Advertisement

Pipeline for Integrated Microarray Expression Normalization Tool Kit (PIMENTo) for Tumor Microarray Profiling Experiments

  • Thomas Nash
  • Matthew Huff
  • W. Bailey GlenJr.
  • Gary HardimanEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1908)

Abstract

We have developed a Pipeline for Integrated Microarray Expression & Normalization Tool kit (PIMENTo) with the aim of streamlining the processes necessary for gene expression analysis in tumor tissue using DNA microarrays. Built with the R programming language and leveraging several open-source packages available through CRAN and Bioconductor, PIMENTo enables researchers to perform complex tasks with a minimal number of operations. Here, we describe the pipeline, review necessary data inputs, examine data outputs and quality control assessments and explore the commands to perform such analysis.

Key words

Microarrays Genomic profiling Tumor Illumina Bead arrays Normalization Quality control 

Notes

Acknowledgments

GH is grateful for MUSC College of Medicine Institutional start-up funds. We thank Dr. Willian A da Silveira for critical reading of the manuscript.

References

  1. 1.
    Chee M, Yang R, Hubbell E et al (1996) Accessing genetic information with high-density DNA arrays. Science 274(5287):610–614CrossRefGoogle Scholar
  2. 2.
    Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470CrossRefGoogle Scholar
  3. 3.
    van 't Veer LJ, Dai H, Van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536CrossRefGoogle Scholar
  4. 4.
    Trachtenberg AJ, Robert JH, Abdalla AE et al (2012) A primer on the current state of microarray technologies. In: Wang J et al (eds) Next generation microarray bioinformatics: methods and protocols, methods in molecular biology, vol 802. Springer, New York, pp 3–17CrossRefGoogle Scholar
  5. 5.
    Hardiman G (2003) Microarrays methods and applications: Nuts & Bolts. DNA Press, Eagleville PAGoogle Scholar
  6. 6.
    Hardiman G (2004) Microarray platforms—comparisons and contrasts. Pharmacogenomics 5(5):487–502CrossRefGoogle Scholar
  7. 7.
    Ragoussis J, Elvidge G (2006) Affymetrix GeneChip system: moving from research to the clinic. Expert Rev Mol Diagn 6(2):145–152CrossRefGoogle Scholar
  8. 8.
    Hardiman G, Carmen A (2006) DNA biochips—past, present and future; an overview. Taylor & Francis, NY, USAGoogle Scholar
  9. 9.
    Hughes TR, Mao M, Jones AR et al (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19(4):342–347CrossRefGoogle Scholar
  10. 10.
    Gunderson KL, Steemers FJ, Lee G et al (2005) A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 37(5):549–554CrossRefGoogle Scholar
  11. 11.
    Vilo J, Kivinen K (2001) Regulatory sequence analysis: application to the interpretation of gene expression. Eur Neuropsychopharmacol 11(6):399–411CrossRefGoogle Scholar
  12. 12.
    Wick I, Hardiman G (2005) Biochip platforms as functional genomics tools for drug discovery. Curr Opin Drug Discov Devel 8(3):347–354PubMedGoogle Scholar
  13. 13.
    Rouse RJ, Field K, Lapira J et al (2008) Development and application of a microarray meter tool to optimize microarray experiments. BMC Res Notes 1:45CrossRefGoogle Scholar
  14. 14.
    Chen C, Grennan K, Badner J et al (2011) Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PLoS One 6(2):e17238CrossRefGoogle Scholar
  15. 15.
    Oeser SG, Baker SC, Chudin E et al (2009) Methods for assessing microarray performance. In: Hardiman G (ed) Microarray innovations: technology and experimentation. CRC Press, Florida, pp 119–125CrossRefGoogle Scholar
  16. 16.
    Taniguchi K, Wu LW, Grivennikov SI et al (2015) A gp130-Src-YAP module links inflammation to epithelial regeneration. Nature 519(7541):57–62CrossRefGoogle Scholar
  17. 17.
    Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193CrossRefGoogle Scholar
  18. 18.
    Cleveland WS (1981) LOWESS: a program for smoothing scatterplots by robust locally weighted regression. Am Stat 35(1):54CrossRefGoogle Scholar
  19. 19.
    Yang YH, Dudoit S, Luu P et al (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30(4):e15–e15CrossRefGoogle Scholar
  20. 20.
    Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244CrossRefGoogle Scholar
  21. 21.
    Leek JT, Scharpf RB, Bravo HC et al (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733–739CrossRefGoogle Scholar
  22. 22.
    Stafford P (2008) Methods in microarray normalization. CRC Press, FloridaCrossRefGoogle Scholar
  23. 23.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 98(9):5116–5121CrossRefGoogle Scholar
  24. 24.
    Tibshirani R, Chu G, Narasimhan B, Li J (2011) samr: SAM: Significance Analysis of Microarrays. R package version 2.0Google Scholar

Copyright information

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

Authors and Affiliations

  • Thomas Nash
    • 1
    • 2
  • Matthew Huff
    • 3
  • W. Bailey GlenJr.
    • 1
    • 4
  • Gary Hardiman
    • 1
    • 5
    • 6
    • 7
    Email author
  1. 1.MUSC Bioinformatics, Center for Genomics MedicineMedical University of South Carolina (MUSC)CharlestonUSA
  2. 2.Department of Computer ScienceCollege of CharlestonCharlestonUSA
  3. 3.MS in Biomedical Sciences ProgramMedical University of South Carolina (MUSC)CharlestonUSA
  4. 4.Department of Pathology and Laboratory MedicineMedical University of South Carolina (MUSC)CharlestonUSA
  5. 5.Department of MedicineMedical University of South Carolina (MUSC)CharlestonUSA
  6. 6.Department of Public Health SciencesMedical University of South Carolina (MUSC)CharlestonUSA
  7. 7.School of Biological Sciences & Institute for Global Food SecurityQueens University BelfastStranmillis RoadUK

Personalised recommendations