Question: The FDR and P value in RNA-seq analysis
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gravatar for mikysyc2016
2.5 years ago by
mikysyc201680
mikysyc201680 wrote:

Hi all, After using edgeR to do RNA-seq analysis, I get p valve and FDR. Which one you will use to select your interest target? I search website more people will recommend FDR value as the cutoff standard. But when read papers, a lot of paper use p value( like -log10P) as y axis to make volcano plot. So what is your suggestion? Thanks!

rna-seq p value and fdr • 4.9k views
ADD COMMENTlink modified 2.5 years ago by Dattatray Mongad350 • written 2.5 years ago by mikysyc201680
1

Volcano plot is the representation of effect (fold change) versus significance (-lgo10p-value). A very simple volcano could be like this but if you want to include FDR you can do it just like this.

ADD REPLYlink written 2.5 years ago by Nitin Narwade450

@OP: Please read more about p and FDR value calculation. FDR values are are adjusted p-values. People rely on adjusted p-values than p-values. However, there is another component in analysis that you can use for filtering: fold change (log fold change). However, both filters are arbitrary and please choose the cut off with the help of some one who knows /did the analysis earlier and validated the results.

ADD REPLYlink written 2.5 years ago by cpad011214k
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gravatar for Dattatray Mongad
2.5 years ago by
National Centre for Cell Science, Pune
Dattatray Mongad350 wrote:

Mostly LogFC and -log10(pvalue) are used to draw volcano plots which are nothing but scatterplots. By these plots you can make illustrations that - how much of your transcripts/genes are significantly upregulated or downregulated. Here, p-values serve the purpose of checking significance.


p-value is a measure of how likely you are to get this data if no real difference existed. So, small p-value means your results are not by chance. But q-values are adjusted p-values by FDR approach, which show you even if your data is statistically significant, what would be False Discovery Rate. So, lower q-value is significant. Usually, if you sort p-value that order is maintained in q-value also.


So, if you want you can plot all transcripts (Log2FC on x-axis and -log10(p-value) on y-axis).

  1. p-value<0.05 => all significant
  2. log2FC>abs(1.5) => all differentially expressed
  3. padj<0.05 and log2FC>abs(1.5) => significant and differentially expressed

his would help reviewer. But use q-value as when you want to build some hypothesis on basis of them.

ADD COMMENTlink written 2.5 years ago by Dattatray Mongad350
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