Metadata-Version: 2.1
Name: scbean
Version: 0.2.4
Summary: integration
Home-page: https://github.com/JHuLab/VIPCCA
Author: Jialu Hu
Author-email: jialuhu@umich.edu
License: MIT
Description: # VIPCCA
        [![Documentation Status](https://readthedocs.org/projects/vipcca/badge/?version=latest)](https://vipcca.readthedocs.io/en/latest/?badge=latest)
        ![PyPI](https://img.shields.io/pypi/v/vipcca?color=blue)
        
        Variational inference of probabilistic canonical correlation analysis
        
        introduction......
        
        ............
        
        ### Create conda enviroment
        
        ```shell
        $ conda create -n VIPCCA python=3.6
        $ conda activate VIPCCA
        ```
        For more information, see https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html
        
        ### Install VIPCCA from pypi
        
        ```shell
        $ pip install VIPCCA
        ```
        
        ### Install VIPCCA from GitHub source code
        ```shell
        
        $ git clone https://github.com/jhu99/VIPCCA.git
        $ pip install -e ./VIPCCA/
        ```
        
        **Note**: Please make sure that the `pip` is for python>=3.6. The current release depends on tensorflow with version 1.15.4. Install tenserfolow-gpu if gpu is avialable on the machine.
        
        
        ### Usage
        
        https://vipcca.readthedocs.io/en/latest/
        
        #### Quick Start
        
        Download example data at http://141.211.10.196/result/test/papers/vipcca/data.tar.gz
        
        ```python
        import vipcca as vip
        from vipcca import preprocessing as pp
        from vipcca import plotting as pl
        
        # read single-cell data.
        adata_b1 = pp.read_sc_data("./data/mixed_cell_lines/293t.h5ad", batch_name="293t")
        adata_b2 = pp.read_sc_data("./data/mixed_cell_lines/jurkat.h5ad", batch_name="jurkat")
        adata_b3 = pp.read_sc_data("./data/mixed_cell_lines/mixed.h5ad", batch_name="mixed")
        
        # pp.preprocessing include filteration, log-TPM normalization, selection of highly variable genes.
        adata_all= pp.preprocessing([adata_b1, adata_b2, adata_b3])
        
        # VIPCCA will train the neural network on the provided datasets.
        handle = vp.VIPCCA(
        							adata_all,
        							res_path='./results/CVAE_5/',
        							split_by="_batch",
        							epochs=100,
        							lambda_regulizer=5,
        							)
        
        # transform user's single-cell data into shared low-dimensional space and recover gene expression.
        adata_integrate=handle.fit_transform()
        
        # Visualization
        pl.run_embedding(adata_integrate, path='./results/CVAE_5/',method="umap")
        pl.plotEmbedding(adata_integrate, path='./results/CVAE_5/', method='umap', group_by="_batch",legend_loc="right margin")
        pl.plotEmbedding(adata_integrate, path='./results/CVAE_5/', method='umap', group_by="celltype",legend_loc="on data")
        ```
        
        
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
