# LFQbench-Note

**Repository Path**: lums/LFQbench-Note

## Basic Information

- **Project Name**: LFQbench-Note
- **Description**: 阅读LFQBench源代码,加入中文注释
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-13
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[<img src="https://raw.githubusercontent.com/IFIproteomics/LFQbench/master/logo.png">](https://github.com/IFIproteomics/LFQbench)

[![Build Status](https://travis-ci.org/IFIproteomics/LFQbench.svg)](https://travis-ci.org/IFIproteomics/LFQbench) [![DOI](https://zenodo.org/badge/15862/IFIproteomics/LFQbench.svg)](https://zenodo.org/badge/latestdoi/15862/IFIproteomics/LFQbench)
======

### Description

LFQbench<sup>[1]</sup> is an open source [R package](https://github.com/IFIproteomics/LFQbench) for the automated evaluation of label-free quantification performance. The evaluation bases on the interpretation of the quantitative analysis results of hybrid proteome samples prepared in known ratios<sup>[2]</sup>.

LFQbench calculates and represents graphically a set of qualitative and quantitative performance metrics like identification rates,  precision and accuracy of quantification, providing developers and end-users with a standardized set of reports to enable an in-depth performance evaluation of their software and analysis platforms.

### Installation  

First, we need to install `devtools`:  

    install.packages("devtools")
    library(devtools)
   
Then we just call  

    install_github("IFIproteomics/LFQbench")
    library(LFQbench)

### Examples

You may find a complete example on how to use LFQbench at the vignette:

vignette("LFQbench")


### References


[1] Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Röst HL, Tate SA, Tsou C, Reiter L, Distler U, Rosenberger G, Perez-Riverol Y, Nesvizhskii AI, Aebersold R	& Tenzer S. A multicenter study benchmarks software tools for label-free proteome quantification. Nature Biotechnology 1546-1696 (2016). DOI: 10.1038/nbt.3685  

[2] Kuharev J, Navarro P, Distler U, Jahn O & Tenzer S. In-depth evaluation of software tools for data-independent acquisition based label-free quantification. Proteomics 15, 3140–3151 (2015).