Warning: Please be aware that these videos are a snapshot, and as such may use an outdated version of the tutorial and/or Galaxy. Below the video you will find links to the tutorials as they appeared at the time of recording.
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Below are video tutorials for this GTN material, created for various (past) events.
Tutorial Video ()
The ability to easily and efficiently analyse RNA-sequencing data is a key
strength of the Bioconductor project. Starting with counts summarised at the
gene-level, a typical analysis involves pre-processing, exploratory data
analysis, differential expression testing and pathway analysis with the
results obtained informing future experiments and validation studies. In this
workflow article, we analyse RNA-sequencing data from the mouse mammary gland,
demonstrating use of the popular edgeR package to import, organise, filter
and normalise the data, followed by the limma package with its voom method,
linear modelling and empirical Bayes moderation to assess differential
expression and perform gene set testing. This pipeline is further enhanced
by the Glimma package which enables interactive exploration of the results
so that individual samples and genes can be examined by the user. The complete
analysis offered by these three packages highlights the ease with which
researchers can turn the raw counts from an RNA-sequencing experiment into
biological insights using Bioconductor.
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