R and Bioconductor for proteomics at the Sainsbury Laboratory

This repository will host the OpenPlant funded R/Bioconductor for proteomics material. The funding was awarded to Jan Sklenar (TSL, Norwich), Laurent Gatto (UCam), Marielle Vigouroux (JIC), Govind Chandra (JIC) to introduce and implement the utilisation of various R for proteomics tools in the Sainsbury Laboratory and more generally on the Norwich campus.

The project will run 6 sessions/days over 6 months with the aim to

• Identify needs and opportunities
• Train invdividuals
• Dedicated development and integration with existing tools

Session 1: Setting the stage (15 Jan 2018)

• Introductory talk: R and Bioconductor for proteomics

During this session, we discussed the concrete needs and opportunities that would be tackled as part of this project, and set a schedule for the sessions until June.

Session 2: Hands-on introduction to R and Bioconductor for proteomcs (2 Feb 2018)

Software requirements: R and RStudio

Download and install R and RStudio. In case you already have R installed, make you have R 3.4.3. To check the version:

> version
			   _
platform       x86_64-pc-linux-gnu
arch           x86_64
os             linux-gnu
system         x86_64, linux-gnu
status         Patched
major          3
minor          4.3
year           2017
month          12
day            12
svn rev        73903
language       R
version.string R version 3.4.3 Patched (2017-12-12 r73903)
nickname       Kite-Eating Tree

Once the software are installed, open RStudio and install Bioconcuctor packages:

source("http://www.bioconductor.org/biocLite.R")
biocLite(c("MSnbase", "msmsTests", "rpx", "pRoloc", "pRolocdata", "msdata"))

To test the installation, load MSnbase:

library("MSnbase")

Programme

• Introduction to R and RStudio and Bioconductor
• R documentation and vignettes
• Variables, vectors and dataframes
• Manipulating data
• Plotting
• Saving and loading data (binary and csv)
• Installing packages

The content of that session is available in the rfp1 Rproject/dir.

Session 3: R/Bioconductor tools for MS-based proteomics (21-03-2018)

Summary from last time:

• Using R and RStudio.
• Data structures: vectors, dataframes and MSnSets.
• Subsetting using [ and $.
• Data input: read.csv and readMSnSet2.
• Data output: save and load.

We didn't get time to see plotting and package installation.

Material for session 3 will focus on consolidating our understanding and usage of dedicated R/Bioconductor packages for MS and proteomics.

Points to discuss/consider

• For identification, use mzid files, that can be opened with mzR::openIDfile or MSnbase:::readMzIdData. To visualise data over time, annotate (with filename and date) and combine there.
• Raw data, with readMSData(, mode = "onDisk").
• Differential expression of count data with msmsTests.

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Final OpenPlant presentation

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## Title: R for Proteomics

Date: 11th March 2019

Abstract:

The final presentation of OpenPlant project aimed to facilitate proteomics data analysis using the existing infrastructure in R environment. The objective to organize training and explore R for Proteomics, an open source project available in Bioconductor packages was met in several workshops with the developer. The generated code was applied to current JIC/TSL projects. We set up basic proteomics data pipeline that is an independent alternative to the existing, mostly commercial, software. The results of this teamwork, experience working with the R packages and perspectives will be presented and discussed by team members.

Target audience:

Those who carry out proteomics experiments and seek a deeper understanding of data analysis involved and, those wishing to learn to use open source proteomics tools available in Bioconductor.

### The team: **Laurent Gatto**, RfP developer, Professor of Bioinformatics, Université catholique de Louvain, Belgium

Jan Sklenar, proteomics and mass spectrometry specialist, The Sainsbury Laboratory, Norwich

Marielle Vigouroux, Bioinformatician, John Innes Centre, Norwich

Govind Chandra, Bioinformatician, John Innes Centre, Norwich

### Key words: Proteomics data analysis, R, open source.

GitHub: github.com/Sjan1/r-for-proteomics-tsl