Hi David,
Thank you for your quick reply and suggestions. The decoy ratio is set to
0.25 as I use two sets of decoys, one for modeling and the other for
validation. Each decoy set corresponds to 25% of entries in the database.
Kind regards,
Rene
Op woensdag 18 december 2013 20:13:27 UTC+1 schreef David Shteynberg:
>
> Hello Rene
>
> Thanks for using the tools and double checking your work.
>
> In my tests I have found that applying the NSP model at the iProphet step
> greatly improves performance on peptide level. And applying the NSP model
> at the ProteinProphet step improves performance on the protein level. The
> two models are somewhat different since the ProteinProphet model considers
> grouping information while the iProphet model doesnt. I have not found the
> two to interfere.
>
> A safe and conservative approach so would look at the conservative
> estimate e.g. ProteinProphet probability cutoff to give me 1% error with
> decoys or 1% error with the model which ever is more conservative.
>
> When the model tends to underestimate error on protein or peptide level
> this is usually stemming from underestimation at the spectrum level by
> PeptideProphet and can be controlled by the CLEVEL={value} option for
> PeptideProphetParser -c{value} for xinteract. Setting this to a number
> greater than zero like .5 or 1 or 2 will serve to make the model more
> conservative overall, a negative value will have opposite effect which will
> carry through to the peptide and protein levels.
>
> Also I am curious why you set decoy rate to 0.25?
>
> Best,
> David
> On Dec 18, 2013 7:29 AM, "Rene B" <[email protected] <javascript:>> wrote:
>
>> Hi all,
>>
>> I am running PeptideProphet, iProphet and ProteinProphet (TPP 4.6.3) on Q
>> Exactive data searched with Comet, Myrimatch and OMSSA. I wondered if the
>> NSP model should be disabled in ProteinProphet when it is enabled in
>> iProphet? I got confused because it seems Petunia enables the NSP model
>> both in iprophet and proteinprophet by default (ie. when xinteract runs
>> with the -ip option).
>>
>> Another question is that when I compare decoy estimated protein FDRs to
>> ProteinProphet modelled FDRs, ProteinProphet seems a bit optimistic (decoy
>> based FDR of 0.1% corresponds to ~0.02% model FDR). This is with NSP
>> enabled in iProphet and disabled in ProteinProphet. How should I deal with
>> discrepancy, ie. should I take the decoy or probability based FDR to select
>> a probability cutoff?
>>
>> I have attached some examples for a search with myrimatch only. These are
>> the commands I used to generate the graphs:
>>
>> xinteract -Nmyrimatch.pep.xml -OAP -p0 -a%ExperimentFolder% -dDECOY0
>> -E%ExperimentTag% *.pep.xml
>> InterProphetParser myrimatch.pep.xml myrimatch.ipro.pep.xml
>> ProphetModels.pl -i myrimatch.ipro.pep.xml -k -r 0.25 -d "DECOY1"
>> ProteinProphet myrimatch.ipro.pep.xml myrimatch.prot.xml IPROPHET NONSP
>> ProtProphModels.pl -k -r 0.25 -d DECOY1 -i myrimatch.prot.xml
>>
>> The graphs are:
>>
>> myrimatch_all.ipro.pep_FDR_10pc: PeptideProphet/iProphet decoy vs model
>> FDR, all models enabled
>> myrimatch_nonsp.ipro.pep_FDR_10pc: PeptideProphet/iProphet decoy vs model
>> FDR, NSP model disabled in iProphet
>> myrimatch_nonsp.prot_FDR_5pc: ProteinProphet decoy vs model FDR, NSP
>> model disabled in ProteinProphet
>> myrimatch_all.prot_FDR_5pc: ProteinProphet decoy vs model FDR, NSP model
>> enabled in iProphet and ProteinProphet
>>
>> Thanks in advance!
>>
>> Kind regards,
>>
>> Rene
>>
>>
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