Metadata-Version: 2.1
Name: ccAFv2
Version: 2.0.0
Summary: Classify scRNA-seq profiling with highly resolved cell cycle phases.
Home-page: https://github.com/plaisier-lab/ccAFv2_py
Author: Christopher Plaisier
Author-email: plaisier@asu.edu
License: GNU General Public License v3.0
Description: # ccAF Version 2:  cell cycle ASU-Fred Hutch neural network based scRNA-seq cell cycle classifier
        The ability to accurately assign a cell cycle phase based on a transcriptome profile has many potential uses in single cell studies and beyond. The cell cycle classifier is based on a keras/TensorFlow multilayer perceptron artificial neural network (MLP-ANN).
        
        ## Dependencies
        There are four dependencies that must be met for ccAF to classify cell cycle states:
        1. [numpy](https://numpy.org/) - ([install](https://numpy.org/install/))
        2. [scipy](https://www.scipy.org/index.html) - ([install](https://www.scipy.org/install.html))
        3. [scanpy](https://scanpy.readthedocs.io/en/latest/) - ([install](https://scanpy.readthedocs.io/en/latest/installation.html))
        4. [tensorflow](https://www.tensorflow.org/) - ([install](https://www.tensorflow.org/install))
        5. [keras](https://keras.io/) - ([install](https://keras.io/getting_started/))
        
        *Python dependency installation commands:*
        > **NOTE!**  pip may need to be replaced with pip3 depending upon your setup.
        
        ```shell
        pip install numpy scipy scanpy tensorflow keras
        ```
        
        ## Installation of ccAF classifier
        The ccAF classifier can be installed with the following command:
        
        ```shell
        pip install ccAF
        ```
        
        ## Alternatively use the ccAF Docker container
        We facilitate the use of ccAF by providing a Docker Hub container [cplaisier/ccafv2](https://hub.docker.com/r/cplaisier/ccafv2) which has all the dependencies and libraries required to run the ccAF classifier. To see how the Docker container is configured plaese refer to the [Dockerfile](https://github.com/plaisier-lab/docker_ccafv2/blob/master/Dockerfile). Please [install Docker](https://docs.docker.com/get-docker/) and then from the command line run:
        
        ```shell
        docker pull cplaisier/ccaf
        ```
        
        Then run the Docker container using the following command (replace <path to scRNA-seq profiles directory> with the directory where you have the scRNA-seq data to be classified):
        
        ```shell
        docker run -it -v '<path to scRNA-seq profiles directory>:/files' cplaisier/ccafv2
        ```
        
        This will start the Docker container in interactive mode and will leave you at a command prompt. You will then want to change directory to where you have your scRNA-seq or trasncriptome profiling data.
        
        ## Gene labels must be in human Gene Ensembl IDs to run ccAF
        The data input into ccAF must use human Ensembl gene IDs (ENSG<#>), whithout the version number. If your data is not currenly labeled with Ensemble gene IDs you may try [mygene](https://docs.mygene.info/projects/mygene-py/en/latest/) or go to the [BioMart](http://uswest.ensembl.org/biomart/martview).
          
        ## Running ccAF against your scRNA-seq data
        The first step in using ccAF is to import your scRNA-seq profiling data into scanpy. A scanpy data object is the expected input into the ccAF classifier. The test dataset can be downloaded from figshare: [BT324_GSC.h5ad](https://figshare.com/ndownloader/files/42902338)
        
        
        ```python
        import scanpy as sc
        from ccAF
        
        # Load WT U5 hNSC data used to train classifier as a loom file
        set1_scanpy = sc.read_h5ad('data/BT324_GSC.h5ad')
        
        # Predict cell cycle phase labels
        predictedLabels = ccAF.predict_labels(set1_scanpy)
        ```
        
        More complete example is available as [test.py](https://github.com/plaisier-lab/ccAFv2/blob/master/tests/test.py) on the GitHub page.
        
        ## Contact
        For issues or comments please contact:  [Chris Plaisier](mailto:plaisier@asu.edu)
        
        ## Citation
        [Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma.](https://doi.org/10.1101/446344) Samantha A. O'Connor, Heather M. Feldman, Chad M. Toledo, Sonali Arora, Pia Hoellerbauer, Philip Corrin, Lucas Carter, Megan Kufeld, Hamid Bolouri, Ryan Basom, Jeffrey Delrow, Jose L. McFaline-Figueroa, Cole Trapnell, Steven M. Pollard, Anoop Patel, Patrick J. Paddison, Christopher L. Plaisier. bioRxiv 446344; doi: [https://doi.org/10.1101/446344](https://doi.org/10.1101/446344)
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
