MSstats-package: Tools for protein significance analysis in DDA,SRM and DIA proteomic experiments for label-free workflows or workflows with stable isotope labeled reference

Description

A set of tools for protein significance analysis in SRM, DDA and DIA experiments.

Arguments

Details

Package:

MSstats

Version:

3.3.12

Date:

2016-05-01

License:

Artistic-2.0

LazyLoad:

yes

The package includes four main sections: I. explanatory data analysis (data pre-processing and quality control of MS runs), II. model-based analysis (finding differentially abundant proteins), III. statistical design of future experiments (sample size calculations), and IV. protein quantification (estimation of protein abundance). Section I contains functions for (1) data pre-processing and quality control of MS runs (see dataProcess) and (2) visualizing for explanatory data analysis (see dataProcessPlots). Section II contains functions for (1) finding differentially abundant proteins (see groupComparison) and (2) visualizing for the testing results (see groupComparisonPlots). Section III contains functions for (1) calculating sample size (see designSampleSize) and (2) visualizing for the sample size calculations (see designSampleSizePlots). Section IV contains functions for (1) per-protein group quantification and patient quantification (see quantification)

Examples of data in MSstats are (1) example of required input data format from label-based SRM experiment SRMRawData; (2) example of required input data format from DDA experiment DDARawData; (3) example of required input data format from label-free SWATH experiment DIARawData.

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):2524-2526, 2014.

Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. "Protein significance analysis in selected reaction monitoring (SRM) measurements." Molecular & Cellular Proteomics, 11:M111.014662, 2012.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.