Methods Inf Med 2005; 44(03): 418-422
DOI: 10.1055/s-0038-1633987
Original Article
Schattauer GmbH

Normalization for Two-channel Microarray Data

C. Ittrich
1   Central Unit Biostatistics, German Cancer Research Center, Heidelberg, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
06 February 2018 (online)

Summary

Objectives: In two-channel microarray experiments the measured gene expression levels are affected by many sources of systematic variation. Normalization refers to the process of removing such systematic sources of variation, to make measured intensities within and between slides comparable. Some commonly used normalization methods removing intensity-dependent dye bias and adjusting differences in variability between slides will be reviewed with the main focus on intensity-dependent normalization methods.

Methods: This article describes different intensity-dependent within-slide normalization methods for the log ratios of red and green channel intensities but also refers to single channel normalization methods incorporating all single channels of the slides at once.

Results: The described procedures provide a useful approach to remove systematic sources of variation like intensity-dependent dye bias and variability between slides in cDNA microarray experiments. This is illustrated by an experimental data set.

Conclusions: Several reasonable normalization procedures for two-channel microarray data have recently been proposed. Deciding on which method would perform well for a concrete experiment is difficult. Designed spike-in experiments or dilution series with known differences for some selected genes would be helpful to assess the different methods, but may be impractical for most laboratories due to the high costs.

 
  • References

  • 1 Nguyen DH, Arpat AB, Wang N, Carroll RJ. DNA Microarray Experiments: Biological and Technological Aspects. Biometrics 2002; 58: 701-17.
  • 2 Repsilber D, Ziegler A. Two-color microarray experiments: teechnology and sources of variance. Methods Inf Med 2005; 44: 400-4. (this issue)
  • 3 Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 2002; 30: e15
  • 4 Huber W, von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002; 18 (Suppl. 01) Supplement 96-104.
  • 5 Yang YH, Thorne NP. Normalization for two-color cDNA microarray data. In. Goldstein DR. (ed.) Science and Statistics: A Festschrift for Terry Speed. IMS Lecture Notes – Monograph Series 2003; Volume 40: 403-18.
  • 6 Kerr M, Martin M, Churchill G. Analysis of variance in microarray data. Journal of Computational Biology 2000; 7: 819-37.
  • 7 Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology 2001; 8 (06) 625-37.
  • 8 Bretz F, Landgrebe J, Brunner E. Design and Analysis of Two-Color Microarray Experiments Using Linear Models. Methods Inf Med 2005; 44: 423-30. (this issue)
  • 9 Dudoit S, Yang YH, Callow MJ, Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002; 12 (01) 111-39.
  • 10 Cleveland WS. Robust locally weighted regression and smoothing scatterplots. JASA 1979; 74: 829-36.
  • 11 Cleveland WS, Grosse E, Shyu WM. Local regression models. Chapter 8 of Statistical Models in S. Chambers JM, Hastie TJ. (eds.) Pacific Grove, California: Wadsworth & Brooks/Cole; 1992
  • 12 Bolstad BM, Irizarry RA, Astrand M, Speed TP. A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 2003; 19: 185-93.
  • 13 Cope LM, Irizarry RA, Jaffee H, Wu Z, Speed TP. A Benchmark for Affymetrix Gene Chip Expression Measures. Bioinformatics 2003; 20: 323-31.
  • 14 Durbin BP, Hardin JS, Hawkins DM, Rocke DM. A variance-stabilizing transformation for geneexpression microarray data. Bioinformatics 2002; 18 (Suppl. 01) Suppl S105-S110.
  • 15 Cui X, Kerr K, Churchill G. Data transformations for cDNA microarray data. Statistical Applications in Genetics and Molecular Biology 2002; 2 (01) Article 4
  • 16 Rocke DM, Durbin BP. A model for measurement error for gene expression analysis. J Comp Biol 2001; 8: 557-69.
  • 17 Rocke DM, Durbin B. Approximate variancestabilizing transformations for gene-expression microarray data. Bioinformatics 2003; 19 (08) 966-72.
  • 18 Speed TP. editor Statistical Analysis of Gene Expression Microarray Data. Boca Raton, Florida: Chapman & Hall/CRC; 2003
  • 19 Parmigiani G, Garrett ES, Irizarry RA, Zeger SL. editors The Analysis of Gene Expression Data: Methods and Software. Heidelberg: Springer; 2003
  • 20 Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y. Design and Analysis of DNA Microarray Investigations. New York: Springer; 2003
  • 21 Schadt EE, Li C, Ellis B, Wong WH. Feature extraction and normalization algorithms for highdensity oligonucleotide gene expression array data. Journal of Cellular Biochemistry 2001; 84 S37 120-5.
  • 22 Tseng GC, Oh M-K. Rohlin L, Liao JC, Wong WH. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research 2001; 29 12: 2549-57.
  • 23 Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. New York: Springer; 2001