Contents

Spatial Analysis

SpatialTis provides a series of Spatial Anlaysis methods:

  • Find cell neighbors

  • Neighbor dependent markers

Cell type based analysis:

  • Spatial Distribution*

  • Spatial Heterogenenity*

  • Hotspot Detection*

  • Neighborhood Analyisis (Cell-Cell Interaction)

* No need to compute neighbors information

Markers based analysis:

  • Spatial Enrichment Analysis (Markers spatial enrichemnt)

  • Spatial Co-expression

  • Spatial community detection

[1]:

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

[2]:

import anndata as ad

data = ad.read_h5ad("../data/imc_data.h5ad")
data

[2]:

AnnData object with n_obs × n_vars = 1776974 × 38
    obs: 'area', 'eccentricity', 'islet_id', 'centroid', 'image', 'case', 'slide', 'part', 'group', 'stage', 'cell_cat', 'cell_type'
    var: 'markers'

This is a huge dataset, almost 1.8 millions of cell

[3]:

import spatialtis as st
import spatialtis.plotting as sp
from spatialtis import CONFIG

CONFIG.EXP_OBS = ["stage", "case", "part", "image"]
CONFIG.CELL_TYPE_KEY = "cell_type"
CONFIG.MARKER_KEY = "markers"
CONFIG.CENTROID_KEY = "centroid"

Since this is a diabetic data, we mainly focus on islets cells. If you are interesated in the immune part, feel free to look into it yourself.

[4]:

islets_cells = ['gamma', 'delta', 'alpha', 'beta']
group_order={"stage":["Non-diabetic", "Onset", "Long-duration"]}

Spatial Distribution

There are three type of distribution pattern (0 if no cells)

  • Random (1)

  • Regular (2)

  • Cluster (3)

Three methods are provided

  • Variance-to-mean ratio (vmr): Index of Dispersion

  • Quadratic statistics (quad): Morisita’s index of dispersion

  • Nearest neighbors search (nns): Clark and Evans aggregation index

Two visualizations:

  • Dot plot

  • Heatmap

[5]:

st.spatial_distribution(data)

⏳ Spatial Distribution

🛠 Method: Nearest neighbors search

Finding distribution pattern ██████████ 100% 00:00|00:13

📦 Added to AnnData, uns: 'spatial_distribution'

⏱ 58s905ms

[5]:



[6]:

sp.spatial_distribution(data)
../_images/tutorial_3-spatial_analysis_cell_type_9_0.png 
[6]:

Or you could visualize it in heatmap

[7]:

sp.spatial_distribution(data, groupby=['stage'], use="heatmap", clustermap_kwargs=dict(row_cluster=True))
../_images/tutorial_3-spatial_analysis_cell_type_11_0.png 
[7]:

Spatial Heterogeneity

Used to evaluate the tissue heterogeneity

Three methods are provided:

  • Shannon entropy (No spatial information)

  • Leibovici entropy (Spatial entropy)

  • Altieri entropy (Spatial entropy)

The number of three species are the same in three figures, You can see that shannon entropy remain the same. Only spatial entropy is changing.

entropy-diff

[8]:

st.spatial_heterogeneity(data)

⏳ Spatial Heterogeneity

🛠 Method: Leibovici entropy

(pid=2801) Error in atexit._run_exitfuncs:
(pid=2801) Traceback (most recent call last):
(pid=2801)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2801)     self._writeout_input_cache(conn)
(pid=2801)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2801)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2801) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145521209344 and this is thread id 4697800192.
(pid=2790) Error in atexit._run_exitfuncs:
(pid=2790) Traceback (most recent call last):
(pid=2790)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2790)     self._writeout_input_cache(conn)
(pid=2790)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2790)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2790) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145471897600 and this is thread id 4530720256.
(pid=2800) Error in atexit._run_exitfuncs:
(pid=2800) Traceback (most recent call last):
(pid=2800)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2800)     self._writeout_input_cache(conn)
(pid=2800)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2800)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2800) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145565806592 and this is thread id 4557991424.
(pid=2791) Error in atexit._run_exitfuncs:
(pid=2791) Traceback (most recent call last):
(pid=2791)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2791)     self._writeout_input_cache(conn)
(pid=2791)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2791)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2791) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145411981312 and this is thread id 4718685696.
(pid=2796) Error in atexit._run_exitfuncs:
(pid=2796) Traceback (most recent call last):
(pid=2796)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2796)     self._writeout_input_cache(conn)
(pid=2796)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2796)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2796) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145353105408 and this is thread id 4552564224.
(pid=2787) Error in atexit._run_exitfuncs:
(pid=2787) Traceback (most recent call last):
(pid=2787)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2787)     self._writeout_input_cache(conn)
(pid=2787)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2787)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2787) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145352572928 and this is thread id 4407406080.
(pid=2789) Error in atexit._run_exitfuncs:
(pid=2789) Traceback (most recent call last):
(pid=2789)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 780, in writeout_cache
(pid=2789)     self._writeout_input_cache(conn)
(pid=2789)   File "/Users/milk/anaconda3/envs/spatialtis/lib/python3.8/site-packages/IPython/core/history.py", line 763, in _writeout_input_cache
(pid=2789)     conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(pid=2789) sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 123145462366208 and this is thread id 4593577472.

Calculating heterogeneity ██████████ 100% 00:00|00:01

📦 Added to AnnData, uns: 'spatial_heterogeneity'

⏱ 44s234ms

[8]:



[9]:

sp.spatial_heterogeneity(data, ['stage'], size=(4, 4),
                         group_order=group_order,
                         title="Tissue heterogeneity in diabetic stages",
                        yaxis_title="Heterogeneity")
../_images/tutorial_3-spatial_analysis_cell_type_14_0.png 
[9]:

If we look at cell components, we could see that the cell type are more divese in Onset stage

[10]:

st.cell_components(data)

⏳ Cell Components

📦 Added to AnnData, uns: 'cell_components'

⏱ 524ms

[10]:



[11]:

cells = ['Tc', 'Th', 'naiveTc', 'macrophage', 'neutrophil', 'otherimmune', 'alpha', 'beta', 'gamma', 'delta']
sp.cell_components(data, ['stage'], agg="mean", percentage=False, selected_types=cells, group_order=group_order, size=(3,3.5),
                  title="Immune cells in Diabetes Stage")
../_images/tutorial_3-spatial_analysis_cell_type_17_0.png 
[11]:

Hotspot detection

Spatial distribution tells you if a cell is clustered, hotspot detection tells you which cells are clustered.

[12]:

st.hotspot(data, selected_types=islets_cells, grid_size=50, search_level=2)

⏳ Hotspot Analysis

Hotspot analysis ██████████ 100% 00:00|00:00

📦 Added to AnnData, obs: 'hotspot'

⏱ 6s396ms

[12]:



[13]:

st.hotspot(data, selected_types=['alpha'], grid_size=15, search_level=2)

⏳ Hotspot Analysis

Hotspot analysis ██████████ 100% 00:00|00:00

📦 Added to AnnData, obs: 'hotspot'

⏱ 6s955ms

[13]:



[14]:

ROI = {"stage": "Onset", "image": "A01"}
sp.cell_map(data, ROI, cell_type_key="hotspot", selected_types=['cold', 'hot'], palette="Paired")
sp.cell_map(data, ROI, selected_types=islets_cells)
../_images/tutorial_3-spatial_analysis_cell_type_21_0.png
../_images/tutorial_3-spatial_analysis_cell_type_21_1.png 
[14]:

Find cell neighbors

Every analysis below will rely on this step. This is a critical step.

Two methods are provided:

  • KD-tree: If your cells are points

  • R-tree: If your cells are polygons

This method is implemented in Rust, it’s much faster even that scipy’s implementation of KD-tree in C

This dataset only contains points information.

[15]:

st.find_neighbors(data, expand=8)

⏳ Find Cell Neighbors

🛠 Method: KD-tree

Find neighbors ██████████ 100% 00:00|00:14

📦 Added to AnnData, obs: 'cell_neighbors'

📦 Added to AnnData, obs: 'cell_neighbors_count'

⏱ 17s907ms

[15]:

Neighborhood Analysis

To profile the cell-cell interaction

This method is also implemented in Rust, see how fast it is.

Three viusalizations:

  • Dot matrix plot

  • Heatmap

  • Graph (static / interactive)

[16]:

st.neighborhood_analysis(data)

⏳ Neighborhood Analysis

🛠 Method: pseudo p-value

Neighborhood analysis ██████████ 100% 00:00|00:28

📦 Added to AnnData, uns: 'neighborhood_analysis'

⏱ 30s493ms

[16]:



[17]:

sp.neighborhood_analysis(data)
../_images/tutorial_3-spatial_analysis_cell_type_27_0.png 
[17]:



[18]:

cell_type_order = ['alpha', 'beta', 'delta', 'gamma', 'Tc', 'naiveTc', 'Th', 'B', 'macrophage', 'neutrophil',
                  'otherimmune', 'acinar', 'ductal', 'endothelial', 'stromal', 'unknown']
sp.neighborhood_analysis(data, selected_types=cell_type_order, use="heatmap",
                         clustermap_kwargs=dict(row_cluster=False, col_cluster=False, method="ward"))
../_images/tutorial_3-spatial_analysis_cell_type_28_0.png 
[18]:
Basic Analysis in SptialTis Spatial Analysis