cPredictor 0.5.0

Cell command line predictor

cPredictor package

This repository defines a command-line tool to predict (cPredictor) datasets according to a cell meta-atlases. At the present time only the meta-atlas for the cornea has been implemented.

Conda and pip

If you have not used Bioconda before, first set up the necessary channels (in this order!). You only have to do this once.

$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge

Install cPredictor into a conda environment and install with PyPI:

$ conda create -n cPredictor python=3.9 pip
$ conda activate cPredictor
$ pip install cPredictor

To see what each of the current functions do you can run these commands:

$ SVM_performance --help
$ SVM_predict --help
$ SVM_import --help

Docker

Alternatively you can run the package containerized through docker:

$ docker pull artsofcoding/cpredictor:latest
$ docker tag artsofcoding/cpredictor:latest cpredictor

For more extensive documentation please navigate to the read-the-docs page on the top right.

Performance with the corneal cell state meta-atlas

Pretrained models can run on ~100.000 cells within 2 minutes on a standard laptop (4 core CPU & 8GB RAM) Check out DagsHub for model testing (internal cross-validation) and calibration DagsHub

To run the container locally we recommend a computer with at least 16 GB of RAM and a 4-core processor.

How to cite

When using this software package, please correctly cite the accompanied DOI under "Citation": https://zenodo.org/doi/10.5281/zenodo.10621121