SPACEc:Distance permutation analysis

import spacec as sp

#import standard packages
import os
import pandas as pd
import scanpy as sc

# silencing warnings
import warnings
warnings.filterwarnings('ignore')

INFO:root: * TissUUmaps version: 3.1.1.6
/home/tim/miniforge3/envs/spacec/lib/python3.9/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: The TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.
  warnings.warn(problem)
/home/tim/miniforge3/envs/spacec/lib/python3.9/site-packages/cudf/utils/gpu_utils.py:89: UserWarning: A GPU with NVIDIA Volta™ (Compute Capability 7.0) or newer architecture is required.
Detected GPU 0: NVIDIA GeForce GTX 1070                                                                                                                                                                                               
Detected Compute Capability: 6.1
  warnings.warn(

# set plotting parameters
sc.settings.set_figure_params(dpi=80, facecolor='white')

# Specify the path to the data
root_path = "/home/user/path/SPACEc/" # inset your own path

data_path = root_path + 'example_data/raw/' # where the data is stored

# where you want to store the output
output_dir = root_path + 'example_data/output/'
os.makedirs(output_dir, exist_ok=True)

# Load data
adata = sc.read(output_dir + "adata_nn_demo_annotated_cn.h5ad")
adata

AnnData object with n_obs × n_vars = 46789 × 59
    obs: 'DAPI', 'x', 'y', 'area', 'region_num', 'unique_region', 'condition', 'leiden_1', 'leiden_1_subcluster', 'cell_type_coarse', 'cell_type_coarse_subcluster', 'cell_type_coarse_f', 'cell_type_coarse_f_subcluster', 'cell_type_coarse_f_f', 'cell_type', 'CN_k20_n40', 'CN_k20_n30', 'CN_k20_n20', 'CN_k20_n25', 'CN_k20_n6', 'CN_k20_n6_annot'
    uns: 'CN_k20_n6_colors', 'Centroid_k20_n20', 'Centroid_k20_n25', 'Centroid_k20_n30', 'Centroid_k20_n40', 'Centroid_k20_n6', 'cell_type_coarse_f_colors', 'cell_type_colors', 'dendrogram_cell_type_coarse_f_subcluster', 'leiden', 'leiden_1_colors', 'neighbors', 'umap', 'unique_region_colors'
    obsm: 'X_pca', 'X_umap'
    layers: 'scaled'
    obsp: 'connectivities', 'distances'

Identify potential interactions

# compute the potential interactions
distance_pvals = sp.tl.identify_interactions(
    adata = adata, # AnnData object
    cellid = "index", # column that contains the cell id (set index if the cell id is the index of the dataframe)
    x_pos = "x", # x coordinate column
    y_pos = "y", # y coordinate column
    cell_type = "cell_type", # column that contains the cell type information
    region = "unique_region", # column that contains the region information
    num_iterations=1000, # number of iterations for the permutation test
    num_cores=10,  # number of CPU threads to use
    min_observed = 10, # minimum number of observed interactions to consider a cell type pair
    comparison = 'condition', # column that contains the condition information we want to compare
    distance_threshold=20/0.5085) # distance threshold in px (20 µm)

index is not in the adata.obs, use index as cellid instead!
Computing for observed distances between cell types!
This function expects integer values for xy coordinates.
x and y will be changed to integer. Please check the generated output!

INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6

Save triangulation distances output to anndata.uns triDist
Permuting data labels to obtain the randomly distributed distances!
this step can take awhile

INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6
INFO:root: * TissUUmaps version: 3.1.1.6

Save iterative triangulation distance output to anndata.uns triDist

adata.uns['triDist']

	celltype1	celltype2	mean_dist	unique_region	iteration	condition
0	B cell	B cell	17.322904	reg002	1	tonsillitis
1	B cell	CD4+ T cell	18.485693	reg002	1	tonsillitis
2	B cell	CD8+ T cell	17.229160	reg002	1	tonsillitis
3	B cell	DC	17.252697	reg002	1	tonsillitis
4	B cell	Epithelial cell	17.441963	reg002	1	tonsillitis
...	...	...	...	...	...	...
482696	cDC1	Neutrophil	14.764823	reg001	1000	tonsil
482697	cDC1	Plasma cell	17.054808	reg001	1000	tonsil
482698	cDC1	Treg	16.820961	reg001	1000	tonsil
482699	cDC1	Vessel	16.668742	reg001	1000	tonsil
482700	cDC1	cDC1	17.044871	reg001	1000	tonsil

482701 rows × 6 columns

# save adata
adata.write(output_dir + "adata_nn_demo_annotated_cn.h5ad")

# Identify significant cell-cell interactions
# dist_table_filt is a simplified table used for plotting
# dist_data_filt contains the filtered raw data with more information about the pairs
#  The function outputs two dataframes:  and dist_data_filt that contains all filtered interactions and  dist_table_filt that contains a table for all interactions that show a significant value in both tissues
dist_table_filt, dist_data_filt = sp.tl.filter_interactions(
    distance_pvals = distance_pvals,
    pvalue = 0.05,
    logfold_group_abs = 0.1,
    comparison = 'condition')

print(dist_table_filt.shape)
dist_data_filt

(39, 2)

	celltype1	celltype2	condition	expected	expected_mean	keep_x	observed	observed_mean	keep_y	pvalue	logfold_group	interaction	logfold_group_abs	pairs
2	B cell	CD4+ T cell	tonsil	[17.281922614950506, 17.270623692108778, 17.28...	17.646184	True	[21.0, 14.7648230602334, 23.706539182259394, 2...	16.636922	True	1.317440e-46	-0.084968	B cell --> CD4+ T cell	0.084968	B cell_CD4+ T cell
3	B cell	CD4+ T cell	tonsillitis	[18.485692875309425, 17.542156808395966, 17.41...	17.656303	True	[28.231188426986208, 14.317821063276353, 27.29...	16.187740	True	8.446516e-67	-0.125282	B cell --> CD4+ T cell	0.125282	B cell_CD4+ T cell
8	B cell	Epithelial cell	tonsil	[17.128317857502207, 17.472384317332953, 17.42...	17.744415	True	[11.180339887498949, 32.0, 13.92838827718412, ...	17.731290	True	1.175790e-04	-0.001067	B cell --> Epithelial cell	0.001067	B cell_Epithelial cell
9	B cell	Epithelial cell	tonsillitis	[17.441963049826683, 17.462752937841383, 17.41...	17.726203	True	[32.984845004941285, 17.699649415384002, 27.99...	20.480112	True	2.999825e-08	0.208340	B cell --> Epithelial cell	0.208340	B cell_Epithelial cell
16	B cell	Mast cell	tonsil	[17.695167954526006, 18.58880119871868, 18.557...	17.461009	True	[15.033296378372908, 16.44427190999916, 18.357...	15.963641	True	1.669109e-03	-0.129347	B cell --> Mast cell	0.129347	B cell_Mast cell
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
469	Vessel	Neutrophil	tonsillitis	[16.0063444598664, 18.12053492002032, 15.15022...	17.369348	True	[22.135943621178654, 17.4928556845359, 14.1421...	18.568986	True	3.021850e-02	0.096351	Vessel --> Neutrophil	0.096351	Vessel_Neutrophil
474	Vessel	Treg	tonsil	[17.954103317276537, 17.15715454178462, 21.619...	17.721069	True	[15.524174696260024, 28.30194339616981, 26.076...	16.876453	True	7.598934e-06	-0.070454	Vessel --> Treg	0.070454	Vessel_Treg
475	Vessel	Treg	tonsillitis	[17.087549869721986, 19.013220946273126, 16.84...	17.743971	True	[26.832815729997478, 11.661903789690601, 17.20...	16.225218	True	2.884915e-10	-0.129091	Vessel --> Treg	0.129091	Vessel_Treg
506	cDC1	Treg	tonsil	[17.032356746663876, 17.110729499765213, 16.99...	17.483293	True	[20.024984394500787, 13.341664064126334, 18.13...	17.240494	True	4.860705e-02	-0.020176	cDC1 --> Treg	0.020176	cDC1_Treg
507	cDC1	Treg	tonsillitis	[16.68538841133706, 17.25883307347274, 20.6700...	17.504357	True	[15.033296378372908, 11.180339887498949, 19.69...	16.065805	True	5.797637e-03	-0.123721	cDC1 --> Treg	0.123721	cDC1_Treg

88 rows × 14 columns

Visualization

sp.pl.plot_top_n_distances(
    dist_table_filt,
    dist_data_filt,
    n=5,
    colors=None,
    dodge=False,
    savefig=False,
    output_fname="",
    output_dir="./",
    figsize=(5, 5),
    unit="px",
    errorbars=True,
)

condition                      tonsil  tonsillitis  abs_dist
pairs                                                       
Mast cell_M1 Macrophage     -0.111741     0.306462  0.418203
CD8+ T cell_Epithelial cell -0.173932     0.148640  0.322572
Epithelial cell_CD8+ T cell -0.127416     0.107586  0.235002
B cell_Epithelial cell      -0.001067     0.208340  0.209407
M2 Macrophage_Mast cell      0.050296    -0.153881  0.204177

sp.pl.dumbbell(data = dist_table_filt, figsize=(8,12), colors = ['#DB444B', '#006BA2'])

sp.pl.distance_graph(dist_table = dist_data_filt, # the (filtered) distance data table you want to plot 
                  distance_pvals = distance_pvals, # the full distance data table
                  condition_pair=['tonsil', 'tonsillitis'],
                  node_size=1600, font_size=6,
                  palette=None,
                  dpi = 600,
                  savefig=False,
                  output_fname="",
                  output_dir=output_dir,)

(88, 14)