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If you use cell2location please cite our paper:

Kleshchevnikov, V., Shmatko, A., Dann, E. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-021-01139-4 https://www.nature.com/articles/s41587-021-01139-4

Please note that cell2locations requires 2 user-provided hyperparameters (N_cells_per_location and detection_alpha) - for detailed guidance on setting these hyperparameters and their impact see the flow diagram and the note. Many real datasets (especially human) show within-slide variability in RNA detection sensitivity - requiring you to try both recommended settings of the detection_alpha parameter: detection_alpha=200 for low within-slide technical variability and detection_alpha=20 for high within-slide technical variability.

Cell2location is a principled Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. This is achieved by estimating which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance).

The tutorial covering the estimation of expresson signatures of reference cell types, spatial mapping with cell2location and the downstream analysis can be found here: https://cell2location.readthedocs.io/en/latest/

You can also try cell2location on Google Colab on a smaller data subset containing somatosensory cortex.

Please report bugs via https://github.com/BayraktarLab/cell2location/issues and ask any usage questions in https://github.com/BayraktarLab/cell2location/discussions.

Cell2location package is implemented in a general way (using https://pyro.ai/ and https://scvi-tools.org/) to support multiple related models - both for spatial mapping, estimating reference cell type signatures and downstream analysis.

We suggest using a separate conda environment for installing cell2location.

Create conda environment and install cell2location package

conda create -y -n cell2loc_env python=3.9

conda activate cell2loc_env
pip install git+https://github.com/BayraktarLab/cell2location.git#egg=cell2location[tutorials]

Finally, to use this environment in jupyter notebook, add jupyter kernel for this environment:

conda activate cell2loc_env
python -m ipykernel install --user --name=cell2loc_env --display-name='Environment (cell2loc_env)'

If you do not have conda please install Miniconda first:

cd /path/to/software
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
# use prefix /path/to/software/miniconda3

Before installing cell2location and it's dependencies, it could be necessary to make sure that you are creating a fully isolated conda environment by telling python to NOT use user site for installing packages, ideally by adding this line to your ~/.bashrc file , but this would also work during a terminal session:

export PYTHONNOUSERSITE="someletters"

User documentation is availlable on https://cell2location.readthedocs.io/en/latest/.

Cell2location architecture is designed to simplify extended versions of the model that account for additional technical and biologial information. We plan to provide a tutorial showing how to add new model classes but please get in touch if you would like to contribute or build on top our package.

We thank all paper authors for their contributions: Vitalii Kleshchevnikov, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W King, Tong Li, Artem Lomakin, Veronika Kedlian, Mika Sarkin Jain, Jun Sung Park, Lauma Ramona, Liz Tuck, Anna Arutyunyan, Roser Vento-Tormo, Moritz Gerstung, Louisa James, Oliver Stegle, Omer Ali Bayraktar

We also thank Krzysztof Polanski, Luz Garcia Alonso, Carlos Talavera-Lopez, Ni Huang for feedback on the package, Martin Prete for dockerising cell2location and other software support.

See https://github.com/BayraktarLab/cell2location/discussions

We also provide an experimental numpyro translation of the model which has improved memory efficiency (allowing analysis of multiple Visium samples on Google Colab) and minor improvements in speed - https://github.com/vitkl/cell2location_numpyro. You can try it on Google Colab - however note that both numpyro itself and cell2location_numpyro are in very active development. Numpyro+JAX are being introduces into scvi-tools so follow updates on that.

export PYTHONNOUSERSITE="literallyanyletters"
conda create -y -n test_scvi16_cuda113 python=3.9
conda activate test_scvi16_cuda113
conda install -y -c anaconda hdf5 pytables git
pip install scvi-tools
pip install git+https://github.com/BayraktarLab/cell2location.git#egg=cell2location[tutorials]
pip3 install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 -f https://download.pytorch.org/whl/torch_stable.html
conda activate test_scvi16_cuda113
python -m ipykernel install --user --name=test_scvi16_cuda113 --display-name='Environment (test_scvi16_cuda113)'