1 Load Libraries

library(TargetDecoy)
library(RCurl)
library(mzID)

2 Download data in working directory

download.file( 
  url = "https://raw.githubusercontent.com/statOmics/PDA22GTPB/data/identification/pyrococcusMSGF%2B.mzid",
  destfile = "pyrococcusMSGF+.mzid"
  )

3 Load Data in R

path2File <- "pyrococcusMSGF+.mzid"
msgf <- mzID(path2File)
## reading pyrococcusMSGF+.mzid...
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE

## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
##  DONE!

4 Launch the Shiny Gadget

Explore the results for search eninge scores to find correct names of search engine scores in the mzID.

evalTargetDecoys(msgf)

5 Evaluate target decoy assumptions

evalTargetDecoys(msgf,"isdecoy","ms-gf:evalue")

  • The plots show that the distribution of the MSGF+ PSM scores are nicely bimodal.

  • The separation between good target PSM scores and bad target PSM scores is less pronounced than for peptide shaker. So it is beneficial to include the other engines with peptideshaker.

  • We do not see deviations from the target decoy assumptions.

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