Course: R/Bioconductor for

Mass Spectrometry and Proteomics

Course website: https://www.physalia-courses.org/courses-workshops/course58/

Where: Free University (FU) Berlin

When: 17-19 February 2020

Instructor: Dr. Laurent Gatto

Overview

This course will introduce participants to the analysis and exploration of mass spectrometry (MS) based proteomics data using R and Bioconductor. The course will cover all levels of MS data, from raw data to identification and quantitation data, up to the statistical interpretation of a typical shotgun MS experiment and will focus on hands-on tutorials. At the end of this course, the participants will be able to manipulate MS data in R and use existing packages for their exploratory and statistical proteomics data analysis.

Target audience and assumed background

The course is targeted to either proteomics practitioners or data analysts/bioinformaticians that would like to learn how to use R and Bioconductor to analyse proteomics data. Familiarity with MS or proteomics in general is desirable, but not essential as we will walk through and describe a typical MS data as part of learning about the tools. 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). Familiarity with other Bioconductor omics data classes and the tidyverse syntax is useful, but not required.

Program

Monday - Classes from 9:30 to 17:30

During the first day, we will focus on raw MS data, including how mass spectrometry works, how raw MS data looks like, MS data formats, and how to extract, manipulate and visualise raw data.

Tuesday- Classes from 9:30 to 17:30

The second day will focus in identification data, how to combine them with raw data, quantitation of MS data, and introduce data structure of quantitative proteomics data

Wednesday- Classes from 9:30 to 17:30

The last day will focus on quantitative proteomics, including data structures, data processing, visualisation statistical analysis to identify differentially expression proteins between two groups.