Dear Gordon,

Thanks a lot for your input. I tried the automatic prior.df estimation of
the estimateDisp() function. and its suggests a much lower prior.df then I
put mannually (9 instead of 25) But when I look at the gof plot, it's way
off. I thought that a good guide for a prior.df estimation is looking for a
value that puts the calculated deviances as close as possible to the
theoretical espected values. This is the prior.df for which your deviances
are straight on the  diagonal line of gof / qq plot) Or am I wrong here?

Best Regards
Adriaan


2014-02-02 Gordon K Smyth <smyth@wehi.edu.au>:

> Date: Fri, 31 Jan 2014 11:59:13 +0000
>> From: Adriaan Sticker <adriaan.sticker@gmail.com>
>> To: Ryan <rct@thompsonclan.org>
>> Cc: bioconductor@r-project.org
>> Subject: Re: [BioC] EdgeR: artifacts on BCV plot
>>
>> Hi
>> Thanks for your input. I checked manually the counts of the lowest BCV
>> values (see below) And I see nothing strange. Except the fact that the
>> counts are all at the low side. So I think I will keep them in.
>> Is it correct to think that the reason they appear on 1 horizontal line is
>> because of the discreteness of the counts?
>>
>
> No it is not because of discreteness.  It is because zero is
> mathematically a perfectly possible value for the BCV.
>
> These genes appear to show variability that is equal or less than Poisson
> variability, even after pulling them up towards the dispersion trend.  In
> other words, these genes are not showing any evidence of differences
> between biological replicates.
>
> Gordon
>
>  Greetings
>> Adriaan
>>
>
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