https://github.com/yafeng/DEqMS

DEqMS is a tool for quantitative proteomic analysis
https://github.com/yafeng/DEqMS

limma quantitative-proteomic-analysis

Last synced: 3 months ago
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DEqMS is a tool for quantitative proteomic analysis

• Host: GitHub
• URL: https://github.com/yafeng/DEqMS
• Owner: yafeng
• Created: 2017-10-17T12:56:54.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-06-01T02:38:07.000Z (over 4 years ago)
• Last Synced: 2024-06-18T02:35:33.221Z (8 months ago)
• Topics: limma, quantitative-proteomic-analysis
• Language: R
• Size: 17 MB
• Stars: 20
• Watchers: 5
• Forks: 2
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-proteomics - DEqMS - R - Developed ontop of limma, but takes into account variability in PSMs. Works on both labelled and unlabelled samples - [paper](https://www.mcponline.org/article/S1535-9476(20)34997-5/fulltext) (6. Stastical Analysis / Table of Contents)

README

        
# DEqMS

DEqMS is a statistical tool for testing differential protein expression in quantitative proteomic analysis, developed by Yafeng Zhu @ Karolinska Institutet. 

DEqMS is a published method, if you use it in your research, please cite:

Zhu et al. DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis. 

Mol Cell Proteomics 2020 Mar 23. [PMID: 32205417](https://pubmed.ncbi.nlm.nih.gov/32205417/)

## Installation

To install lastest DEqMS package (v1.6.0), start R (version "4.0") and enter:

```{r}

if (!requireNamespace("BiocManager", quietly = TRUE))

    install.packages("BiocManager")

BiocManager::install("DEqMS")

```

## Introduction

DEqMS is developed on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore achieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.

## How to use it

Browse DEqMS online Vignettes [here](https://bioconductor.org/packages/release/bioc/vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html)