Metadata-Version: 2.1
Name: excellxgene
Version: 1.2.3
Summary: Web application for exploration of large scale scRNA-seq datasets, upgraded to enable end-to-end interactive analysis.
Home-page: https://github.com/czbiohub/cellxgene
Author: Chan Zuckerberg Biohub
Author-email: alexander.tarashansky@czbiohub.org
License: MIT
Description: <img src="./docs/cellxgene-logo.png" width="300">
        
        # Exploratory CellxGene (ExCellxGene)
        This fork implements some of the key features that have been highly requested by the data science team at CZBiohub.
        
        Features include:
        - Hotkeys (SHIFT+? to see a tooltip describing all available  hotkeys)
        - End-to-end interactive analysis and reembedding, with new embeddings hierarchically organized.
        - LIDAR graph interaction mode (the airplane) - Show an interactive tooltip describing the cells underneath your cursor. Very helpful for the color impaired or for large datasets with hundreds of labels.
        - Sankey plots
        - Leiden clustering
        - Label fusion and deletion
        - Interactive selection of data layer for expression visualization
        - Many other quality-of-life improvements.
        
        ## Patch notes (v1.2.3)
        - Gene sets are now grouped based on their descriptions under collapsible headers.
        - Gene sets are now more compact, displaying 10 genes at a time with buttons to flip through pages.
        - Differential expression now calculates the top 100 genes.
        - A new button in the menubar allows you to calculate marker genes for all labels in a selected category.
        - Embeddings are now indented according to their hierarchical organization, and nested embeddings are collapsible.
        - Categorical labels are now sortable based on the currently displayed continuous medatada.
        - All preprocessing and reembedding parameters now have a tooltip.
        - Added a button to display hotkey menu to the menubar.
        - Various bugfixes.
        
        ### Installation
        
        1. Install miniconda if conda not available already:
        
        ```
        wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O ~/miniconda.sh
        bash ~/miniconda.sh -b -p $HOME/miniconda
        ```
        
        2. Create and activate a new environment:
        
        ```
        conda create -n cxg python=3.7
        conda activate cxg
        ```
        
        3. Install excellxgene with pip:
        ```
        pip install excellxgene
        ```
        
        4. Download the git repository to get the example datasets (assumes git is available, if not install it with conda install -c anaconda git)
        ```
        git clone https://github.com/czbiohub/cellxgene
        cd cellxgene
        ```
        Datasets are stored in `example-dataset`
        
        5. Launch cellxgene with:
        ```
        cellxgene launch example-dataset
        ```
        
        
        This should launch a cellxgene session with all the datasets in example-datasets/ loaded in.
        
        If you're running excellxgene remotely, please launch with:
        ```
        cellxgene launch example-datasets --host 0.0.0.0
        ```
        
        Ping me on the Biohub slack (@Alec) if you have any questions!
        
Platform: UNKNOWN
Classifier: Framework :: Flask
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
Provides-Extra: prepare
