[BioC] DESeq: computing 95% CI for negative binomial distribution ?

[Yvan Wenger]

Hi Mike,

In fact have timecourse data (I attach an example here and hope it goes
through). However, the confidence intervals shown in the figure are
computed assuming a normal distribution.

I am speaking only about normalized data, no ratio or other comparisons
between two conditions.

For the measurement I have replicates, I would like to plot them as the
mean ± 95% confidence intervals. As I understand, but I might be wrong, the
upper and lower confidence intervals shouldn't be symmetric as the data
follow a negative binomial distribution (and if I am wrong here, my
question makes no sense). Basically, I was wondering if it was possible to
use the dispersion estimates calculated by DESeq to calculate the lower and
upper confidence intervals and raw (normalized) read counts.

I hope what I intend to do is now more clear.

Yvan



On Wed, Apr 10, 2013 at 6:04 PM, Michael Love
<michaelisaiahlove at gmail.com>wrote:

>  hi Yvan,
>
>  To be a little more specific, are you looking for confidence intervals
> for the log fold changes?  or for the dispersion estimates?
>
>  Mike
>
>
> On Wed, Apr 10, 2013 at 5:04 PM, Yvan Wenger <yvan.wenger at unige.ch> wrote:
>
>> Hello everybody,
>>
>> It might be a very naive question but:
>>
>> As I can see, the estimateDispersions function can be used to used to
>> retrieve the dispersions used by DESeq. From that data, is there a way to
>> calculate upper and lower 95% confidence intervals?
>>
>> Best,
>>
>> Yvan
>>
>>         [[alternative HTML version deleted]]
>>
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>
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