MA plot


An MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M and A scales, then plotting these values. Though originally applied in the context of two channel DNA microarray gene expression data, MA plots are also used to visualise high-throughput sequencing analysis.

Explanation

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. By minimizing these systematic variations, true biological differences can be found. To determine whether normalization is needed, one can plot Cy5 intensities against Cy3 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. The MA-plot is a plot of the distribution of the red/green intensity ratio plotted by the average intensity. M and A are defined by the following equations.
M is, therefore, the binary logarithm of the intensity ratio and A is the average log intensity for a dot in the plot. MA plots are then used to visualize intensity-dependent ratio of raw microarray data. The MA plot puts the variable M on the y-axis and A on the x-axis and gives a quick overview of 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 would be located at 0, since log is 0. If this is not the case, then a normalization method such as LOESS should be applied to the data before statistical analysis.

Packages

Several Bioconductor packages, for the R software, provide the facility for creating MA plots. These include affy, limma, marray, and edgeR
Similar "RA" plots can be generated using the raPlot function in the caroline R package.

Example in the R programming language


library
if
y <- , c
x11
ma.plot-log2
title
library
  1. do a [quantile normalization
x <- normalize.quantiles
x11
ma.plot-log2
title