Metadata-Version: 2.1
Name: Cell_BLAST
Version: 0.3.6
Summary: Single-cell transcriptome querying tool
Home-page: https://github.com/gao-lab/Cell_BLAST
Author: Zhijie Cao
Author-email: caozj@mail.cbi.pku.edu.cn
License: UNKNOWN
Description: # Cell BLAST
        
        Cell BLAST is a cell querying tool for single-cell transcriptomics data.
        
        For each query cell, it searches for most similar cells in the reference database.
        Annotations in reference cells, e.g. cell type, can then be transfered to query
        cells based on cell-to-cell similarities. See our
        [preprint](https://www.biorxiv.org/content/10.1101/587360v2) for details.
        
        ![flowchart](docs/_static/flowchart.svg)
        
        ## Installing the python package
        
        We only support installation via pip right now.
        
        Installation within virtual environments are recommended, see
        [virtualenv](https://virtualenv.pypa.io/en/latest/) or
        [conda](https://conda.io/docs/user-guide/tasks/manage-environments.html).
        
        For conda, here's a one-liner to set up an empty environment
        for installing Cell BLAST:
        
        `conda create -n cb python=3.6 && source activate cb`
        
        Now follow the instructions below to install Cell BLAST:
        
        1. Make sure you have a working version of tensorflow or tensorflow-gpu
           (version >= 1.5). You can follow the
           [official instructions](https://www.tensorflow.org/install/)
           about how to install tensorflow (and dependencies like CUDA and CuDNN
           for the GPU version), or just install via anaconda, which handles
           dependencies automatically:
        
           For installing the GPU supported version:
           `conda install tensorflow-gpu=1.8`
        
           For installing the CPU only version:
           `conda install tensorflow=1.8`
        
        2. Install Cell BLAST by running:
           `pip install Cell-BLAST`
        
        3. Check if the package can be imported in python interpreter:
           `import Cell_BLAST as cb`
        
        Online documentation can be found [here](http://cblast.gao-lab.org/doc/index.html).
        
        ## Web-based interface
        
        We also provide a [Web-based service](http://cblast.gao-lab.org/) for
        off-the-shelf querying of our ACA reference panels.
        
        ## Repository structure
        
        * The `Cell_BLAST` directory contains the Cell BLAST python package.
        * The `Datasets` directory contains scripts used for building the ACA database.
        * The `Evaluation` directory contains scripts used for benchmarking
          and producing some figures of the manuscript.
        * The `docs` directory contains files used to generate the online documentation.
        * The `test` directory contains unit tests for the python package.
        
        ## Reproduce results
        
        ### Obtain required data files
        
        <!-- TODO: Provide data download information here -->
        
        ### Environment setup
        
        #### Python
        
        First create a conda environment and install most packages via:
        
        ```bash
        conda env create -n cb-gpu -f env.yml && conda activate cb-gpu
        ```
        
        Optionally, if GPU does not work properly (possibly due to inconsistent conda
        channels used), reinstalling tensorflow should solve the problem:
        
        ```bash
        conda install tensorflow=1.8.0 tensorflow-base=1.8.0 tensorflow-gpu=1.8.0 --force-reinstall
        ```
        
        Finally, install customized packages or packages unavailable in conda.
        All dependencies have already been installed via `env.yml`,
        so `--no-deps` is added to prevent overwriting conda installed packages:
        
        ```bash
        pip install Cell-BLAST==0.2.14 --no-deps
        pip install local/scScope-0.1.5.tar.gz --no-deps  # Add random seed setting
        pip install local/DCA-0.2.2.tar.gz --no-deps  # Allow GPU memory growth, suppress integer warning
        pip install local/DCA_modpp-0.2.2.tar.gz --no-deps  # Modify preprocessing
        pip install local/ZIFA-0.1.tar.gz --no-deps  # Remove fixed random seeds
        pip install local/Dhaka-0.1.tar.gz --no-deps
        pip install local/scvi-0.2.3.tar.gz --no-deps  # Fix torch bugs
        ```
        
        #### R
        
        Start R (tested on version `3.6.0`) at project root and run:
        
        ```R
        packrat::restore()
        ```
        
        Then install the customized version of Seurat by:
        
        ```R
        install.packages("local/seurat-2.3.3.tar.gz", repos=NULL, type="source")  # Remove fixed random seeds
        ```
        
        For CCA anchor (Seurat v3), we used a separate packrat environment.
        
        To build this dedicated environment, start R at directory "packrat/envs/seurat_v3" and run:
        
        ```R
        packrat::restore()
        ```
        
        Then install the customized version of Seurat v3 by:
        
        ```R
        install.packages("../../../local/seurat-3.0.2.tar.gz", repos=NULL, type="source")  # Remove fixed random seeds
        ```
        
        ### Run all benchmarks and reproduce figures
        
        Make sure the conda environment create above is activated.
        Go to directory "Evaluation" and run the following command:
        
        ```bash
        snakemake -prk
        ```
        
        Some jobs will likely fail, e.g. due to timeout or memory issues, and cause
        downstream steps, including result plotting, to fail as well.
        
        These failing jobs will be blacklisted in future runs, so just run the above
        command for a second time, and results for the successful jobs should be
        summarized and plotted without error.
        
        ## Contact
        
        Feel free to submit an issue or contact us at
        [cblast@mail.cbi.pku.edu.cn](mailto:cblast@mail.cbi.pku.edu.cn)
        for problems about the python package, website or database.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
