Skip to main content

cell-type assignment and gene module extraction of scRNA-seq

Project description

CAME

DOI

English | 简体中文

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.

Installation

It's recommended to create a conda environment for running CAME:

conda create -n env_came python=3.8
conda activate env_came

Install 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 came

Install the developmental version of CAME from source code:

git clone https://github.com/XingyanLiu/CAME.git
cd CAME
python setup.py install

Example data

The 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.h5ad is 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=512 in "came/PARAMETERS.py" may result in a more accurate cell-type transfer.

Test CAME's pipeline (optional)

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 

Contribute

Support

If you are having issues, please let us know. We have a mailing list located at:

Citation

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.

Preprint: https://doi.org/10.1101/2021.09.25.461790

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

came-0.1.13.tar.gz (16.1 MB view hashes)

Uploaded source

Built Distribution

came-0.1.13-py3-none-any.whl (16.1 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page