mzIDMsgfSwissprotExample.Rmd

---
title: "Example: MSGF+ search on Swiss-Prot"
author: 
  - name: Lieven Clement
    affiliation:
    - Ghent University
output: 
    html_document:
      code_download: true
      theme: flatly
      toc: true
      toc_float: true
      highlight: tango
      number_sections: true
linkcolor: blue
urlcolor: blue
citecolor: blue
---

# Load Libraries 

```{r}
library(TargetDecoy)
library(RCurl)
library(mzID)
```

# Download data in working directory

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

# Load Data in R

```{r}
path2File <- "pyrococcusMSGF+.mzid"
msgf <- mzID(path2File)
```

# Launch the Shiny Gadget 

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

```{r eval=FALSE}
evalTargetDecoys(msgf)
```

# Evaluate target decoy assumptions 


```{r}
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.