R workshops 2015

Workshop 1 – Introduction to R

Tired of relying on bioinformaticians? R is the perfect environment to easily explore your data and do all the analyses and checks you’ve always dreamed of! In this workshop, we will work through the basics of R and build the foundations of your upcoming bioinformatics experience. Hands on session for R beginners.

9.15am-5pm, Friday 8th May in room N4/17, Stewart Biology building. Email us at hgssmcgill@gmail.com to register before Wednesday 6th May.

Data and slides of the workshop.

Workshop 2 – Advanced R for bioinformatics

This workshop will be a medley of tips and small tutorials to perform common bioinformatics analysis steps. See a description of the different topics proposed below. Please vote for the ones you prefer in this doodle (you can also propose other topics in comment). For intermediate R users (the first workshop will not be sufficient to follow).

9.15am-5pm, Friday 15th May in room N4/17, Stewart Biology building. Email us at hgssmcgill@gmail.com to register before Wednesday 6th May.

Data end slides of the workshop.

Advanced visualization.

Ggplot2 package. Representing several “dimensions” of information in publication-ready graphs. Customization of legends and aesthetics aspects. Extra : Genomic regions and tracks representation.

Genomic ranges manipulation.

GenomicRanges package. Creating, comparing, merging, transforming genomic ranges. In practice : importing regions from a bed file, annotating overlapping regions, computing distance to genomic features.

Accessing available genomic annotations.

Examples : AnnotationHub, biomaRtVariantAnnotation packages. Retrieve location of known genomic features (genes, regulatory regions, variants)  for functional analysis.

Analyzing large data.

Reading big files; chunk by chunks analysis; optimized manipulation of large data.frames with dplyr/data.table packages; memory considerations; parallel computing.

Automation and code reduction.

How to use functions to have a modular and clear script, to facilitate reanalysis with different parameters or parameter exploration.

Advanced manipulation of data.frames.

Dplyr, data.table, reshape packages. Applying operations on blocks of data; deriving customized summary of large data.frames; deconstructing 2D matrices into data.frames (eg. for ggplot2 plotting).

Data cleaning.

From raw files,  eg annotation/results downloaded from online resources/collaborator. Importing custom(/ugly) formats; string manipulation for data homogenization (eg to fix discordant gene/sample names).

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