lgatto / 2021_08_31_rprotaz_online

Exploring, visualising and analysing mass spectrometry-based proteomics data in R

Exploring, visualising and analysing mass spectrometry-based proteomics data in R

General information

• Dates: 31/08/2021 – 9/09/2021

Course description:

This course covers how to access, manipulate, visualise and analyse mass spectrometry (MS) and quantitative proteomics data, using R/Bioconductor packages.. The course will be based on the following materials: https://rformassspectrometry.github.io/docs/

Audience:

Participants need to have a working knowledge of R (R syntax, commonly used functions, basic data structures such as data frames, vectors, matrices, … and their manipulation). The Data Carpentry courses, WSBIM1207 (https://uclouvain-cbio.github.io/WSBIM1207/) and/or WSBIM1322 (https://uclouvain-cbio.github.io/WSBIM1322/) course are suggested as a prerequisite to this course but not compulsory if you already have a working knowledge in R as mentioned above. Familiarity with other Bioconductor omics data classes and the tidyverse syntax is useful, but not required.

Aims:

During this course you will learn about:

• R/Bioconductor data structures for mass spectrometry data and proteomics data
• Accessing data from the public PRIDE repository
• Reading, manipulating and visualising raw data
• Reading, visualising and processing quantitative data
• Learn how the MS and proteomics R/Bioconductor infrastructure fits in the general Bioconductor ecosystem.

Learning Objectives:

After this course you should be able to:

• Prepare/convert proteomics data for it to be analysed in R.
• Import MS experiments and extract, process and visualise parts all or thereof, such as for example plot the raw spectra for a protein of interest.
• Generate quantitative data or import data from third party software such as, for example, MaxQuant or Proteome Discoverer.
• Process and visualise and analyse quantitative data in R such as, for example, filter or impute missing values, produce heatmaps or PCA plots, normalise your data and run a statistical test.

Timetable:

Day	Title	Duration (hrs)	Date	Time (UK)	file
1	Troubleshooting software installation (30 min)	0.5	Aug 31st	1-3:30 pm	00-install.R
1	Introduction: a typical MS experiment and file formats, getting data	2			01-ms.R
2	Raw data: introduction, data structures, data input/out	3	Sep 1st	1-4 pm	02-raw.R
3	Identification data: parsing search results, combining raw and id data and visualising identification data	2	Sep 2nd	1-3 pm	03-id.R
4	Quantitative proteomics: introduction, data structures, data input/output	3	Sep 7th	1-5 pm	04-quant.R
	BYOD (1 hr)	1
5	Quantitative proteomics: visualisation and analysis	3	Sep 9th	1-5 pm	05-analysis.R
	BYOD (1 hr)	1