A Toolbox for analysis of Proteomics Data
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The SafeQuant Package includes methods for analysis of quantitative (LFQ,TMT,HRM) Proteomics data.


1) Install Dependencies

A) Install CRAN library dependencies (open R)

R> install.packages(c("seqinr","gplots","corrplot","optparse","data.table","epiR","ggplot2","magrittr","dplyr","ggrepel"))

B) Install BioConductor library dependencies (open R)

R> source("http://bioconductor.org/biocLite.R")
R> biocLite(c("limma","Biobase","pcaMethods","impute","GO.db","UniProt.ws","affy"))

2) Install SafeQuant from sources

Option 1, install "master branch" using "devtools"

Make sure you have a working development environment.

Windows: Install Rtools.

Mac: Install Xcode from the Mac App Store.

Linux: Install a compiler and various development libraries (details vary across different flavors of Linux).

R> install.packages("devtools")
R> library(devtools)
R> install_github("eahrne/SafeQuant")

Option 2, install latest CRAN version

R> install.packages("SafeQuant")

3) Running safeQuant.R

A) locate file safeQuant.R (C:\Users\ahrnee-adm\Downloads\SafeQuant\exec\safeQuant.R ) This is the SafeQuant main script. Copy it to an appropriate directory, e.g. c:\Program Files\SafeQuant\

B) open terminal To display help options

> Rscript "c:\Program Files\SafeQuant\safeQuant.R" -h

To run (with minimal arguments)

> Rscript "c:\Program Files\SafeQuant\safeQuant.R" -i "c:\Program Files\SafeQuant\testData\peptide_measurement.csv" -o "c:\Program Files\SafeQuant\out"

Input file: "Peptide Measurement" .CSV file

  • File -> Export Peptide Measurements. This option is available once you have reached the "Resolve Conflicts" Step in Progenesis QI
  • When choosing properties to be included in the exported file check the "Grouped accessions (for this sequence)" check box.
Scaffold (TMT, experimental support)

Input file: "Raw Export" .XLS

Note that the experimental design needs to be specified (column numbers refer to listing order in .txt).

> Rscript "c:\Program Files\SafeQuant\safeQuant.R"  -i ../../SafeQuantTestData/TMT_10-Plex_Scaffold_Raw_Export_Example.xls --EX 1,2,3,4,5:6,7,8,9,10
MaxQuant (experimental support)

Input file: proteinGroups.txt

Note that the experimental design needs to be specified (column numbers refer to listing order in .txt).

> Rscript "c:\Program Files\SafeQuant\safeQuant.R"  -i ../../SafeQuantTestData/misc/maxQuant/proteinGroups.txt --EX 1,2,3:6,7,8 

Basic functionality of the safeQuant.R script

  1. Data Normalization
    • LFQ
      • Global data normalization by equalizing the total MS1 peak areas across all LC/MS runs.
    • Isobaric Labeling experiments (TMT or iTRAQ)
      • Global data normalization by equalizing the total reporter ion intensities across all reporter ion channels.
  2. Ratio Calculation
    • LFQ
      • Summation of MS1 peak areas per peptide/protein and LC-MS/MS run, followed by calculation of peptide/protein abundance ratios.
    • Isobaric Labeling experiments (TMT or iTRAQ)
      • Summation of reporter ion intensities per peptide/protein and LC-MS/MS run, followed by calculation of peptide/protein abundance ratios.
  3. Statistical testing for differential abundances
    • The summarized peptide/protein expression values are used for statistical testing of between condition differentially abundant peptides/proteins. Here, empirical Bayes moderated t-tests is applied, as implemented in the R/Bioconductor limma package (Smyth, 2004). The resulting per protein and condition comparison p-values are subsequently adjusted for multiple testing using the Benjamini-Hochberg method.

Smyth, G. K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 3 SP -, Article3. http://www.ncbi.nlm.nih.gov/pubmed/16646809

Use Case Manual


.tsv export help


Package Documentation



  • Ahrne, E. et al. Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments. J Proteome Res 15, 2537–2547 (2016). https://www.ncbi.nlm.nih.gov/pubmed/27345528
  • Ahrne, E., Molzahn, L., Glatter, T., & Schmidt, A. (2013). Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics. Journal of Proteome Research Just Accepted Manuscript https://www.ncbi.nlm.nih.gov/pubmed/23794183
  • Glatter, T., Ludwig, C., Ahrne, E., Aebersold, R., Heck, A. J. R., & Schmidt, A. (2012). Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion. https://www.ncbi.nlm.nih.gov/pubmed/23017020