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CAME is a tool for Cell-type Assignment and Module Extraction, based on a heterogeneous graph neural network.
For detailed usage, please refer to CAME-Documentation.
CAME outputs the quantitative cell-type assignment for each query cell, that is, the probabilities of cell types that exist in the reference species, which enables the identification of the unresolved cell states in the query data.
Besides, CAME gives the aligned cell and gene embeddings across species, which facilitates low-dimensional visualization and joint gene-module extraction.
It's recommended to create a conda environment for running CAME:
conda create -n env_came python=3.8
conda activate env_cameInstall required packages:
# on CPU
pip install "scanpy[leiden]"
pip install torch # >=1.8
pip install dgl # tested on 0.7.2, better below 1.0.*See Scanpy, PyTorch and DGL for detailed installation guide (especially for GPU version).
Install CAME by PyPI:
pip install cameInstall the developmental version of CAME from source code:
git clone https://github.com/XingyanLiu/CAME.git
cd CAME
python setup.py installThe test code is based on the sample data attached to the CAME package.
It is initially saved in compressed form (CAME/came/sample_data.zip),
and will be automatically decompressed to the default directory
(CAME/came/sample_data/) when necessary, which contains the following files:
- gene_matches_1v1_human2mouse.csv (optional)
- gene_matches_1v1_mouse2human.csv (optional)
- gene_matches_human2mouse.csv
- gene_matches_mouse2human.csv
- raw-Baron_mouse.h5ad
- raw-Baron_human.h5ad
You can access these data by came.load_example_data().
If you tend to apply CAME to analyze your own datasets, you need to prepare at least the last two files for the same species (e.g., cross-dataset integration);
For cross-species analysis, you need to provide another .csv
file where the first column contains the genes in the reference species and the
second contains the corresponding query homologous genes.
NOTE: the file
raw-Baron_human.h5adis a subsample from the original data for code testing. The resulting annotation accuracy may not be as good as using the full dataset as the reference.
Suggestions
If you have sufficient GPU memory, setting the hidden-size
h_dim=512in "came/PARAMETERS.py" may result in a more accurate cell-type transfer.
To test the package, run the python file test_pipeline.py:
# test_pipeline.py
import came
if __name__ == '__main__':
came.__test1__(6, batch_size=2048)
came.__test2__(6, batch_size=None)python test_pipeline.py - Issue Tracker: https://github.com/XingyanLiu/CAME/issues
- Source Code:
- https://github.com/zhanglabtools/CAME
- https://github.com/XingyanLiu/CAME (the developmental version)
If you are having issues, please let us know. We have a mailing list located at:
If CAME is useful for your research, consider citing our work:
Liu X, Shen Q, Zhang S. Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network[J]. Genome Research, 2022: gr. 276868.122.