Metadata-Version: 2.1
Name: VIPCCA
Version: 0.1.5
Summary: VIPCCA
Home-page: https://github.com/JHuLab/VIPCCA
Author: Jialu Hu
Author-email: jialuhu@umich.edu
License: MIT
Description: # VIPCCA
        Badges-----------------
        
        Variational inference of probabilistic canonical correlation analysis
        
        introduction......
        
        ............
        
        ### Install VIPCCA
        ```shell
        $ tar -zxvf scxx.x.x.x.tar.gz
        $ pip install -e ./scxx/
        ```
        
        ```shell
        $ pip install vipcca
        ```
        
        **Note**: you need to make sure that the `pip` is for python3. Our package is suitable for tensorflow 1.13.1
        
        
        
        ### Usage
        
        Read the doc url..........................
        
        #### Quick Start
        
        Download example data at http://141.211.10.196/result/test/papers/vipcca/data.tar.gz
        
        ```python
        import VIPCCA as vp
        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",
        							patience_es=50,
        							patience_lr=20,
        							lambda_regulizer=5,
        							# uncomment the following line if a pretrained model was provided in the result folder.
        							# model_file="model.h5" 
        							)
        
        # transform user's single-cell data into shared low-dimensional space and recover gene expression.
        adata_transform=handle.fit_transform()
        
        # Visualization
        pl.run_embedding(adata_transform, path=test_result_path,method="umap")
        pl.plotEmbedding(adata_transform, path=test_result_path, method='umap', group_by="_batch",legend_loc="right margin")
        pl.plotEmbedding(adata_transform, path=test_result_path, method='umap', group_by="celltype",legend_loc="on data")
        ```
        
        
        
        
        #### reference
        https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
        
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
