Skip to main content

Gene Expression Data Analysis

  • Chapter
  • First Online:
Genome Data Analysis

Part of the book series: Learning Materials in Biosciences ((LMB))

  • 2971 Accesses

Abstract

The objectives of this chapter are to teach generating DEGs in microarray gene expression data, extracting a gene cluster of genes with similar patterns of expression, classifying the observed data using SVM and KNN, and learning the basic syntax of the R program, a useful tool for genome data analysis.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    BCR/ABL fusion gene, cytogenetically normal, and ALL1/AF4 fusion gene.

Bibliography

  1. Chiaretti S et al (2004) Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103(7):2771–2778

    Article  CAS  Google Scholar 

  2. Eisen MB et al (1998) Cluster analysis and display of genome-wide expression patterns. PNAS 95(25):14863–14868

    Article  CAS  Google Scholar 

  3. Gentleman R (2016) Annotate: annotation for microarrays. R package version 1.52.1

    Google Scholar 

  4. Gentleman R, Carey V, Huber W, Hahne F (2016) genefilter: genefilter: methods for filtering genes from high-throughput experiments. R package version 1.56.0

    Google Scholar 

  5. Golub T (2016) golubEsets: exprSets for golub leukemia data. R package version 1.16.0

    Google Scholar 

  6. Huber W et al (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(suppl 1):S96–S104

    Article  Google Scholar 

  7. Li X (2009) ALL: a data package. R package version 1.16.0

    Google Scholar 

  8. Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2017) e1071: misc functions of the department of statistics, probability theory group (formerly: E1071), TU Wien. R package version 1.6-8. https://CRAN.R-project.org/package=e1071

  9. Tamayo P et al (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. PNAS 96(6):2907–2912

    Article  CAS  Google Scholar 

  10. Tibshirani R, Chu G, Narasimhan B, Li J (2011) samr: SAM: Significance analysis of microarrays. R package version 2.0. https://CRAN.R-project.org/package=samr

  11. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York. ISBN 0-387-95457-0

    Book  Google Scholar 

  12. Yan J (2016) som: Self-Organizing Map. R package version 0.3-5.1. https://CRAN.R-project.org/package=som

Download references

Author information

Authors and Affiliations

Authors

1 Electronic Supplementary Material

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kim, J.H. (2019). Gene Expression Data Analysis. In: Genome Data Analysis. Learning Materials in Biosciences. Springer, Singapore. https://doi.org/10.1007/978-981-13-1942-6_6

Download citation

Publish with us

Policies and ethics