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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# TargetDecoy
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/statOmics/TargetDecoy/actions)
[](https://codecov.io/gh/statOmics/TargetDecoy?branch=master)
[](https://bioconductor.org/checkResults/release/bioc-LATEST/TargetDecoy)
[](https://bioconductor.org/checkResults/devel/bioc-LATEST/TargetDecoy)
[](http://bioconductor.org/packages/stats/bioc/TargetDecoy/)
[](https://support.bioconductor.org/tag/TargetDecoy)
[](https://bioconductor.org/packages/release/bioc/html/TargetDecoy.html#since)
[](http://bioconductor.org/checkResults/devel/bioc-LATEST/TargetDecoy/)
[](https://bioconductor.org/packages/release/bioc/html/TargetDecoy.html#since)
<!-- badges: end -->
The goal of __TargetDecoy__ is to to generate diagnostic plots to evaluate the
quality of the target decoy approach (TDA).
A first step in the data analysis of Mass Spectrometry (MS) based proteomics
data is to identify peptides and proteins. With this respect the huge number of
experimental mass spectra typically have to be assigned to theoretical peptides
derived from a sequence database. Search engines are used for this purpose.
These tools compare each of the observed spectra to all candidate theoretical
spectra derived from the sequence data base and calculate a score for each
comparison. The observed spectrum is then assigned to the theoretical peptide
with the best score, which is also referred to as the peptide to spectrum match
(PSM). It is of course crucial for the downstream analysis to evaluate the
quality of these matches. Therefore False Discovery Rate (FDR) control is used
to return a reliable list PSMs. The FDR, however, requires a good
characterisation of the score distribution of PSMs that are matched to the wrong
peptide (bad target hits). In proteomics, the target decoy approach (TDA) is
typically used for this purpose. The TDA method matches the spectra to a
database of real (targets) and nonsense peptides (decoys). A popular approach to
generate these decoys is to reverse the target database. Hence, all the PSMs
that match to a decoy are known to be bad hits and the distribution of their
scores are used to estimate the distribution of the bad scoring target PSMs. A
crucial assumption of the TDA is that the decoy PSM hits have similar properties
as bad target hits so that the decoy PSM scores are a good simulation of the
target PSM scores. Users, however, typically do not evaluate these assumptions.
To this end we developed TargetDecoy to generate diagnostic plots to evaluate
the quality of the target decoy method.
## Installation
You can install `r BiocStyle::Biocpkg("TargetDecoy")` from
[*Bioconductor*](http://bioconductor.org/) using the following code:
```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("TargetDecoy")
```
The latest development version of __TargetDecoy__ can also be installed from
[GitHub](https://github.com/statOmics/TargetDecoy) with:
```r
BiocManager::install("statOmics/TargetDecoy")
```
## Getting started
Check the [vignette](https://bioconductor.org/packages/release/bioc/vignettes/TargetDecoy/inst/doc/TargetDecoy.html).