Hi Adriaan,

Perhaps the best plan is to inspect these genes and their counts 
directly to see if there are any obvious commonalities about them. You 
can access the tagwise dispersions in the DGEList object using 
dge$tagwise.dispersions. It's not clear exactly how many of the genes 
show this artifact, but try finding the 10 or so genes with the lowest 
dispersions and look at their counts to see if there is anything amiss.

For what it's worth though, it doesn't look like these genes are having 
a noticeable effect on your dispersion trend, and I don't think you have 
much to worry about.

-Ryan

On 1/30/14, 10:23 AM, Adriaan Sticker wrote:
> Hi all,
>
> I made some BCV plots of my data after the tagewise estimation step. I
> notice sometimes that I gave genes with identical very low BCV values .It
> appears as a horizontal line below the rest of my data but it is always
> above zero. I put an example in attachement. They disapear when I higher
> the cutoff of my filter (cpm(counts)>1 to  cpm(counts)>2) but then I also
> lose a fraction of my genes.
>
> I wonder how I should interpret these values? What are they exactly. My
> guess would be that they are very low counts and due the discretness of
> count data, their bcv is zero?
> If I dont up my filter cutoff and thus leave them in the data, how harmfull
> are they? Can they influence much the estimation of BCV of the other data?
> (I use prior.df = 20) I can see the trended dispersion line moving a bit
> when I up my filter for the lower counts.
>
> In attachement the BCV plot with the artifacts (cpm(counts)>1) and a BCV
> plot without them (cpm(counts)>2)
>
> Best regards
> Adriaan Sticker
>
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor


	[[alternative HTML version deleted]]

