MA plot

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Within

high-throughput sequencing analysis.[1][2]

Explanation

Microarray data is often normalized within arrays to control for systematic biases in dye coupling and hybridization efficiencies, as well as other technical biases in the DNA probes and the print tip used to spot the array.[3] By minimizing these systematic variations, true biological differences can be found. To determine whether normalization is needed, one can plot Cy5 (R) intensities against Cy3 (G) intensities and see whether the slope of the line is around 1. An improved method, which is basically a scaled, 45 degree rotation of the R vs. G plot is an MA-plot.[4] The MA-plot is a plot of the distribution of the red/green intensity ratio ('M') plotted by the average intensity ('A'). M and A are defined by the following equations.

M is, therefore, the

distribution
of the data.

In many microarray gene expression experiments, an underlying assumption is that most of the genes would not see any change in their expression; therefore, the majority of the points on the y-axis (M) would be located at 0, since log(1) is 0. If this is not the case, then a

LOESS
should be applied to the data before statistical analysis. (On the diagram below see the red line running below the zero mark before normalization, it should be straight. Since it is not straight, the data should be normalized. After being normalized, the red line is straight on the zero line and shows as pink/black.)

Packages

Several Bioconductor packages, for the R software, provide the facility for creating MA plots. These include affy (ma.plot, mva.pairs), limma (plotMA), marray (maPlot), and edgeR(maPlot)

Similar "RA" plots can be generated using the raPlot function in the caroline CRAN R package.

An interactive MA plot to filter genes by M, A and p-values, search by names or with a lasso, and save selected genes, is available as an R-Shiny code Enhanced-MA-Plot.

Example in the R programming language

library(affy)

if (require(affydata)) 
{
     data(Dilution)
}

y <- (exprs(Dilution)[, c("20B", "10A")])

x11()

ma.plot( rowMeans(log2(y)), log2(y[, 1])-log2(y[, 2]), cex=1 )

title("Dilutions Dataset (array 20B v 10A)")

library(preprocessCore)

#do a quantile normalization
x <- normalize.quantiles(y)

x11()

ma.plot( rowMeans(log2(x)), log2(x[, 1])-log2(x[, 2]), cex=1 ) 
title("Post Norm: Dilutions Dataset (array 20B v 10A)")
MA Plot for raw data, note of the distortion in the upper ranges.
Pre Normalization
MA Plot for quantile normalized data.
Post Normalization
MA Plots.

See also

References

  1. PMID 19910308
    .
  2. PMID 25516281
    .
  3. ^ YH Yang, S Dudoit, P Luu, DM Lin, V Peng, J Ngai, TP Speed. (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research vol. 30 (4) pp. e15.
  4. ^ Dudoit, S, Yang, YH, Callow, MJ, Speed, TP. (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sin. 12:1 111–139
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