EdgeR Vs DESeq2 question

[geneart]

Hello Folks,
I am analysing a dataset using edgeR and DESEq2 and trying to understand what I am seeing.
Here is what I have so far:
I do not have any technical replicates of my samples, unfortunately it is just how the experiment was designed. All I have is control and treated(2 types of treatment)condition per sample and thats it...I use all the controls (as replicates ,across 2 types of treatment) and all the treated as replicates(across both types of treatment) together.
So I plug the HTSeq counts for these in DESeq2 and EdgeR and try to see how things compare.
DESeq2: worked fine in the sense it convinces me enough to be a method of choice in prifiling differentially expressed genes and I have 24 of them and clustering indicated that there is no effect of treatment and biology of samples play a high role.
EdgeR: I get about 40 differentially expressed gene according to my results from Common dispersion estimate but the tagwise dispersion estimate does not work at all !
My prior.n is 3.85.so when I do :
cds<-estimateTagwiseDisp(cds,prior.n=3)
Error in estimateTagwiseDisp(cds,prior.n=3)
unused argument (prior.n=3)

I tried using the default prior.n as 10 and it still does not work.
So the mean variance plot indicated that the common dispersion (solid blueline) and the tagwise dispersion(blue circles) to be tightly knit and the raw varainces along the same line but a little more dispersed than the former two.

So my question is:
Why is then the tagwise dispersion not working ?
Can I use the MDS plot from edgeR and the clustering heatmap from DESeq2 on the same comparable page? they both cluster the datasets however am I comparing apples to oranges here?
Also EdgeR does this CPM and then also has the pvalues from the cmn.dispersion results , can I compare this to the pvalues I got from results from DESeq2?
Pvalues are pvalues, however I was owndering if they are indicating the same things or atleast close enough that I can compare my diff.expressed genes from these two results to say edgeR gave me a,b,c and DESeq2 gave me b,c E,F and so on?
If the p-values are comparable, at least the way they are generated.......is it because the cooks distance is accounted for that I have less number of DE genes in DESe2 than in edgeR?
I am not a statistical person, so please if any one can comment on these , I would appreciate it. Good to get different perspectives.
Thanks
geneart.

[Gordon Smyth]

The reason why estimateTagwiseDisp doesn't work is that you are inputing a value for an argument (prior.n) that doesn't exist. Why not simply use:

estimateTagwiseDisp(cds)

If you want to try changing the default values, it would be best to read the help page for the function, which would tell you what the arguments are.

Gordon

[geneart]

Thanks for the email Gordon,
I actually did that wihtout a prior.n value and it there was no error. However I am not sure if it did use the default value for prior.n. Infact it might not have used it at all ! Am I understanding this right? Because accroding to the document from EdgeR , default value for prior.n =10 and when I actually used it as it , I got errors. So if tagwise worked for me wiht only (CDS) instead of calling prior.n, then the mean variance plot that I plotted using CDS should be good. Am I correct? It does show the tagwise plotting as well. Just confirming.
Thanks
geneart.

[Gordon Smyth]

No, you are not understanding correctly. As told you in my last post, there is no argument called prior.n. There was at one time, but it hasn't existed for years. The error message from the software tells you the same thing.

You need to read the documentation that comes with the edgeR package. I don't know what "document" you refer to, but it does not seem to be the documentation that comes with the software.

You said in your original post that tagwise dispersion estimation in edgeR didn't work, but now it turns out that it actually worked fine.

[geneart]

EdgeR

Thanks Gordon, the tagwise dispersion actually worked.