Using glMDPlot function how to show the DGE's in color ?

Question

[sunnykevin97]

HI

I plotted the DGE's using glMDPlot, showing same color for all genes. http://freeimage.host/i/2V7hiJ How do I show up/down DGE's in color. suggestions.

glMDPlot(fit2,counts = fit2$coefficients,status = dtt)

[Ram]

1. Please see How to add images to a Biostars post to add your images properly. You need the direct link to the image, not the link to the webpage that has the image embedded (which is what you have used here)
2. Please use the formatting bar (especially the code option) to present your post better. You can use backticks for inline code (`text` becomes text), or select a chunk of text and use the highlighted button to format it as a code block. I've done it for you this time.
   

[sunnykevin97]

Thanks for the suggestions.

Answer 1

[ATpoint]

First of all, please follow up on your previous questions, you asked quite many recently. Please upvote answers that were helpful and accept one or many that solved the issue. Some more effort in your questions would also be appreciated, e.g. what you tried.

For color-highlighted MA-plots I do not think this function you use natively supports it. So either write a a custom wrapper yourself, or use the function I provide here in this tutorial Basic normalization, batch correction and visualization of RNA-seq data which can be found at Gist or use other wrappers such as this one here based on EnhancedVolcano EnhancedVolcano becomes a pseudo-EnhancedMA.

For my function you need a vector of logCPMs (average expression) as Xval, a log fold change (logFC) as Yval and a set of p-values, e.g. e.g. the FDR returned by your analysis as Pval. You can then set Pval.threshold to a cutoff you like and it will color-highlight genes below this threshold, separated into being greated or below a logFC of zero. Other options are explained in the header of the function.

Using my function returns something like the first plot below, the EnhancedVolcano one is the second plot, the latter offering more options to customize results / highlight individual genes and most importantly much more documentation so you probably want to go with this one.

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