single-cell-tutorial | Single cell current best practices | Genomics library

A docker container with a working sc-tutorial environment is now available here thanks to Leander Dony. If you would like to set up the environment via conda or manually outside of the docker container, please follow the instructions below. To run the tutorial case study, several packages must be installed. As both R and python packages are required, we prefer using a conda environment. To facilitate the setup of a conda environment, we have provided the sc_tutorial_environment.yml file, which contains all conda and pip installable dependencies. R dependencies, which are not already available as conda packages, must be installed into the environment itself. To set up a conda environment, the following instructions must be followed. These steps should set up an environment to perform single cell analysis with the tutorial workflow on a Linux system. Please note that we have encountered issues with conda environments on Mac OS. When using Mac OS we recommend installing the packages without conda using separately installed python and R versions. Alternatively, you can try using the base conda environment and installing all packages as described in the conda_env_instructions_for_mac.txt file. In the base environment, R should be able to find the relevant gsl libraries, so LDFLAGS and CFLAGS should not need to be set. Also note that conda and pip doesn't always play nice together. Conda developers have suggested first installing all conda packages and then installing pip packages on top of this where conda packages are not available. Thus, installing further conda packages into the environment may cause issues. Instead, start a new environment and reinstall all conda packages first. If you prefer to set up an environment manually, a list of all package requirements are given at the end of this document.
Set up the conda environment from the sc_tutorial_environment.yml file. conda env create -f sc_tutorial_environment.yml
Ensure that the environment can find the gsl libraries from R. This is done by setting the CFLAGS and LDFLAGS environment variables (see https://bit.ly/2CjJsgn). Here we set them so that they are correctly set every time the environment is activated. cd YOUR_CONDA_ENV_DIRECTORY mkdir -p ./etc/conda/activate.d mkdir -p ./etc/conda/deactivate.d touch ./etc/conda/activate.d/env_vars.sh touch ./etc/conda/deactivate.d/env_vars.sh Where YOUR_CONDA_ENV_DIRECTORY can be found by running conda info --envs and using the directory that corresponds to your conda environment name (default: sc-tutorail). WHILE NOT IN THE ENVIRONMENT(!!!!) open the env_vars.sh file at ./etc/conda/activate.d/env_vars.sh and enter the following into the file: #!/bin/sh CFLAGS_OLD=$CFLAGS export CFLAGS_OLD export CFLAGS="`gsl-config --cflags` ${CFLAGS_OLD}" LDFLAGS_OLD=$LDFLAGS export LDFLAGS_OLD export LDFLAGS="`gsl-config --libs` ${LDFLAGS_OLD}" Also change the ./etc/conda/deactivate.d/env_vars.sh file to: #!/bin/sh CFLAGS=$CFLAGS_OLD export CFLAGS unset CFLAGS_OLD LDFLAGS=$LDFLAGS_OLD export LDFLAGS unset LDFLAGS_OLD Note again that these files should be written WHILE NOT IN THE ENVIRONMENT. Otherwise you may overwrite the CFLAGS and LDFLAGS environment variables in the base environment!
Enter the environment by conda activate sc-tutorial or conda activate ENV_NAME if you changed the environment name in the sc_tutorial_environment.yml file.
Open R and install the dependencies via the commands: install.packages(c('devtools', 'gam', 'RColorBrewer', 'BiocManager')) update.packages(ask=F) BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot"), version = "3.8")
The following packages are required to run the first version of the case study notebook. For further versions see the README.md in the latest_notebook/ and old_releases/ folders.
Jupyter notebook
IRKernel
rpy2
R >= 3.4.3
Python >= 3.5
scanpy
numpy
scipy
pandas
seaborn
louvain>=0.6
python-igraph
gprofiler-official (from Case study notebook 1906 version)
python-gprofiler from Valentine Svensson's github (vals/python-gprofiler) only needed for notebooks before version 1906
ComBat python implementation from Maren Buettner's github (mbuttner/maren_codes/combat.py) only needed for scanpy versions before 1.3.8 which don't include sc.pp.combat()
scater
scran
MAST
gam
slingshot (change DESCRIPTION file for R version 3.4.3)
monocle 2
limma
ComplexHeatmap
RColorBrewer
clusterExperiment
ggplot2
IRkernel