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

Installation

1) Install Dependencies

  1. Install CRAN library dependencies (open R)

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

  2. Install BioConductor library dependencies (open R)

    R> source("http://bioconductor.org/biocLite.R") R> biocLite(c("limma","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) To run safeQuant.R (Post-process Progenesis LFQ datasets or Scaffold TMT datasets)

  1. locate file safeQuant.R (C:-adm.R ) This is the SafeQuant main script. Copy it to an appropriate directory, e.g. c:Files
  2. open terminal To display help options

    Rscript "c:Files.R" -h To run (with minimal arguments)

    Rscript "c:Files.R" -i "c:Files_measurement.csv" -o "c:Files"

Tips

  1. If using Progenesis QI we advice running SafeQuant on "Peptide Measurement" Exports.
  1. When working with Progenesis "Feature Exports" it is advisable to discard all features (rows) not annotated with a peptide, to speed up SafeQuant analysis. This can be done using the "filterLargeProgenesisPeptideFile.pl" perl script. (C:-adm.pl)
  1. install perl (or activePerl for windows http://www.activestate.com/activeperl)

  2. open terminal

    perl "C:Files.pl" "C:Files.csv" This will create a new versions of the feature file called with the extension "_FILTERED" features.csv -> features_FILTERED.csv

Basic functionality of the safeQuant.R script

  1. Data Normalization
  2. Ratio Calculation
  3. Statistical testing for differential abundances

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

https://raw.githubusercontent.com/eahrne/SafeQuant/master/inst/manuals/SafeQuant_UseCases.txt

.tsv export help

https://github.com/eahrne/SafeQuant/blob/master/inst/manuals/tsv_spreadsheet_help.pdf

Package Documentation

https://github.com/eahrne/SafeQuant/blob/master/inst/manuals/SafeQuant-man.pdf

Publications