Introduction to RNA-seqWhat are we measuring in an RNA-seq experiment?Experimental design considerationsRNA-seq quantification: from reads to count matrixFinding the reference sequencesWhere are we heading towards in this workshop?

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Illustration of part of the central dogma of molecular biology, where DNA is transcribed to RNA, and intronic sequences are spliced out

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Illustration of the major experimental steps of an RNA-seq experiment

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A classification of many different factors affecting measurements obtained from an experiment into treatment, biological, technical and error effects

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Illustration of a set of reads generated by a sequencer, and genomic and transcriptomic reference sequences

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An example MA plotAn example heatmap

RStudio Project and Experimental Data

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Your working directory should look like this
Your working directory should look like this

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A new .Rproj file should be created in your chosen working directory.
A new .Rproj file should be created in your chosen working directory.

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Your R working directory should now be set to where the .Rproj file resides.
Your R working directory should now be set to where the .Rproj file resides.

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A file named GSE96870_counts_cerebellum.csv should now reside in the data folder.
A file named GSE96870_counts_cerebellum.csv should now reside in the data folder.

Importing and annotating quantified data into R

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Exploratory analysis and quality control

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Differential expression analysis

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Shrinkage of log fold changes is useful for visualization and ranking of genes, but for result exploration typically the independentFiltering argument is used to remove lowly expressed genes.

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Extra exploration of design matrices

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Gene set enrichment analysis

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Next steps