Advertisement

Statistical Analysis of Microarray Data

Protocol
First Online:
Part of the Methods in Molecular Biology book series (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.

Key words

Microarrays Bioconductor R Differential expression 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Efron B (2013) Large-scale inference: empirical Bayes methods for estimation, testing, and prediction. Cambridge University Press, CambridgeGoogle Scholar
  2. 2.
    Sánchez-Pla A (2014) DNA microarrays technology: overview and current status. In: Carolina Simó AC, García-Cañas V (eds) Comprehensive analytical chemistry, vol 63. Elsevier, pp 1–23Google Scholar
  3. 3.
    Draghici S (2012) Statistics and data analysis for microarrays using R and bioconductor. CRC Press, New YorkGoogle Scholar
  4. 4.
    Sánchez-Pla A, Reverter F, Ruíz de Villa MC, Comabella M (2012) Transcriptomics: mRNA and alternative splicing. J Neuroimmunol 248:23–31.  https://doi.org/10.1016/j.jneuroim.2012.04.008CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Mehta JP, Rani S (2011) Software and tools for microarray data analysis. Methods Mol Biol 784:41–53CrossRefGoogle Scholar
  6. 6.
    Carvalho BS, Irizarry RA (2010) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26:2363–2367.  https://doi.org/10.1093/bioinformatics/btq431CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Carvalho B (2015) Pd.mogene.2.1.st: Platform design info for affymetrix mogene-2.1-stGoogle Scholar
  8. 8.
    Huber W, Carey VJ et al (2015) Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 12:115–121CrossRefGoogle Scholar
  9. 9.
    Kauffmann A, Gentleman R, Huber W (2009) ArrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25:415–416CrossRefGoogle Scholar
  10. 10.
    Warnes GR, Bolker B, Bonebakker L, et al (2016) Gplots: various r programming tools for plotting dataGoogle Scholar
  11. 11.
    Wickham H (2009) Ggplot2: elegant graphics for data analysis. Springer-Verlag, New YorkCrossRefGoogle Scholar
  12. 12.
    Slowikowski K (2017) Ggrepel: repulsive text and label geoms for ‘ggplot2’Google Scholar
  13. 13.
    Bushel P (2013) Pvca: principal variance component analysis (pvca)Google Scholar
  14. 14.
    Smyth GK (2005) limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using r and bioconductor. Springer-Verlag, New York, pp 397–420CrossRefGoogle Scholar
  15. 15.
    Gentleman R, Carey V, Huber W, Hahne F (2017) Genefilter: genefilter: methods for filtering genes from high-throughput experimentsGoogle Scholar
  16. 16.
    Gentleman R (2017) Annotate: annotation for microarraysGoogle Scholar
  17. 17.
    Carlson M (2017) Org.Mm.eg.db: Genome wide annotation for mouseGoogle Scholar
  18. 18.
    MacDonald JW (2017) Mogene21sttranscriptcluster.db: Affymetrix mogene21 annotation data (chip mogene21sttranscriptcluster)Google Scholar
  19. 19.
    Yu G, He Q-Y (2016) ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol BioSyst 12:477–479.  https://doi.org/10.1039/c5mb00663eCrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Li S, Mi L, Yu L et al (2017) Zbtb7b engages the long noncoding rna blnc1 to drive brown and beige fat development and thermogenesis. Proc Natl Acad Sci 114:E7111–E7120.  https://doi.org/10.1073/pnas.1703494114CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Clough E, Barrett T (2016) The Gene Expression Omnibus database. Methods Mol Biol 1418:93–110CrossRefGoogle Scholar
  22. 22.
    Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264.  https://doi.org/10.1093/biostatistics/4.2.249CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hackstadt AJ, Hess AM (2009) Filtering for increased power for microarray data analysis. BMC Bioinformatics 10:11.  https://doi.org/10.1186/1471-2105-10-11CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chrominski K, Tkacz M (2015) Comparison of high-level microarray analysis methods in the context of result consistency. PLoS One 10:e0128845.  https://doi.org/10.1371/JOURNAL.PONE.0128845CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Jeanmougin M, de Reynies A, Marisa L et al (2010) Should we abandon the t-Test in the analysis of gene expression microarray data: a comparison of variance modeling strategies. PLoS One 5:e12336.  https://doi.org/10.1371/journal.pone.0012336CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Allison DB, Cui X, Page GP, Sabripour M (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7:55–65.  https://doi.org/10.1038/nrg1749CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98:5116–5121.  https://doi.org/10.1073/pnas.091062498CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:1–25.  https://doi.org/10.2202/1544-6115.1027CrossRefGoogle Scholar
  29. 29.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300Google Scholar
  30. 30.
    Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375.  https://doi.org/10.1371/journal.pcbi.1002375CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Statistics and Bioinformatics Unit (UEB)Vall d’Hebron Research Institute (VHIR)BarcelonaSpain
  2. 2.Genetics Microbiology and Statistics DepartmentUniversity of BarcelonaBarcelonaSpain

Personalised recommendations

Cite protocol

Buy options