MA plot 绘图计算公式

MA图主要应用在基因组数据可视化方面，实现数据分布情况的展示。早期主要应用于DNA芯片数据，现在常用于高通量测序数据中基因差异表达分析结果的展示。其计算公式如下： M一般做Y轴...

MA图主要应用在基因组数据可视化方面，实现数据分布情况的展示。早期主要应用于DNA芯片数据，现在常用于高通量测序数据中基因差异表达分析结果的展示。

其计算公式如下：

M一般做Y轴，A一般做X轴。

M常对应差异表达分析获得的差异对比组之间基因表达变化log2FC

A可以利用差异对比组的FPKM进行计算，以R和G来表示差异对比组的话，可以取R组基因的平均FPKM和G组基因的平均FPKM进行计算。也包括多个R包可以实现MA图绘制:

affy (ma.plot, mva.pairs), limma (plotMA), marray (maPlot), and edgeR(maPlot)

原文参考维基百科：MA plot (点击进入页面）

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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 binary logarithm of the intensity ratio (or difference between log intensities) 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 (microarrays typically show a bias here, with higher A resulting in higher |M|, i.e. the brighter the spot the more likely an observed difference between sample and control). The MA plot uses M as the y-axis and A as 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 (M) would be located at 0, since Log(1) is 0. If this is not the case, then a normalization method such as 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.)

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