Metadata-Version: 2.1
Name: cellpath
Version: 0.2.dev0
Summary: CellPath, multiple trajectories inference in single cell RNA-Seq data from RNA velocity
Home-page: https://github.com/PeterZZQ/CellPath
Author: Ziqi Zhang
Author-email: zhangziqibuaa@gmail.edu
License: GNU General Public License v3 (GPLv3)
Description: 
        # CellPath(Inference of multiple trajectories in single cell RNA-Seq data from RNA velocity)
        
        CellPath v0.1.0
        
        [Zhang's Lab](https://xiuweizhang.wordpress.com), Georgia Institute of Technology
        
        Developed by Ziqi Zhang
        
        ## Description
        CellPath is a single cell trajectory inference method that infers cell developmental trajectory using single-cell RNA Sequencing data and RNA-velocity data. The preprint is posted on bioarxiv: https://www.biorxiv.org/content/10.1101/2020.09.30.321125v2
        
        ## News
        Include leiden algorithm for meta-cell clustering, which is more suitable for datasets with intricate trajectories. You can specify the clustering algorithm you use with either `flavor = "leiden"` or `flavor = "k-means"` in `cellpath.meta_cell_construction()` or `cellpath.all_in_one()`, please check the `run_cellpath.ipynb` for more details.
        
        ## Dependencies
        ```
        Python >= 3.6.0
        
        numpy >= 1.18.2
        
        scipy >= 1.4.1
        
        networkx>=2.5
        
        pandas >= 1.1.5
        
        scikit-learn >= 0.22.1
        
        anndata >= 0.7.6
        
        scvelo >= 0.2.3
        
        seaborn >= 0.10.0
        
        statsmodels >= 0.12.1 (optional, for differentially expressed gene analysis)
        
        rpy2 >= 3.3.0 (optional, for principal curve only)
        ```
        
        ## Installation
        
        ### Install from pypi
        
        ```
        pip install cellpath
        ```
        
        ### Install from github
        
        Clone the repository with
        
        ```
        git clone https://github.com/PeterZZQ/CellPaths.git
        ```
        
        And run 
        
        ```
        cd CellPaths/
        pip install .
        ```
        
        Uninstall using
        
        ```
        pip uninstall cellpath
        ```
        
        ## Usage
        
        `run_cellpath.ipynb` provide a short pipeline of running cellpaths using **cycle-tree** trajectory dataset in the paper.
        
        * Initialize using adata with calculated velocity using scvelo
        ```
        cellpath_obj = cp.CellPath(adata = adata, preprocess = True)
        ```
        
        `preprocessing`: the velocity has been calculated and stored in adata or not, if False, the velocity will be calculated during initialization with scvelo
        
        * Run cellpath all in one
        ```
        cellpath_obj.all_in_one(num_metacells = num_metacells, n_neighs = 10, pruning = False, num_trajs = num_trajs, insertion = True, prop_insert = 0.50)
        ```
        
        `num_metacells`: number of meta-cells in total
        
        `n_neighs`: number of neighbors for each meta-cell
        
        `pruning`: way to construct symmetric k-nn graph, prunning knn edges or including more edges
        
        `num_trajs`: number of trajectories to output in the end
        
        `insertion`: insert unassigned cells to trajectories or not
        
        `prop_insert`: proportion of cells to be incorporated into the trajectories
        
        `Pseudo-time and branching assignment result
        
        ```
        cellpath_obj.pseudo_order
        ```
        * Additional visualizations, please check `run_cellpath.ipynb` for details.
        
        ## Datasets
        * You can access the real dataset that we used for the benchmarking through: https://www.dropbox.com/sh/nix4wnoiwda5id5/AACTxvGTQ82UzwMJs2IWSriKa?dl=0. You can reproduce the result by putting the file into the root directory and run the notebook in `./Examples/`. 
        
            * `./Examples/CellPath_hema.ipynb`: mouse hematopoiesis dataset.
            * `./Examples/CellPath_dg.ipynb`: dentate-gyrus dataset.
            * `./Examples/CellPath_pe.ipynb`: pancreatic endocrinogenesis dataset.
            * `./Examples/CellPath_forebrain.ipynb`: forebrain dataset.
        
        
        ## Contents
        
        * `CellPath/` contains the python code for the package
        * `example_data/real/` contains four real datasets, used in the paper, dentate-gyrus dataset, pancreatic endocrinogenesis dataset and human forebrain dataset. Files in real_data folder can be downloaded from [dropbox](https://www.dropbox.com/sh/s6hzrxv1nojf35t/AADROI0j16a1MtJLbHE5e1Hpa?dl=0)
        * `example_data/simulated/` contains simulated cycle-tree dataset
        
        
        ## Test in manuscript
        Test script for the result in manuscript can be found with the [link](https://github.com/PeterZZQ/CellPath_test)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: principal_curve
