Skip to main content
SpringerLink
Log in

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

  • Protocol
  • First Online:
Tumor Profiling

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

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chee M, Yang R, Hubbell E et al (1996) Accessing genetic information with high-density DNA arrays. Science 274(5287):610–614

    Article  CAS  Google Scholar 

  2. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470

    Article  CAS  Google Scholar 

  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):530–536

    Article  CAS  Google Scholar 

  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 3–17

    Chapter  Google Scholar 

  5. Hardiman G (2003) Microarrays methods and applications: Nuts & Bolts. DNA Press, Eagleville PA

    Google Scholar 

  6. Hardiman G (2004) Microarray platforms—comparisons and contrasts. Pharmacogenomics 5(5):487–502

    Article  CAS  Google Scholar 

  7. Ragoussis J, Elvidge G (2006) Affymetrix GeneChip system: moving from research to the clinic. Expert Rev Mol Diagn 6(2):145–152

    Article  CAS  Google Scholar 

  8. Hardiman G, Carmen A (2006) DNA biochips—past, present and future; an overview. Taylor & Francis, NY, USA

    Google Scholar 

  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):342–347

    Article  CAS  Google Scholar 

  10. Gunderson KL, Steemers FJ, Lee G et al (2005) A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 37(5):549–554

    Article  CAS  Google Scholar 

  11. Vilo J, Kivinen K (2001) Regulatory sequence analysis: application to the interpretation of gene expression. Eur Neuropsychopharmacol 11(6):399–411

    Article  CAS  Google Scholar 

  12. Wick I, Hardiman G (2005) Biochip platforms as functional genomics tools for drug discovery. Curr Opin Drug Discov Devel 8(3):347–354

    CAS  PubMed  Google Scholar 

  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 1:45

    Article  Google Scholar 

  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):e17238

    Article  CAS  Google Scholar 

  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 119–125

    Chapter  Google Scholar 

  16. Taniguchi K, Wu LW, Grivennikov SI et al (2015) A gp130-Src-YAP module links inflammation to epithelial regeneration. Nature 519(7541):57–62

    Article  CAS  Google Scholar 

  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):185–193

    Article  CAS  Google Scholar 

  18. Cleveland WS (1981) LOWESS: a program for smoothing scatterplots by robust locally weighted regression. Am Stat 35(1):54

    Article  Google Scholar 

  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–e15

    Article  Google Scholar 

  20. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244

    Article  Google Scholar 

  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):733–739

    Article  CAS  Google Scholar 

  22. Stafford P (2008) Methods in microarray normalization. CRC Press, Florida

    Book  Google Scholar 

  23. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 98(9):5116–5121

    Article  CAS  Google Scholar 

  24. Tibshirani R, Chu G, Narasimhan B, Li J (2011) samr: SAM: Significance Analysis of Microarrays. R package version 2.0

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. MUSC Bioinformatics, Center for Genomics Medicine, Medical University of South Carolina (MUSC), Charleston, SC, USA

    Thomas Nash, W. Bailey Glen Jr. & Gary Hardiman

  2. Department of Computer Science, College of Charleston, Charleston, SC, USA

    Thomas Nash

  3. MS in Biomedical Sciences Program, Medical University of South Carolina (MUSC), Charleston, SC, USA

    Matthew Huff

  4. Department of Pathology and Laboratory Medicine, Medical University of South Carolina (MUSC), Charleston, SC, USA

    W. Bailey Glen Jr.

  5. Department of Medicine, Medical University of South Carolina (MUSC), Charleston, SC, USA

    Gary Hardiman

  6. Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC, USA

    Gary Hardiman

  7. School of Biological Sciences & Institute for Global Food Security, Queens University Belfast, Stranmillis Road, Belfast, BT9 5AG, UK

    Gary Hardiman

Authors
  1. Thomas Nash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Huff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. W. Bailey Glen Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gary Hardiman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gary Hardiman .

Editor information

Editors and Affiliations

  1. Department of Pathology, University of California San Diego, La Jolla, CA, USA

    Sarah S. Murray

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Nash, T., Huff, M., Glen, W.B., Hardiman, G. (2019). Pipeline for Integrated Microarray Expression Normalization Tool Kit (PIMENTo) for Tumor Microarray Profiling Experiments. In: Murray, S. (eds) Tumor Profiling. Methods in Molecular Biology, vol 1908. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9004-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9004-7_11

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9002-3

  • Online ISBN: 978-1-4939-9004-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation