Metadata-Version: 2.1
Name: scbean
Version: 0.2.8
Summary: integration
Home-page: https://github.com/JHuLab/VIPCCA
Author: Jialu Hu
Author-email: jialuhu@umich.edu
License: MIT
Description: # scbean
        [![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/scbean?color=blue)
        
        scbean is a package we provide for single-cell data integration and other tasks.
        
        ## scbean--VIPCCA 
        Variational inference of probabilistic canonical correlation analysis
        
        introduction......
        ............
        
        ### Create conda environment
        For more information about conda environment, see this [tutorial](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html).
        ```shell
        $ conda create -n scbean python=3.6
        $ conda activate scbean
        ```
        
        
        ### Install scbean from pypi
        
        ```shell
        $ pip install scbean
        ```
        
        ### Install scbean from GitHub source code
        ```shell
        $ git clone https://github.com/jhu99/scbean.git
        $ cd ./scbean/
        $ pip install .
        ```
        
        **Note**: 
        
        - Please make sure that the `pip` is for python>=3.6. The current release depends on tensorflow with version 2.4.0. Install tenserfolow-gpu if gpu is avialable on the machine.
        
        - If there is a need to run large data sets, we provide version 1.1.1 (depending on tensorflow 1.15.1), which uses sparseTensor to reduce memory usage.
        ```shell
        $ pip install scbean==1.1.1
        ```
        
        ### Usage
        
        For detailed documentation, please check [here](https://vipcca.readthedocs.io/en/latest/).
        
        #### Quick Start
        
        Download the [data](http://141.211.10.196/result/test/papers/vipcca/data.tar.gz) of the sample we provided.
        
        ```python
        import scbean.model.vipcca as vip
        import scbean.tools.utils as tl
        import scbean.tools.plotting as pl
        
        # If your script depends on a specific backend you can use the use() function:
        import matplotlib
        matplotlib.use('TkAgg')
        
        # read single-cell data.
        adata_b1 = tl.read_sc_data("./data/mixed_cell_lines/293t.h5ad", batch_name="293t")
        adata_b2 = tl.read_sc_data("./data/mixed_cell_lines/jurkat.h5ad", batch_name="jurkat")
        adata_b3 = tl.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= tl.preprocessing([adata_b1, adata_b2, adata_b3])
        
        # VIPCCA will train the neural network on the provided datasets.
        handle = vip.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_integrate()
        
        # 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
