MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments
Clicks: 8
ID: 298660
2014
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
2.1
/100
8 views
1 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #152 of 829 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
MSstats is an R package for statistical relative quantification of proteins and peptides in mass spectrometry-based proteomics. Version 2.0 of MSstats supports label-free and label-based experimental workflows and data-dependent, targeted and data-independent spectral acquisition. It takes as input identified and quantified spectral peaks, and outputs a list of differentially abundant peptides or proteins, or summaries of peptide or protein relative abundance. MSstats relies on a flexible family of linear mixed models.The code, the documentation and example datasets are available open-source at www.msstats.org under the Artistic-2.0 license. The package can be downloaded from www.msstats.org or from Bioconductor www.bioconductor.org and used in an R command line workflow. The package can also be accessed as an external tool in Skyline (Broudy et al., 2014) and used via graphical user interface.
| Reference Key |
openalex_W2114165700
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean, Olga Vitek |
| Journal | BMC Bioinformatics |
| Year | 2014 |
| DOI |
10.1093/bioinformatics/btu305
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.