[BioC] (adj.p.value & log2FC) or (B.value & log2FC)

[Gordon K Smyth]

Dear Deb,

Well, this is not really a simple question at all.  The way that you 
prioritise your discoveries is not just a matter of statistics, but also 
depends on the context and aims of your study.  That is why limma offers 
different options.

If you want to know what the limma developers do, have a look at the case 
studies in the limma User's Guide.  We do not actually recommend either of 
the options that you mention.

The most common analysis would be to simply choose genes by FDR.  But 
please don't ask me what cutoff you should use for FDR.  It is quite 
common to use 0.05 or 0.1, but there is no correct value and this is for 
you to decide on the basis of the science of your own study.

If you want to give even more priority to larger fold changes, then we 
recommend that you use treat().  This is better than just cutting on 
estimated logFC value.

There is no B.value cutoff.  The B-statistic is for ranking, not for 
absolute cutoff.  I do not know where you might have got the cutoff value 
"4" from.  I have not seen anyone suggest this.  To use the B-statistic 
for absolute cutoff would require estimating the overall proportion of DE 
genes, which limma can do by propTrueNull but doesn't do by default.

Best wishes
Gordon

> Date: Mon,  2 Dec 2013 10:15:53 -0800 (PST)
> From: "deb [guest]" <guest at bioconductor.org>
> To: bioconductor at r-project.org, devbt15 at gmail.com
> Subject: [BioC] (adj.p.value & log2FC) or (B.value & log2FC)
>
>
> Hi Sir,
> I have a simple question regarding cut-off parameter to be used for filtering out DEGs from the topT object obtained using LIMMA.
> Which statistics is preferred more and why?
> 1) filter of adj.p.value and log2FC.
> 2) filter of B.value and log2FC.
> I mean both give similar ranking of genes but usually I have  seen people using an adj.p.value cut-off of 0.01. What is the minimum cut-off value for B.value?(Is it 4?)
> It is a technical question so I do not have any output to be put in the next sessionInfo() field.
> Thank you for your input Sir.
> Regards.
> Deb.
>
>
>
> -- output of sessionInfo():
>
> topT<- topTable(fit3, coef=1, adjust="BH", sort.by="B",number=nrow(data))

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