Installation

Install Julia

To install Julia, follow the Getting Started instructions.

Install Quarto (optional)

These tutorials are written in Quarto. You don’t need Quarto if you only want to play with the notebooks, but you will need it if you want to reprodcue this website locally. See the Get Started page for installation instructions.

Install the Julia extension for your favourite editor/IDE (optional)

Extensions or plugins for VS Code, Emacs, and VIM are linked from the Julia homepage

Install IJulia

To run the tutorial notebooks, you need IJulia. If you installed Quarto, follow these instructions to make sure Quarto can use IJulia to render documents with embedded Julia code.

Install BioFindr

BioFindr is a registered Julia package. To install it, open a Julia console and enter the Pkg REPL by pressing ] from the Julia REPL. Then:

pkg> add BioFindr

See the Pkg documentation for more information. If you’re new to using Pkg, Julia’s package manager, see the Getting Started notes.

BioFindr is still under development and new features are added regularly. Make sure to update your installation regularly:

pkg> up BioFindr

Install the BioFindrTutorials repository

The BioFindrTutorials repository uses DrWatson to make a reproducible scientific project named

BioFindrTutorials

To (locally) reproduce this project, do the following:

  1. Download this code base. Notice that raw data are not in

    julia> using Pkg
julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
julia> Pkg.activate("path/to/this/project")
julia> Pkg.instantiate()

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box, including correctly finding local paths.

You may notice that most scripts start with the commands:

using DrWatson
@quickactivate "BioFindrTutorials"

which auto-activate the project and enable local path handling from DrWatson.

Download and preprocess data

The scripts folder of the project contains a number of files named process_data_*. After downloading the initial data files (see below), you can then run the script to produce processed data files in the Apache Arrow format, which will be stored in the exp_pro folder.

GEUVADIS data

  1. Download preprocessed data files from the GEUVADIS study.

  2. Save all files in the folder exp_raw/findr-data-geuvadis.

  3. Concatenate the transcript files:

    $ cat dt1.csv.gz dt2.csv.gz > dt.csv.gz

  4. Unzip all .gz files.

  5. Create a folder exp_pro/findr-data-geuvadis (sorry, not very clean that this isn’t automated).

  6. Run the script process_data_findr-data-geuvadis.jl. You can either run it from the command line:

    $ julia scripts/process_data_findr-data-geuvadis.jl

    or open the file and run it interactively in your editor/IDE of choice. See Running Code for more information about how to this in VS Code.

  7. Verify that the directory exp_pro/findr-data-geuvadis contains matched .arrow files for each .csv file in the exp_raw/findr-data-geuvadis folder.

Run the tutorials

You are now all set up to run the tutorials. You can either work directly with the *.qmd files in the website folder, or use the jupyter notebooks (*.ipynb files) in the notebooks folder. Make sure to have IJulia installed if using the latter option.