https://github.com/bioconductor-source/swath2stats

https://github.com/bioconductor-source/swath2stats

Repository

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/"
)
```

# SWATH2stats 

This package is intended to transform extracted SWATH/DIA data from the OpenSWATH or other  (e.g. Spectronaut) software into a format directly-usable by statistics packages (e.g. mapDIA, PECA, MSstats) while performing filtering, annotation and FDR assessment if necessary.

## Analyzing SWATH/DIA data

How to extract SWATH/DIA data before using SWATH2stats with OpenSWATH can be found here: 


## Usage

SWATH2stats is a Bioconductor package. Go to  to see all information related to installation. Importantly, there exist both a release and development version of every Bioconductor R package. 


## Contribution

Please feel free to comment and post issues or pull requests on github (direct link in upper right corner). 


## Publication

The publication describing this package you can find here: 



## References

+ Blattmann P, Heusel M, Aebersold R. SWATH2stats: An R/Bioconductor Package to Process and Convert
  Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools. PLoS ONE 11(4): e0153160 (2016). doi: 10.1371/journal.pone.0153160.
  
+ Rost HL, Rosenberger G, Navarro P, Gillet L, Miladinovic SM, Schubert OT, Wolski W, Collins BC, Malmstrom J, Malmstrom L, Aebersold R. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnology. 2014 Mar;32(3):219-23. doi: 10.1038/nbt.2841.
  
+ Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, Vitek O. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments.Bioinformatics. 2014 Sep 1;30(17):2524-6. doi: 10.1093/bioinformatics/btu305.
  
+ Rosenberger G, Ludwig C, Rost HL, Aebersold R, Malmstrom L. aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics. 2014 Sep 1;30(17):2511-3. doi: 10.1093/bioinformatics/btu200.
  
+ Suomi T., Corthals G., Nevalainen O.S., and Elo L.L. (2015). Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins. J Proteome Res. Nov 6;14(11):4564-70. doi: 10.1021/acs.jproteome.5b00363.
  
+ Suomi, T. and Elo L.L. (2017). Enhanced differential expression statistics for data-independent acquisition proteomics" Scientific Reports 7, Article number:
5869.doi:10.1038/s41598-017-05949-y

Owner

GitHub Events