Advertisement

Microarray Bioinformatics

  • Robert P. Loewe
  • Peter J. NelsonEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 671)

Abstract

Bioinformatics has become an increasingly important tool for molecular biologists, especially for the analysis of microarray data. Microarrays can produce vast amounts of information requiring a series of consecutive analyses to render the data interpretable. The direct output of microarrays cannot be directly interpreted to show differences in settings, conditions of samples, or time points. To make microarray experiments interpretable, it is necessary that a series of algorithms and approaches be applied. After normalization of generated data, which is necessary to make a comparison feasible, significance analysis, clustering of samples and biological compounds of interest and visualization are generally performed. This chapter will focus on providing a basic understanding of the generally approaches and algorithms currently employed in microarray bioinformatics.

Key words:

Microarray Bioinformatics Normalization Clustering SAM RMA PCA 

Notes

Acknowledgments

This work was supported by the Deutsche Forschungsgemeinschaft SFB 571 C2, FP6 EU grant INNOCHEM to PJN and BMBF BioChance to PJN.

References

  1. 1.
    Angres B. Cell microarrays. Expert Rev Mol Diagn 2005;5(5):769–79.CrossRefGoogle Scholar
  2. 2.
    Chiosis G, Brodsky JL. Small molecule microarrays: from proteins to mammalian cells – are we there yet? Trends Biotechnol 2005;23(6):271–4.CrossRefGoogle Scholar
  3. 3.
    Costa JL, Meijer G, Ylstra B, Caldas C. Array comparative genomic hybridization copy number profiling: a new tool for translational research in solid malignancies. Semin Radiat Oncol 2008;18(2):98–104.CrossRefGoogle Scholar
  4. 4.
    Liang PH, Wu CY, Greenberg WA, Wong CH. Glycan arrays: biological and medical applications. Curr Opin Chem Biol 2008;12(1):86–92.CrossRefGoogle Scholar
  5. 5.
    Liu XS. Getting started in tiling microarray analysis. PLoS Comput Biol 2007;3(10):1842–4.CrossRefGoogle Scholar
  6. 6.
    Lopez MF, Pluskal MG. Protein micro- and macroarrays: digitizing the proteome. J Chromatogr B Analyt Technol Biomed Life Sci 2003;787(1):19–27.CrossRefGoogle Scholar
  7. 7.
    Stadtherr K, Wolf H, Lindner P. An aptamer-based protein biochip. Anal Chem 2005;77(11):3437–43.CrossRefGoogle Scholar
  8. 8.
    Voduc D, Kenney C, Nielsen TO. Tissue microarrays in clinical oncology. Semin Radiat Oncol 2008;18(2):89–97.CrossRefGoogle Scholar
  9. 9.
    Wu P, Castner DG, Grainger DW. Diagnostic devices as biomaterials: a review of nucleic acid and protein microarray surface performance issues. J Biomater Sci Polym Ed 2008;19(6):725–53.CrossRefGoogle Scholar
  10. 10.
    Simon R. Microarray-based expression profiling and informatics. Curr Opin Biotechnol 2008;19(1):26–9.CrossRefGoogle Scholar
  11. 11.
    Knudsen S. Image analysis. In: Guide to analysis of DNA microarray data; 2004.CrossRefGoogle Scholar
  12. 12.
    Cohen CD, Lindenmeyer MT, Eichinger F, et al. Improved elucidation of biological processes linked to diabetic nephropathy by single probe-based microarray data analysis. PLoS One 2008;3(8):e2937.CrossRefGoogle Scholar
  13. 13.
    Henger A, Schmid H, Kretzler M. Gene expression analysis of human renal biopsies: recent developments towards molecular diagnosis of kidney disease. Curr Opin Nephrol Hypertens 2004;13(3):313–8.CrossRefGoogle Scholar
  14. 14.
    Scherer A, Krause A, Walker JR, et al. Opti­mized protocol for linear RNA amplification and application to gene expression profiling of human renal biopsies. Biotechniques 2003;34(3):546–50, 52–4, 56.Google Scholar
  15. 15.
    Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001;29(4):365–71.CrossRefGoogle Scholar
  16. 16.
    Rogers S, Cambrosio A. Making a new technology work: the standardization and regulation of microarrays. Yale J Biol Med 2007;80(4):165–78.Google Scholar
  17. 17.
    Edgar R, Barrett T. NCBI GEO standards and services for microarray data. Nat Biotechnol 2006;24(12):1471–2.CrossRefGoogle Scholar
  18. 18.
    Gardiner-Garden M, Littlejohn TG. A comparison of microarray databases. Brief Bioinform 2001;2(2):143–58.CrossRefGoogle Scholar
  19. 19.
    Shi L, Reid LH, Jones WD, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006;24(9):1151–61.CrossRefGoogle Scholar
  20. 20.
    Durinck S. Pre-processing of microarray data and analysis of differential expression. Methods Mol Biol 2008;452:89–110.CrossRefGoogle Scholar
  21. 21.
    Dudoit S, Yang YH, Callow MJ, Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 2002;12:111–39.Google Scholar
  22. 22.
    Ritchie ME, Silver J, Oshlack A, et al. A comparison of background correction methods for two-colour microarrays. Bioinformatics 2007;23(20):2700–7.CrossRefGoogle Scholar
  23. 23.
    Yang YH, Dudoit S, Luu P, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002;30(4):e15.CrossRefGoogle Scholar
  24. 24.
    de Longueville F, Atienzar FA, Marcq L, et al. Use of a low-density microarray for studying gene expression patterns induced by hepatotoxicants on primary cultures of rat hepatocytes. Toxicol Sci 2003;75(2):378–92.CrossRefGoogle Scholar
  25. 25.
    de Longueville F, Surry D, Meneses-Lorente G, et al. Gene expression profiling of drug metabolism and toxicology markers using a low-density DNA microarray. Biochem Pharmacol 2002;64(1):137–49.CrossRefGoogle Scholar
  26. 26.
    Calza S, Valentini D, Pawitan Y. Normalization of oligonucleotide arrays based on the least-variant set of genes. BMC Bioinformatics 2008;9:140.CrossRefGoogle Scholar
  27. 27.
    Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A 2001;98(1):31–6.CrossRefGoogle Scholar
  28. 28.
    Hochreiter S, Clevert DA, Obermayer K. A new summarization method for Affymetrix probe level data. Bioinformatics 2006;22(8):943–9.CrossRefGoogle Scholar
  29. 29.
    Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4(2):249–64.CrossRefGoogle Scholar
  30. 30.
    Harbron C, Chang KM, South MC. RefPlus: an R package extending the RMA algorithm. Bioinformatics 2007;23(18):2493–4.CrossRefGoogle Scholar
  31. 31.
    Holder D, Raubertas RF, Pikounis VB, Svetnik V, Soper K. Statistical analysis of high density oligonucleotide arrays: A SAFER approach. In: ASA annual meeting. Atlanta, GA; 2001.Google Scholar
  32. 32.
    Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol 2000;7(6):819–37.CrossRefGoogle Scholar
  33. 33.
    de Haan JR, Wehrens R, Bauerschmidt S, Piek E, van Schaik RC, Buydens LM. Interpretation of ANOVA models for microarray data using PCA. Bioinformatics 2007;23(2):184–90.CrossRefGoogle Scholar
  34. 34.
    Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98(9):5116–21.CrossRefGoogle Scholar
  35. 35.
    Kadota K, Nakai Y, Shimizu K. A weighted average difference method for detecting differentially expressed genes from microarray data. Algorithms Mol Biol 2008;3:8.CrossRefGoogle Scholar
  36. 36.
    Zhao H, Chan KL, Cheng LM, Yan H. Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments. BMC Bioinformatics 2008;9(Suppl 1):S9.CrossRefGoogle Scholar
  37. 37.
    Schreiber F. Visualization. Methods Mol Biol 2008;453:441–50.CrossRefGoogle Scholar
  38. 38.
    MacQueen J. Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley symposium on mathematical statistics and probability. University of California Press, Berkeley, CA; 1967. p. 281–97.Google Scholar
  39. 39.
    Goldstein DR, Ghosh D, Conlon EM. Statistical issues in the clustering of gene expression data. Stat Sin 2002;12:219–40.Google Scholar
  40. 40.
    Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998;95(25):14863–8.CrossRefGoogle Scholar
  41. 41.
    McLachlan GJ, Bean RW, Ng SK. Clustering. Methods Mol Biol 2008;453:423–39.CrossRefGoogle Scholar
  42. 42.
    Chen G, Jaradat SA, Banerjee N, Tanaka TS, Ko MSH, Zhang MQ. Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data. Stat Sin 2002;12:241–62.Google Scholar
  43. 43.
    Kim SY, Lee JW, Bae JS. Effect of data normalization on fuzzy clustering of DNA microarray data. BMC Bioinformatics 2006;7:134.CrossRefGoogle Scholar
  44. 44.
    Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000;97(18):10101–6.CrossRefGoogle Scholar
  45. 45.
    Daffertshofer A, Lamoth CJ, Meijer OG, Beek PJ. PCA in studying coordination and variability: a tutorial. Clin Biomech 2004;19(4):415–28.CrossRefGoogle Scholar
  46. 46.
    Hubert M, Engelen S. Robust PCA and classification in biosciences. Bioinformatics 2004;20(11):1728–36.CrossRefGoogle Scholar
  47. 47.
    D’Souza M, Zhu X, Frisina RD. Novel approach to select genes from RMA normalized microarray data using functional hearing tests in aging mice. J Neurosci Methods 2008;171(2):279–87.CrossRefGoogle Scholar
  48. 48.
    Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007;23(3):306–13.CrossRefGoogle Scholar
  49. 49.
    Dopazo J, Al-Shahrour F. Expression and microarrays. Methods Mol Biol 2008;453:245–55.CrossRefGoogle Scholar
  50. 50.
    Werner T. Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol 2008;19(1):50–4.CrossRefGoogle Scholar
  51. 51.
    Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5(10):R80.CrossRefGoogle Scholar
  52. 52.
    Okoniewski MJ, Miller CJ. Comprehensive analysis of affymetrix exon arrays using BioConductor. PLoS Comput Biol 2008;4(2):e6.CrossRefGoogle Scholar
  53. 53.
    Popova T, Mennerich D, Weith A, Quast K. Effect of RNA quality on transcript intensity levels in microarray analysis of human post-mortem brain tissues. BMC Genomics 2008;9:91.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Medical Policlinic, Ludwig MaximilliansUniversity of MunichMunichGermany

Personalised recommendations