Tutorial 2: 10X Visium Breast dataset
=====================================

.. raw:: html

    <div style="font-size: 15px;">In this tutorial, we show how to apply scSTADE to identify spatial domains on 10X Visium data. As a example, we analyse the Breast dataset.</div>

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.. raw:: html

    <div style="font-size: 15px;">The source code package is freely available at https://github.com/cuiyaxuan/scSTADE/tree/master. The datasets used in this study can be found at https://drive.google.com/drive/folders/1H-ymfCqlDR1wpMRX-bCewAjG5nOrIF51?usp=sharing.</div>

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.. code:: ipython3

    #Low-resolution spatial transcriptomics data simplified version.
    import sys
    sys.path.append("/.../scSTADE-master/scSTADE_Cluster_Functions")
    # Input the path.
    from scSTADE import scSTADE
    import os
    import torch
    import pandas as pd
    import scanpy as sc
    from sklearn import metrics
    import multiprocessing as mp
    
    def setup_seed(seed=41):
        import torch
        import os
        import numpy as np
        import random
        torch.manual_seed(seed)  
        np.random.seed(seed)  # Numpy module.
        random.seed(seed)  # Python random module.
        if torch.cuda.is_available():
            # torch.backends.cudnn.benchmark = False
            torch.backends.cudnn.deterministic = True
            torch.cuda.manual_seed(seed)  
            torch.cuda.manual_seed_all(seed) 
            #os.environ['PYTHONHASHSEED'] = str(seed)
    
    setup_seed(41)
    
    device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
    
    
    n_clusters = 30  ###### the number of spatial domains.
    file_fold = '/home/cuiyaxuan/spatialLIBD/3.Human_Breast_Cancer' #### to your path
    adata = sc.read_visium(file_fold, count_file='filtered_feature_bc_matrix.h5', load_images=True) #### project name
    adata.var_names_make_unique()
    model = scSTADE(adata,device=device,n_top_genes=5000)
    adata = model.train()
    radius = 50
    tool = 'mclust' # mclust, leiden, and louvain
    from utils import clustering
    
    if tool == 'mclust':
       clustering(adata, n_clusters, radius=radius, method=tool, refinement=True)
    elif tool in ['leiden', 'louvain']:
       clustering(adata, n_clusters, radius=radius, method=tool, start=0.1, end=2.0, increment=0.01, refinement=False)
    
    adata.obs['domain']
    adata.obs['domain'].to_csv("label.csv")


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    Begin to train ST data...


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    Optimization finished for ST data!
    fitting ...
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.. code:: ipython3

    import matplotlib as mpl
    import scanpy as sc
    import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt
    import warnings
    import visual
    mpl.rcParams['pdf.fonttype'] = 42
    mpl.rcParams["font.sans-serif"] = "Arial"
    warnings.filterwarnings('ignore')
    file_fold = '/home/cuiyaxuan/spatialLIBD/3.Human_Breast_Cancer' #### to your path
    adata = sc.read_visium(file_fold, count_file='filtered_feature_bc_matrix.h5', load_images=True) #### project name
    df_label=pd.read_csv('./label.csv', index_col=0) 
    visual.visual(adata,df_label)



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    #cells after MT filter: 3798



.. image:: 2_Example_BreastSlice_test_files/2_Example_BreastSlice_test_4_1.png
   :width: 362px
   :height: 337px


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    WARNING: saving figure to file figures/showvisualdomainplot_plot.pdf



.. image:: 2_Example_BreastSlice_test_files/2_Example_BreastSlice_test_4_3.png
   :width: 362px
   :height: 337px