README.md

Env for reproducing

If you want repeat our whole benchmark and evaluation workflow, please configure environment as follow.

Python env

First, you need to install extra Python packages such as:

• snakemake
• papermill
• jupyter

Warning Do NOT change install order !

mamba create -p ./conda python==3.8 -y && conda activate ./conda
mamba install pytorch=1.11.0 torchvision torchaudio cudatoolkit=11.3 -c pytorch -y
mamba install pyg -c pyg -y

mamba install -c conda-forge papermill parse dill jupyter -y 
mamba install -c bioconda -c conda-forge snakemake==7.12.0 tabulate==0.8.10 -y

git clone [email protected]:gao-lab/SLAT.git
cd SLAT
git checkout tags/v0.2.0
pip install -e ".[dev,doc]"

R env

STEP 1: Install R

You also should configure R environment. We strong recommend to compile a new R-4.1.3 rather than install R in conda.

Warning Please make sure you have deactivated any conda env before using R

cd SLAT/resource
wget https://cran.r-project.org/src/base/R-4/R-4.1.3.tar.gz
tar -xzvf R-4.1.3.tar.gz && cd R-4.1.3 && 
  ./configure --without-x --with-cairo --with-libpng --with-libtiff --with-jpeglib --enable-R-shlib --prefix={YOUR_PATH} &&
   make && make install

STEP 2: Register R kernel

Then register the jupyter kernel for R so snakemake can call R in benchmark workflow.

install.packages('IRkernel')
IRkernel::installspec(name = 'slat_r', displayname = 'slat_r')

STEP 3: Install R packages

At last, please install all R packages we used from renv.lock (see renv).

install.packages('renv')
install.packages('IRkernel')
renv::restore()

System env

Singularity

You also need install singularity, because we use container to ensure the repeatability.