biology

MA plot

MA plot is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand MA plot rather than just read about it. In short: Within computational biology, 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 (log ratio) and A (mean average) scales, then plotting these values.

Key takeaways

MA plot belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect MA plot to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of MA plot from memory before moving on to harder problems.

Reference excerpt

Within computational biology, 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 (log ratio) and A (mean average) 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 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. 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. The MA-plot is a plot of the distribution of the red/green intensity ratio ('M'), that is plotted against the average intensity ('A'). M and A are defined by the following equations.

M = log 2 ⁡ ( R / G ) = log 2 ⁡ ( R ) − log 2 ⁡ ( G ) {\displaystyle M=\log _{2}(R/G)=\log _{2}(R)-\log _{2}(G)}

A = 1 2 log 2 ⁡ ( R G ) = 1 2 ( log 2 ⁡ ( R ) + log 2 ⁡ ( G ) ) {\displaystyle A={\frac {1}{2}}\log _{2}(RG)={\frac {1}{2}}(\log _{2}(R)+\log _{2}(G))}

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 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 (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.)

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

Illustrations

Worked examples

Example 1 — a first encounter with MA plot

Start with the simplest possible case. Write down what MA plot claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to MA plot before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about MA plot ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of MA plot

In research: MA plot appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses MA plot in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: MA plot is common in secondary-school and first-year university syllabi. It links to neighbouring topics Gene expression, Microarrays, Plots (graphics), so understanding it makes those chapters shorter.
In everyday life: Look for MA plot outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study MA plot in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what MA plot means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain MA plot out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is MA plot in simple terms?

Why does MA plot matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study MA plot?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on MA plot.