Springer Protocols: Abstract: 4. Pre-Processing of Microarray Data and Analysis of Differential Expression

4. Pre-Processing of Microarray Data and Analysis of Differential Expression

By: Steffen Durinck³

Abstract

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Microarrays have become a widely used technology in molecular biology research. One of their main uses is to measure gene expression. Compared to older expression measuring assays such as Northern blotting, analyzing gene expression data from microarrays is inherently more complex due to the massive amounts of data they produce. The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming. Many software tools for microarray data analysis are available. Currently one of the most popular and freely available software tools is Bioconductor. This chapter uses Bioconductor to preprocess microarray data, detect differentially expressed genes, and annotate the gene lists of interest.

Affiliation(s): (3) Katholieke Universiteit Leuven, Leuven, Belgium

Book Title: Bioinformatics: Data, Sequence Analysis and Evolution

Series: Methods in Molecular Biology | Volume: 452 | Pub. Date: May-01-2008 | Page Range: 89-110 | DOI: 10.1007/978-1-60327-159-2_4

Subject: Bioinformatics

Key Words: microarray - normalization - bioconductor - R - differential gene expression

References

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1.	Yang, Y. H., 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.

2.	Zakharin, S. O., Kim, K., Mehta, T., et al. (2005) Sources of variation in Affymetrix microarray experiments.BMC Bioinformatics 6, 214.

3.	Affymetrix (2002) Statistical Algorithms Description Documenthttp://www.affyme-trix.com/support/technical/whitepapers/ sadd_whitepaper.pdf

4.	Li, C., Wong, W. H. (2001) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection.Proc Natl Acad Sci U S A 98(1), 31–36.

5.	Irizarry, R. A., Hobbs, B., Collin, F., et al. (2003) Exploration, normalization, and summaries of high-density oligonucleotide array probe level data.Biostatistics 4, 249–264.

6.	Wu, Z., Irizarry, R., Gentleman, R., et al. (2004) A model based background adjustment for oligonucleotide expression arrays.JAMA 99(468), 909–917.

7.	Huber, W., von Heydebreck, A., Suelt-mann, H., et al. (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression.Bioinformatics 18, S96–S104.

8.	Bolstad, B. M., Irizarry, R. A., Astrand, M., et al. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.Bioinformatics 19(2), 185–193.

9.	Cope, L., Irizarry, R, Jaffee, H., et al. (2004) A benchmark for Affymetrix Gene-Chip expression measures.Bioinformatics 20(3), 323–331.

10.	Shedden, K., Chen, W., Kuick, R., et al. (2005) Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.BMC Bioinformatics 6(1), 26.

11.	Van de Peppel, J., Kemmeren, P., van Bakel, H., et al. (2003) Monitoring global messenger RNA changes in externally controlled microarray experiments.EMBO Repts 4(4), 387–393.

12.	Workman, C., Jensen, L. J., Jarmer, H., et al. (2002) A new non-linear normailzation method for reducing variability in DNA microarray experiments.Genome Biology 3(9), research0048.

13.	Kerr, K., Martin, M., Churchill, G. (2000) Analysis of Variance for gene expression microarray data.J Comput Biol 7, 819–837.

14.	Tusher, V. G., Tibshirani, R., Chu, G. (2001) Significance analysis of micro-arrays applied to the ionizing radiation response.Proc Natl Acad Sci U S A 98(9), 5116–5121.

15.	Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.Stat Appl Gen Mol Biol 3(1), Article 3.

16.	Smyth, G. K., Michaus, J., Scott, H. (2005). The use of within-array replicate spots for assessing differential expression in microarray experiments.Bioinformatics 21(9), 2067–2075.

17.	Durinck, S., Moreau, Y., Kasprzyk, A., et al. (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.Bioinformatics 21, 3439–3440.

18.	Zhang, J., Carey, V., Gentleman, R. (2003) An extensible application for assembling annotation for genomic data.Bioinformatics 19(1), 155–156.

19.	Kasprzyk, A., Keefe, D., Smedley, D., et al. (2004) EnsMart: a generic system for fast and flexible access to biological data.Genome Res 14(1), 160–169.

20.	Gentleman, R. C., Carey, V. J., Bates, D. M., et al. (2004) Bioconductor: open software development for computational biology and bioinformatics.Genome Biol 5, R80.

21.	Gentleman, R. C., Carey, V., Huber, W., et al. (2005)Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, NY.

22.	Gautier, L., Cope L., Bolstad, B. M., et al. (2004) Affy: analysis of Affymetrix Gene-Chip data at the probe level.Bioinformatics 20(3), 307–315.

23.	Dudoit, S., Yang, Y. H., Callow, M. J., et al. (2002) Statistical methods for identifying genes with differential expression in replicated cDNA microarray experiments.Stat Sin 12, 111–139.

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