Proline qR

Overview

Proline-qR is a set of R scripts and Rmarkdown reports for generating reports of controlled quantitation datasets from Proline. Some data are included but you can provide your own data (exported as xlsx document from Proline) and tell Proline-qR to generate the appropriate report depending on the reference quantitation datasets.

The generated reports and figures are broadly inspired by the figures shown in Proline paper (Bouyssié,D. et al. (2020) Proline: an efficient and user-friendly software suite for large-scale proteomics. Bioinformatics, 36, 3148–3155).

The supported datasets:

• UPS: is a proteomic standard composed of an equimolarmixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate. It has been described in Ramus,C. et al. (2016) Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset. Journal of Proteomics, 132, 51–62 and in Bouyssié,D. et al. (2020) Proline: an efficient and user-friendly software suite for large-scale proteomics. Bioinformatics, 36, 3148–3155.

  • UPS_10Concentrations: 10 different concentrations

  • UPS_4Concentrations: a subset of 4 concentrations

• ABRF: is the dataset from the ABRF 2015 Study described in Choi,M. et al. (2017) ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC–MS/MS Experiments. J. Proteome Res., 16, 945–957.

• PME12: is a EuPA Standardization Initiative. Proteomics Multicentric Experiment 12 (PME12) was launched last April 2019 with the aim of comparing the performance of different label free quantification methods/pipelines. HeLa, Saccharomyces cerevisiae and Escherichia coli tryptic digests were purchased, resuspended in milli Q water with 0.1% FA and sonicated for optimal recovery. Peptide extracts were then mixed in different proportions as described by Navarro et al. (Nature Biotech 2016).

Installation

Proline-qR has been tested with R version 3.6.0. The following packages/version are required:

rmdformats_0.3.5 matrixcalc_1.0-3 reshape2_1.4.3   ggplot2_3.2.0    dplyr_1.0.0      openxlsx_4.1.0.1 rmarkdown_1.13   knitr_1.23

In the current version the code is delivered as an archive of the source project. To install Proline-qR uncompress the archive on your disk and open Proline-qR.Rproj with RStudio. Once opened load the required library and generate a report from a folder containing an xlsx export from Proline:

# source script files 
source('R/file-utils.R')
source('R/commons_functions.R')
source('R/generate-reports.R')

Usage

To use Proline-qR start by loading the required dependencies

# load required libraries

library(knitr)
library(rmarkdown)
library(dplyr)
library(ggplot2)
library(openxlsx)

The main functions are located in the file R/generate-reports.R. The following line generate in the outputfolder a report for the PME12 dataset data from the Proline xlsx that are in the supplied data folder. The search rule for relative forlder path is to search in the data/<dataset_name>/<relative folder path> folder.

generate_dataset_report("PME12", "20191216/spec_bestion_sum")

An absolute filepath can also be provided. In this case the report is generate into the same folder.

generate_dataset_report("PME12", "C:/Local/tmp/20191216/spec_bestion_sum")

Getting help

To get help send a email to the authors.