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[spholmes]

This is usually where we try and insist on the necessary pre-requisites explicitly.
For this lesson it might also be the opportunity for providing links to background material which are needed.

[lgeistlinger]

Thanks @spholmes. Here is also where we need to expand and sharpen on pre-requisites based on the comments from @lgatto.

A general statement is provided under https://ccb-hms.github.io/osca-workbench/:

"This tutorial strives to be of interest to the experimental biologists wanting to analyze their data and to the bioinformaticians approaching single-cell data."

In more detail, we list on the same page the following pre-requisites:

• Familiarity with R/Bioconductor, such as the Introduction to data analysis with R and Bioconductor lesson.

• Familiarity with the biology of gene expression and scRNA-seq, such as the review article A practical guide to single-cell RNA-sequencing by Haque et.al.

My current hunch is that we declare background on stats such as PCA, t-SNE, UMAP, etc as beneficial but not required, as the course focuses on the application of these concepts. That means while a good understanding of the underlying concepts will be beneficial for analysis and interpretation, a rough intuition of these concepts as eg provided for PCA in Section 4.2 of the OSCA book will be sufficient for getting started.

[lgeistlinger]

Added to pre-requisites on the landing page in eb69cc0:

• Familiarity with multivariate analysis and dimensionality reduction, such as Chapter 7 of the book Modern Statistics for Modern Biology by Holmes and Huber.

[andrewGhazi]

The learner profile page specifically was updated in #33