deqms

DEqMS is a tool for quantitative proteomic analysis

https://github.com/yafeng/deqms

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: pubmed.ncbi, ncbi.nlm.nih.gov
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.1%) to scientific vocabulary

Keywords

limma quantitative-proteomic-analysis

Keywords from Contributors

transcriptomics bioconductor-package genomics bioinformatics gene proteomics fragmentation dims data-munging topdown
Last synced: 6 months ago · JSON representation

Repository

DEqMS is a tool for quantitative proteomic analysis

Basic Info
  • Host: GitHub
  • Owner: yafeng
  • Language: R
  • Default Branch: master
  • Size: 17 MB
Statistics
  • Stars: 23
  • Watchers: 4
  • Forks: 3
  • Open Issues: 14
  • Releases: 0
Topics
limma quantitative-proteomic-analysis
Created over 8 years ago · Last pushed 11 months ago
Metadata Files
Readme

README.md

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

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

Owner

  • Login: yafeng
  • Kind: user
  • Location: Boston
  • Company: Harvard medical school

GitHub Events

Total
  • Issues event: 4
  • Watch event: 2
  • Issue comment event: 13
  • Push event: 4
  • Pull request event: 3
  • Fork event: 1
  • Create event: 1
Last Year
  • Issues event: 4
  • Watch event: 2
  • Issue comment event: 13
  • Push event: 4
  • Pull request event: 3
  • Fork event: 1
  • Create event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 122
  • Total Committers: 9
  • Avg Commits per committer: 13.556
  • Development Distribution Score (DDS): 0.459
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Yafeng Zhu y****u@s****e 66
Yafeng Zhu y****u@y****e 23
yafeng y****u@o****m 17
Jorrit Boekel j****l@s****e 4
Nitesh Turaga n****a@g****m 4
Yafeng Zhu y****u@Y****l 4
vobencha v****a@g****m 2
Kayla-Morrell k****l@r****g 1
Yafeng Zhu y****u@Y****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • potockym (1)
  • lisiarend (1)
  • jesswhitts (1)
  • nomascus (1)
  • longchung90 (1)
  • yudada2020 (1)
  • Arthfael (1)
  • 14stutzmanav (1)
Pull Request Authors
  • yafeng (2)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

DESCRIPTION cran
  • R >= 3.5 depends
  • ggplot2 * depends
  • graphics * depends
  • limma >= 3.34 depends
  • stats * depends
  • BiocStyle * suggests
  • ExperimentHub * suggests
  • LSD * suggests
  • farms * suggests
  • ggrepel * suggests
  • knitr * suggests
  • matrixStats * suggests
  • plyr * suggests
  • reshape2 * suggests
  • rmarkdown * suggests
  • utils * suggests