[BioC] edgeR and FDR values always equals 1

[A Gossner]

Hi Simon,

Have plotted two samples as you suggested [ plot 
(d$counts[,2],d$counts[,3],log="xy")] and saved the file on the FTP server.
ftp.ed.ac.uk
Login using the username 'anonymous' and use your email address as password.
get /edupload/logplot.eps as well as get /edupload/plot2.eps and get 
/edupload/plot2.eps
Which are plot asinh(count) graphs you suggested but must confess not 
sure if plotted correctly, plot(d$counts[,2],asinh(d$counts[,2]))
plot(d$counts[,3],asinh(d$counts[,3]))
but they are plots 1 and 2 respectively.

There does seem to be some correlation on the log-log plot.

Thanks

Anton



On 19:59, Simon Anders wrote:
> Hi
>
> On 11/11/2010 11:44 AM, A Gossner wrote:
>> While using edgeR to analysis my Tag-seq data, no matter which way I
>> analyse the data common or tagwise dispersion the FDR value is always 1.
>> Typical output is shown below;
> [...]
>>> d$common.dispersion
>> [1] 4.884378
>
> A common dispersion value of 4.8 means that your expresiion typically 
> varies between replicates by 220% (that's sqrt(4.8.)). In other words, 
> there is only noise and no signal in your data -- or you are doing 
> something fundamentally wrong.
>
> Look at some scatter plots, plotting the count values of one sample 
> against the count values of a replicate sample on a log-log scale (or 
> better, plot asinh(count) ). Do they seem to correlate?
>
>   Simon
>
>

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