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Microarray Data Analysis : References
An MA-plot of microarray data is a plot of log-ratio of two expression intensities versus the mean log-expression of the two. In terms of log-expression values, an MA-plot can be seen as a mean-difference plot. MA-plots are especially applied to the red and green channels of two-colour arrays although they are useful for single-channel arrays as well. An MA-plot of two-colour data is a representation of the data from one array. For single-channel arrays one needs as least two arrays to make an MA-plot.
The original paper which introduced the idea and coined the name for two colour microarray data was:
Dudoit, S., Yang, Y. H, Callow, M. J., and Speed, T. P. (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12, 111-140. (Tech report #578) (January 2002)
MA-plots for single channel microarray platforms such as Affymetrix are computed from the means and differences of log-expression values from two microarrays. Such plots were introduced and used by
Bolstad, B. M., Irizarry, R. A., Astrand, M., Speed TP (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185-193.
The underlying idea of plotting differences versus means of correlated measurements goes back at least to early work on plots by John Tukey. Such plots are sometimes called Tukey mean-difference plots, for example in:
Chambers, J. M., Cleveland, W. S., Kleiner, B., and Tukey, P. A. (1983). Graphical Methods of Data Analysis. Wadsworth (pp. 48-57).
Cleveland, W. S., (1993). Visualizing Data. Hobart Press.
The idea of mean-difference plots was popularised in medical statistics circles by the highly cited paper:
Bland, J. M., and Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307-310.
In medical statistics such plots are often called Bland-Altman plots.
In the context of two-colour microarray Section 1.2 of the following paper gives examples and a detailed discussion of MA-plots:
Xiangqin Cui, M. Kathleen Kerr, and Gary A. Churchill (2003). Transformations for cDNA Microarray Data. Statistical Applications in Genetics and Molecular Biology, 2, Article 4. http://www.bepress.com/sagmb/vol2/iss1/art4
All of the following case studies make use of MA-plots:
Díaz, E., Ge, Y., Yang, Y. H., Loh, K. C., Serafini, T. A., Okazaki, Y, Hayashizaki, Y, Speed, T. P., Ngai, J., Scheiffele, P. (2002). Molecular analysis of gene expression in the developing pontocerebellar projection system. Neuron 36, 417-434. (Full Text) 10/2002
Díaz, E., Yang, Y. H., Ferreira, T., Loh, K. C., Okazaki, Y., Hayashizaki, Y., Tessier-Lavigne, M., Speed, T. P., and Ngai, J. (2003). Analysis of gene expression in the developing mouse retina. PNAS 100, 5491-5496. 29/4/2003 www.pnas.org/cgi/doi/10.1073/pnas.0831080100
Lin, D. M., Yang, Y. H., Scolnick, J. A., Brunet, L. J., Marsh, H., Peng, V., Okazaki, Y., Hayashizaki, Y., Speed, T. P., and Ngai, J. (2004). Spatial patterns of gene expression in the olfactory bulb. PNAS published August 10, 2004, www.pnas.org/cgi/doi/10.1073/pnas.0404872101