sc_hackathon_2023

Ghent hackathon on Python/R interoperability for single cell and spatial bioinformatics

Installation

Simultaneous development of Python and R packages be tricky, as they have different packages managers and practices. Below is an example workflow to setup an Python and R conda environment to get the code examples up and running. VS Code is presumed as IDE, but Jupyter Lab and RStudio also support conda environments.

If you have Windows, you can use the Windows Subsystem for Linux to install a Linux distribution. You can then use the Linux instructions below.
If you have a Macbook with M1 chip, you can use the instructions at the bottom of this page.
If you have Linux, install conda (or mamba for faster dependency resolving)

conda env update -f env_mixed.yaml
conda activate ghent_hackathon_mixed

Usage

In the examples folder, there are some examples of how to use the packages. They're either Jupyter notebooks .ipynb or Quarto .qmd. You can open the notebooks using Jupyter Lab or VS Code. You can open the Quarto files using VS Code or RStudio. You can convert between the two using the quarto convert command line tool.

Working with conda kernels

You can use the conda environment as a kernel in Jupyter Lab or VS Code. This will allow you to use the environment in a notebook. You can also use the conda environment as a kernel in RStudio. Make sure the environment you want to use has a kernel installed such as ipykernel or r-irkernel. VS Code should pick up on the kernel, especially if you do a Reload Window. You can also use Jupyter Lab and nb_conda_kernels in the main environment.

You can use quarto within the conda environment, but using quarto to access an external conda environment requires an extra configuration step.

Working with git development branches

When hacking or patching external packages, try to use a local development branch. This will make it easier to track changes and merge them back to the original package. Fork the package repository and clone it to a different location. Apply your changes and install the package in editable mode. For example:

pip install -e ../anndata

For R, you can use the devtools package to install a package from a local directory:

devtools::install_local("../anndataR")

To share your changes, you can push them to your forked repository and create a pull request. You can reference the pull request in your environment file using the pip section. For R packages, you can add install devtools install instructions at the top of the script.

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
if (!requireNamespace("anndataR", quietly = TRUE))
    devtools::install_github("scverse/anndataR@issue-82-nullable-vectors-and-boolean-enums")

Macbook ARM support

If you want to run on a Macbook with M1 chip, you need to do one of the following if you want to use Conda and R packages (as Bioconda does not yet support ARM on Mac):

SLOW: use a Linux virtual machine (e.g. UTM and VS Code)
WORKS: use a remote Linux workstation (this can have problems if you want to use graphical interfaces such as RStudio, napari, plotting...)
FAILS: install using the env_mixed_arm.yaml (without bioconda channel) instead of env_mixed.yaml. The BioCManager will then manually install all remaining packages (>5min).
FAILS: install using the env_mixed.yaml, but emulate the x86 architecture using Rosetta. This will but will be faster than option 2, but hacking low level packages could give wrong architecture errors due to the emulation.
```
CONDA_SUBDIR=osx-64 conda env update -f env_mixed.yaml
```
WORKS: Use docker with an RStudio server inside it, like e.g. here.
FAILS: use pixi (see below) to resolve the R/Python environment. This also uses the conda packages, so will have similar issues.

When using option 4, you can test you're actively using Rosetta using a python shell after environment activation:

import platform, subprocess
platform.processor() # should return 'i386' if using Rosetta
subprocess.run(["sysctl", "-n", "sysctl.proc_translated"]) # should return 1 if using Rosetta

Using conda kernels in Pixi

Install pixi

# OR create pixi.toml and install deps
pixi init
pixi add ...

# OR start from pixi.toml
pixi install

# setup kernels
## find out jupyter PATH
which jupyter
## install kernel
PATH=$PATH:/srv/scratch/benjaminr/anaconda3/bin pixi run R
IRkernel::installspec(name = 'ir43pixi', displayname = 'R 4.3 pixi')

## check if install
jupyter kernelspec list

## VS Code Reload, should be able to select the kernel now