Note

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3.three dims models reconstruction of subpopulations

This notebook demonstrates the process of the 3D models reconstruction of subpopulations based on 3D spatial transcriptome data. This is done in the following three steps.

1. Construct the 3D point cloud model;

2. Construct the mesh model based on the 3D point cloud model;

3. Save the models.

This example reconstructs 3D Drosophila CNS model based on 3D spatial transcriptome data of Drosophila CNS.

Packages

import os
import sys
from pathlib import Path

import numpy as np
import matplotlib.colors as mplc
import spateo as st

|-----> setting visualization default mode in dynamo. Your customized matplotlib settings might be overritten.

/home/yao/.local/lib/python3.8/site-packages/nxviz/__init__.py:18: UserWarning:
nxviz has a new API! Version 0.7.3 onwards, the old class-based API is being
deprecated in favour of a new API focused on advancing a grammar of network
graphics. If your plotting code depends on the old API, please consider
pinning nxviz at version 0.7.3, as the new API will break your old code.

To check out the new API, please head over to the docs at
https://ericmjl.github.io/nxviz/ to learn more. We hope you enjoy using it!

(This deprecation message will go away in version 1.0.)

  warnings.warn(
/home/yao/anaconda3/envs/BGIpy38_tf2/lib/python3.8/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (5.0.0)/charset_normalizer (2.0.12) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
/home/yao/anaconda3/envs/BGIpy38_tf2/lib/python3.8/site-packages/spaghetti/network.py:36: FutureWarning:

The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.

Data source

# embryo anndata
adata = st.sample_data.drosophila(filename="E7-9h_cellbin_tdr_v2.h5ad")
adata

AnnData object with n_obs × n_vars = 25857 × 8136
    obs: 'area', 'slices', 'nGenes', 'nCounts', 'pMito', 'nMito', 'pass_basic_filter', 'scc', 'auto_anno', 'anno_cell_type', 'anno_tissue', 'anno_germ_layer', 'actual_stage'
    uns: 'PCs', '__type', 'auto_anno_result', 'dendrogram_anno_cell_type', 'dendrogram_anno_germ_layer', 'dendrogram_anno_tissue', 'explained_variance_ratio_', 'latter_models_align', 'neighbors', 'pca_mean', 'pca_valid_ind', 'pearson_X_neighbors', 'rank_genes_groups', 'rank_genes_groups_anno_cell_type', 'rank_genes_groups_anno_germ_layer', 'rank_genes_groups_anno_tissue', 'scc', 'spatial'
    obsm: '3d_align_spatial', 'align_spatial', 'bbox', 'before_3d_align_spatial', 'contour', 'pearson_X_pca', 'pearson_X_umap', 'spatial', 'tdr_spatial'
    layers: 'counts_X', 'log1p_X', 'pearson_X', 'spliced', 'unspliced'

# embryo pc&mesh
# The embryo pc model and mesh model are reconstructed by ``2.three dims models reconstruction``
embryo_pc = st.tdr.read_model(filename="embryo_pc_model.vtk")
embryo_mesh = st.tdr.read_model(filename="embryo_mesh_model.vtk")
embryo_pc.point_data, embryo_mesh.cell_data

(pyvista DataSetAttributes
 Association     : POINT
 Active Scalars  : cell_size
 Active Vectors  : None
 Active Texture  : None
 Active Normals  : None
 Contains arrays :
     cell_size               float64    (25857,)             SCALARS
     tissue_rgba             float32    (25857, 4)
     tissue                  <U14       (25857,)
     obs_index               <U16       (25857,)
     cell_radius             float64    (25857,),
 pyvista DataSetAttributes
 Association     : CELL
 Active Scalars  : tissue_rgba
 Active Vectors  : None
 Active Texture  : None
 Active Normals  : None
 Contains arrays :
     tissue_rgba             float32    (39992, 4)           SCALARS
     vtkOriginalCellIds      int64      (39992,)
     tissue                  <U12       (39992,))

Customize cpo through an interactive window

cpo, _ = st.pl.three_d_plot(
    model=st.tdr.collect_models([embryo_pc, embryo_mesh]),
    key="tissue",
    model_style=["points", "surface"],
    jupyter=False,
    window_size=(512, 512),
)
cpo

[(553.2878243418567, 1098.4674808068507, 277.4399476053088),
 (1.9670869138005287, -6.902875264241757, -2.2120172004343885),
 (-0.16299443079060863, -0.16480753930466982, 0.9727647662819544)]

Extraction of subpopulation from embryon model

subpopulation = "CNS"
subpopulation_rpc = st.tdr.three_d_pick(model=embryo_pc, key="tissue", picked_groups=subpopulation)[0]
subpopulation_tpc = st.tdr.interactive_rectangle_clip(model=subpopulation_rpc, key="tissue", invert=True, bg_model=embryo_pc)[0]

WARNING:root:Encountered issue in callback (most recent call last):
  File "/home/yao/PythonProject/Yao_packages/spateo-release/spateo/tdr/widgets/clip.py", line 26, in _split_model
    if not original_model.n_cells:
AttributeError: 'NoneType' object has no attribute 'n_cells'
WARNING:root:Encountered issue in callback (most recent call last):
  File "/home/yao/PythonProject/Yao_packages/spateo-release/spateo/tdr/widgets/clip.py", line 26, in _split_model
    if not original_model.n_cells:
AttributeError: 'NoneType' object has no attribute 'n_cells'
WARNING:root:Encountered issue in callback (most recent call last):
  File "/home/yao/PythonProject/Yao_packages/spateo-release/spateo/tdr/widgets/clip.py", line 26, in _split_model
    if not original_model.n_cells:
AttributeError: 'NoneType' object has no attribute 'n_cells'

st.pl.three_d_plot(
    model=subpopulation_tpc,
    key="tissue",
    model_style="points",
    model_size=3,
    opacity=[1, 0.6, 1],
    jupyter="static",
    cpo=cpo,
    window_size=(512, 512),
)

3D reconstruction of subpopulation mesh model

subpopulation_color = mplc.to_hex(c=subpopulation_tpc["tissue_rgba"][0], keep_alpha=True)
subpopulation_mesh, subpopulation_pc = st.tdr.construct_surface(
    pc=subpopulation_tpc,
    key_added="tissue",
    label=subpopulation,
    color=subpopulation_color,
    alpha=1.0,
    cs_method="marching_cube",
    cs_args={"mc_scale_factor": 1.},
    smooth=3000,
    scale_factor=1
)
subpopulation_mesh.cell_data

pyvista DataSetAttributes
Association     : CELL
Active Scalars  : tissue_rgba
Active Vectors  : None
Active Texture  : None
Active Normals  : None
Contains arrays :
    vtkOriginalCellIds      int64      (39992,)
    tissue_rgba             float32    (39992, 4)           SCALARS
    tissue                  <U3        (39992,)

st.pl.three_d_plot(
    model=st.tdr.collect_models([subpopulation_pc, subpopulation_mesh]),
    key="tissue",
    model_style=["points", "surface"],
    model_size=5,
    opacity=[1, 0.5],
    jupyter="static",
    cpo=cpo,
)

st.pl.three_d_multi_plot(
    model=st.tdr.collect_models(
        [
            st.tdr.collect_models([embryo_mesh, subpopulation_pc]),
            st.tdr.collect_models([embryo_mesh, subpopulation_mesh]),
            st.tdr.collect_models([embryo_mesh, subpopulation_pc, subpopulation_mesh])
        ]
    ),
    key="tissue",
    model_style=[["surface", "points"], "surface", ["surface", "points", "surface"]],
    model_size=3,
    opacity=[1, 1, [1, 1, 0.3]],
    shape=(1, 3),
    window_size=(512*3, 512),
    jupyter="static",
    cpo=cpo,
)

Save models

st.tdr.save_model(model=subpopulation_pc, filename="subpopulatio_pc_model.vtk")
st.tdr.save_model(model=subpopulation_mesh, filename="subpopulatio_mesh_model.vtk")