Dear Eric and David
By accepting lower thresholds I actually mean accepting higher FDRs but
that is not so useful here because there is not so many hits after the very
high probabilities and I will not be gaining much by using lower
thresholds. PeptideProphet gives me a lot of PSMs with very high confidence
and very low FDR. But as I allow higher FDRs like 2-3% it falls behind PEP
from MaxQuant.
Here is another plot to better show my point. I have compared the combined
results above with Max Quant's results as I increase the FDR. As it can be
seen, number of unique peptides is more with PepPro/iPro for low FDR, but
in the end it falls behind PEP.
<https://lh3.googleusercontent.com/-VZgUOiAvvno/WG1ggpOR3uI/AAAAAAAABBA/nyrjsBbLrGQ60eo3iqB3beMOVs4ZCXCAACLcB/s1600/Picture1.png>
And this can be understood from the following plot:
<https://lh3.googleusercontent.com/-jmsbfmBfbXA/WG1hNLP8e1I/AAAAAAAABBI/SBnRc5-naTw09vjKk0K4m9xcCBgS-1Z8QCLcB/s1600/Picture2.png>
Do you have any thoughts on this?
Thank you very much,
Ali
On Wednesday, January 4, 2017 at 3:08:14 PM UTC-5, Ali wrote:
>
> Dear David and Eric
>
> Thanks for your replies and suggestions. I thought that I might be able to
> increase identification by accepting hits with lower thresholds. I do
> understand what you mentioned here.
>
> Thanks again,
> Ali
>
> On Tuesday, January 3, 2017 at 1:57:38 PM UTC-5, Eric Deutsch wrote:
>>
>> In the ideal case where the PeptideProphet and iProphet had perfect
>> discriminating power, i.e. the correct and incorrect PSM distributions do
>> not overlap, you would expect a large peak at P=0 and P=1 and nothing in
>> between. As a dataset diverges from this ideal and the correct and
>> incorrect distributions begin to overlap, you will still have very large
>> peaks near P=0 and near P=1, with a small number of intermediate values
>> where the probability is between 0 and 1.
>>
>>
>>
>> So, in short, your distribution is exactly what you would expect based on
>> what you did. It is indicative of a great dataset. You should follow
>> David’s suggestion, and then if you replot, you will see huge peaks at 0
>> and 1 with rather little in between.
>>
>>
>>
>> Regards,
>>
>> Eric
>>
>>
>>
>>
>>
>> *From:* [email protected] [mailto:[email protected]]
>> *On Behalf Of *David Shteynberg
>> *Sent:* Tuesday, January 03, 2017 10:36 AM
>> *To:* spctools-discuss
>> *Subject:* Re: [spctools-discuss] PeptideProphet/iProphet Probability
>> Distribution
>>
>>
>>
>> These results are showing you that iProphet has great discriminating
>> power even when you restrict the input to only non-low probability PSMs
>> (filtering with PeptideProphet probability). The best way to run iProphet
>> is to give it all the data from PeptideProphet (use a PeptideProphet
>> minimum probability of 0).
>>
>>
>>
>> -David
>>
>>
>>
>> On Mon, Jan 2, 2017 at 9:26 PM, Ali <[email protected]> wrote:
>>
>> Dear all
>>
>>
>>
>> I am plotting the distribution of the assigned PeptideProphet and
>> iProphet probabilities to my search results. I am seeing a rather strange
>> pattern shown below. Why am I seeing so many hits with very high confidence
>> (probability >0.98) but suddenly after that threshold the number of
>> assigned probabilities to the PSMs decrease abruptly? What could be the
>> reason for this pattern? One would expect to see all ranges of
>> probabilities for PSMs.
>>
>>
>>
>>
>> <https://lh3.googleusercontent.com/-BeikGpozjkw/WGsygFV83UI/AAAAAAAABAg/DcG6lRxucM4IRclvdadal-eFSaA05sd6QCLcB/s1600/Picture1.png>
>>
>>
>>
>>
>>
>> This figure is the combination of different search engines:
>>
>> - Comet (min PepPro=0.5)
>>
>> - X! Tandem GPM (min PepPro=0.05)
>>
>> - X! Tandem TPP (min PepPro=0.05)
>>
>>
>>
>> The horizontal axis is between 0.5 and 1.
>>
>>
>>
>> and I can see the same pattern for the PeptideProphet distribution of
>> each one of these search engine results including SpectraST (Except for X!
>> Tandem GPM).
>>
>>
>>
>> Thank you very much,
>>
>>
>>
>> Ali
>>
>>
>>
>>
>>
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