Coruña, A Coruña, Spain

    Amparo Alonso-Betanzos

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Gonzalo Sanz, R., Sánchez-Pla, A. (2019). Statistical Analysis of Microarray Data. In: Bolón-Canedo, V., Alonso-Betanzos, A. (eds) Microarray Bioinformatics. Methods in Molecular Biology, vol 1986. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9442-7_5

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  • DOI: https://doi.org/10.1007/978-1-4939-9442-7_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9441-0

  • Online ISBN: 978-1-4939-9442-7

  • eBook Packages: Springer Protocols

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