Installation

Installation#

It is recommended to choose conda as your package manager. Conda can be obtained, e.g., by installing the Miniconda distribution. For detailed instructions, please refer to the respective documentation.

With conda installed, open your terminal and create a new environment by executing the following commands::

conda create -n facspy python=3.10
conda activate facspy

PyPI#

Currently, FACSPy is in beta-phase. There will be a PyPI release once the beta phase is finished.

pip install FACSPy

Development Version#

In order to get the latest version, install from GitHub using

pip install git+https://github.com/TarikExner/FACSPy@main

Alternatively, clone the repository to your local hard drive via

git clone https://github.com/TarikExner/FACSPy.git && cd FACSPy
git checkout --track origin/main
pip install .

Note that while FACSPy is in beta phase, you need to have access to the private repo.

Jupyter notebooks#

Jupyter notebooks are highly recommended due to their extensive visualization capabilities. Install jupyter via

conda install jupyter

and run the notebook by entering jupyter-notebook into the terminal.

Installing with an R environment#

AnnData objects can be readily converted to SingleCellExperiments in R via the rpy2 and anndata2ri interface. Combining R and python can be difficult sometimes, the steps described here have been tested on Windows (r-base=4.1.3) and linux (r-base=4.2.1).

In order to setup an environment covering both R and python use the following commands in conda:

conda create -n facspy_r 
conda activate facspy_r
conda install python=3.10 jupyter r-base

In order to install FACSPy with the dependencies for the data analysis in R and python, either install from PyPI:

pip install FACSPy[r_env]

or locally:

git clone https://github.com/TarikExner/FACSPy.git && cd FACSPy
git checkout --track origin/main
cd ..
pip install FACSPy
pip install anndata2ri rpy2

This is the minimal setup in order to convert an AnnData object to a SingleCellExperiment object. You can then save the SingleCellExperiment and use your own R distribution for further analysis.

If you want to install R-packages in the same environment, please follow the instructions below.

Example installation for CATALYST and Spectre#

Make sure you have RTools installed when using Windows.

CATALYST and Spectre are R libraries for the analysis of cytometry data. In order to install CATALYST and Spectre, use the following additional commands:

conda install -c r rtools
conda install -c conda-forge r-curl r-scales r-igraph r-cowplot r-ggridges r-ggbeeswarm r-viridis r-devtools r-emmeans r-recipes r-biocmanager r-matrixstats r-ggplot2 r-rainbow r-rcpphnsw r-rcpparmadillo r-sf r-tiff r-cairo r-terra

From within an R session:

> install.packages(c("RCurl", "curl", "XML")) # don't use compilation
> BiocManager::install("CATALYST")
> library(CATALYST)
> devtools::install_github("immunedynamics/spectre")
> library(Spectre)

R package installation#

You can install R packages different ways, here we cover the four most relevant scenarios.

  1. Open an R session by typing R into the terminal. Install packages via:

    > install.packages("ggplot2")

  2. Open an R session by typing R into the terminal and install packages via BioConductor:

    > install.packages("BiocManager")
> BiocManager::install("SingleCellExperiment")

  3. Open an R session by typing R into the terminal and install from GitHub via:

    > install.packages("devtools")
> devtools::install_github("saeyslab/CytoNorm")

  4. Install via conda:

    >>> conda install -c conda-forge r-hdf5r

Please check the availability of your desired package on the respective websites. If there are conda versions available, this is often preferred due to build complications when systems libraries and conda libraries for building are not the same.

Issues#

If there are any issues, bugs or problems, please do not hesitate to submit an issue.

Contributing#

Your contributions are welcome!

Please submit an issue or pull request via Github. Pull requests with updated documentation and accompanying unit tests are preferred but not obligate!

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