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Statistical Analysis of Microarray Data

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Microarray Bioinformatics

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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Correspondence to Alex Sánchez-Pla .

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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

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