Tree Structure Construction
====================================================

This dataset contains 4,714 cells from the **Red Nucleus** region of the human midbrain, as part of the **Human Brain Cell Atlas**. 
For more details, refer to the `description <https://cellxgene.cziscience.com/e/470565f2-5afc-456a-b617-18e4496c04fd.cxg/>`_. 
It is available for free download in **h5ad** format from the **CELLxGENE** website via this link: `Download Link <https://datasets.cellxgene.cziscience.com/5488ff72-58ed-4f0d-913c-1b6d4d8412b1.h5ad>`_.

Hierarchical Clustering of the Data
--------------------------------------------------

.. code-block:: python

    import requests
    import anndata
    import CellScope
    import numpy as np
    from scipy.sparse import issparse
    url = "https://datasets.cellxgene.cziscience.com/5488ff72-58ed-4f0d-913c-1b6d4d8412b1.h5ad"
    file_path = "Siletti-1.h5ad"
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        with open(file_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
    adata = anndata.read_h5ad("Siletti-1.h5ad")
    fea_raw = adata.X
    cell_types = adata.obs['cell_type']
    label = np.array(cell_types)
    fea_raw,fea_log,fea = CellScope.cs.Normalization(fea_raw)
    fea_Fitting_1, Signal_Space, Center_index = CellScope.cs.Manifold_Fitting_1(fea)
    if issparse(fea_Fitting_1):
        fea_Fitting_1 = fea_Fitting_1.toarray()
    fea_Fitting_2, fitting_index, index_after_outlier_removal = CellScope.cs.Manifold_Fitting_2(fea_Fitting_1)
    T_all_1 = CellScope.cs.GraphCluster(fea_Fitting_1)
    T_all_2 = CellScope.cs.GraphCluster(fea_Fitting_2)
    Y_1 = CellScope.cs.Visualization(fea_Fitting_1)
    Y_2 = CellScope.cs.Visualization(fea_Fitting_2)

Tree Structure Visualization
--------------------------------------------------

.. code-block:: python

    Y_initial, label_step0, Y_1, Title_1, Y_all, Title_all, index_1, index_all, step0, step1 = CellScope.ts.generate_tree_structured(fea_Fitting_1, T_all_1, step0 = None, step1 = 8)
    CellScope.ts.visualize_tree_structured(Y_initial, label_step0, Y_1, Title_1, Y_all, Title_all, index_1, index_all, step0, step1, T_all_1, save_fig = True, save_path='Res')

The current version of CellScope's code for drawing tree structures can generate a visualization for each node. The naming convention is as follows: Initial represents the visualization of all cells; Cluster 0 to Cluster 5 represent the visualization of the first-layer nodes; Cluster 51 and Cluster 52 correspond to the first and second subclusters of Cluster 5, respectively, for the second-layer nodes, and so forth.

.. image:: _static/Tree_Structure_Each.png

Through this set of image outputs, CellScope provides users with a template for self-assembly using Adobe Illustrator software, as shown in the figure below.

.. image:: _static/5class.png

The current version of CellScope can automatically generate hierarchical tree diagrams with up to three levels.

.. code-block:: python

    CellScope.ts.tree_structure_visualization_static(T_all_1,step0,step1,Title_1,Title_all,Y_initial,Y_1,Y_all,index_1,index_all, save_figure=True, filename="Res/tree_visualization_static.pdf")

.. image:: _static/tree_visualization_static.png

To better observe cell differentiation processes and pathways, CellScope provides dynamic visualization of hierarchical tree structures.

.. code-block:: python

    CellScope.ts.tree_structure_visualization_dynamic(T_all_1,step0,step1,Title_1,Title_all,Y_initial,Y_1,Y_all,index_1,index_all,save_gif=True, 
                                       gif_filename="tree_animation.gif", dpi=100)

.. image:: _static/tree_animation.gif
   :align: center

If you are more interested in the differentiation at a specific step or deeper differentiation pathways, we recommend using tree_structure_visualization_step to progressively generate the branches of the tree-structured visualization.

.. code-block:: python

    _,_ = CellScope.ts.tree_structure_visualization_step(T_all_1,step0,step1,Title_1,Title_all,Y_initial,Y_1,Y_all,index_1,index_all,main_radius=2.0,orbit_radius=1.5,
    orbit_distance=4.0,branch_params=None,save_fig = False,save_path = '')

.. image:: _static/tree_visualization_step.png

Marker Gene Between Each clusters
--------------------------------------------------

.. code-block:: python

    n = Y_initial.shape[0]
    Gene_Name = adata.var['feature_name']
    Gene_Name = np.array(Gene_Name)
    for ii in range(len(Title_1)):
        cluster1 = index_1[ii]
        cluster2 = np.setdiff1d(range(n),cluster1)
        marker_gene_indices,_ = CellScope.fm.FindMarker(fea_log, cluster1, cluster2, selected_number = 5, selected_method = 'diff pct')
        marker_gene_name = Gene_Name[marker_gene_indices]
        CellScope.gev.compare_violin_plot_between_classes(fea_log, marker_gene_indices, marker_gene_name, 
                                                cluster1, cluster2, class_name=[Title_1[ii], 'Rest'], 
                                                figsize=(10, 3), save_fig=False, save_path=None)
        
    for ii in range(int(len(index_all)/2)):
        cluster1 = index_all[2*ii]
        cluster2 = index_all[2*ii+1]
        marker_gene_indices,_ = CellScope.fm.FindMarker(fea_log, cluster1, cluster2, selected_number = 5, selected_method = 'diff pct')
        marker_gene_name = Gene_Name[marker_gene_indices]
        CellScope.gev.compare_violin_plot_between_classes(fea_log, marker_gene_indices, marker_gene_name, 
                                                cluster1, cluster2, class_name=[Title_all[2*ii], Title_all[2*ii+1]], 
                                                figsize=(10, 3), save_fig=False, save_path=None)

Using the violin plots of the marker genes in Cluster 1 versus other cells, as well as between Cluster 3-2-1 and Cluster 3-2-2 as examples.

.. image:: _static/Tree_Structure_Marker_Gene_1.png
.. image:: _static/Tree_Structure_Marker_Gene_2.png