QC
MSstatsQC : Longitudinal system suitability monitoring and quality control for proteomic experiments
Statistical process control (SPC) is a general and well-established method of quality control (QC) which can be used to monitor and improve the quality of a process such as LC MS/MS. `MSstatsQC` and `MSstatsQCgui` are two open-source R packages for statistical analysis and monitoring of QC and system suitability testing (SST) samples produced by spectrometry-based proteomic experiments. Our framework termed `MSstatsQC` is available through www.msstats.org/msstatsqc. It uses SPC tools to track metrics including total peak area, retention time, full width at half maximum (FWHM) and peak asymmetry for proteomic experiments. We introduce simultaneous and time weighted control charts and change point analysis to monitor mean and variability of metrics. Proposed longitudinal monitoring approach significantly improves the ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems of mass spectrometric assays, thereby, reducing cost of control and failure.
Documentation
Vignette here.
Cheatsheet here.
Installation
From Bioconductor: MSstatsQC
MSstatsQC 2.0.0 (Bioconductor development version : Release 3.8)
Type the following in R console window
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MSstatsQC", version = "devel")
The development version of the package MSstatsQC is the most recent and is available here. The versioning of the main package is updated twice a year, to synchronise with the Bioconductor release.
From Bioconductor: MSstatsQCgui
MSstatsQC 1.2.0 (Bioconductor version : Release 3.7)
Type the following in R console window
## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("MSstatsQC")
Shiny Application
Datasets
Example datasets for DDA, SRM and DIA experiments are available through the following link.
Datasets
Maintainers
- Eralp Dogu, Mugla University
- Sara Taheri, Northeastern University
Citing MSstatsQC
- Dogu, E. et al.”Longitudinal system suitability monitoring and quality control for targeted proteomic experiments..” Molecular and Cellular Proteomics (2017), doi:10.1074/mcp.M116.064774
- Dogu, E. et al.”MSstatsQC 2.0: R/Bioconductor package for statistical quality control of mass spectrometry-based proteomic experiments.” J. Proteome Res. (2018), 10.1021/acs.jproteome.8b00732