Question: plotMA in deseq2
0
gravatar for gv
3 months ago by
gv40
United States
gv40 wrote:

Hi,

In DESeq2, while making  MAPlot for an object obtained by using results function, is there a way to highlight only those genes, whose log2FC values are >= |1|. For example:

ddsMat <- DESeq(ddsMat)

(res.05_FC0<-results(ddsMat,alpha=0.05,lfcThreshold = 0))  

plotMA(res.05_FC0,main="DESeq2_WTGFP_vs_KOHET",ylim=c(-10,10),alpha=0.05)

Now this will show log2FC values which are like 0.80, 0.75, -0.73 also as red. I want to highlight only those values as red which have log2FC values like 1.3,1.2,-1.34 etc i.e >|1|.

Can I do it in the in built function?

Also the default alpha for plotMA is 0.05??

Am i Correct on this ? log2FC of 1.34 from the above output means there is 2^1.34 difference between the groups?

 

Thanks

 

deseq2 • 121 views
ADD COMMENTlink modified 3 months ago by Michael Love21k • written 3 months ago by gv40
Answer: plotMA in deseq2
0
gravatar for Michael Love
3 months ago by
Michael Love21k
United States
Michael Love21k wrote:

There’s no built in way to do this, but the function is pretty simple. 

For default values check the help page

https://www.rdocumentation.org/packages/DESeq2/versions/1.12.3/topics/plotMA

ADD COMMENTlink written 3 months ago by Michael Love21k

Hi Michael

Thanks for the reply. It seems like the default for plotMA is 0.1 from this line

plotMA"(object, alpha = 0.1, main = "", xlab = "mean of normalized counts", ylim, MLE = FALSE, ...)

Also I am trying to play with lfcThreshold parameter of the results function(will ask a separate question on it ). In the output file under log2FoldChange column, if the value is 2, does that mean the gene difference between 2 groups being compared is 4 times? Thanks for all your help.

ADD REPLYlink modified 3 months ago by Michael Love21k • written 3 months ago by gv40

Yes, a log2 fold change of 2 means the expression goes up by 4 fold. We explain how to interpret the results in this section of the workflow (the workflow is like an expanded version of the vignette, going at a slower pace):

https://bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#differential-expression-analysis

ADD REPLYlink written 3 months ago by Michael Love21k
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