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')
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_excel from `anndata` is deprecated. Import anndata.io.read_excel instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_hdf from `anndata` is deprecated. Import anndata.io.read_hdf instead.
warnings.warn(msg, FutureWarning)
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_umi_tools from `anndata` is deprecated. Import anndata.io.read_umi_tools instead.
warnings.warn(msg, FutureWarning)
2024-11-24 21:33:36.026147: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2024-11-24 21:33:36.026168: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
INFO:root: * TissUUmaps version: 3.1.1.6
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_geometric/typing.py:68: UserWarning: An issue occurred while importing 'pyg-lib'. Disabling its usage. Stacktrace: /home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/libpyg.so: undefined symbol: _ZN3c1010Dispatcher17runRecordFunctionERN2at14RecordFunctionESt17reference_wrapperIKNS_14FunctionSchemaEENS_11DispatchKeyE
warnings.warn(f"An issue occurred while importing 'pyg-lib'. "
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_geometric/typing.py:86: UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: /home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_scatter/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
warnings.warn(f"An issue occurred while importing 'torch-scatter'. "
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_geometric/typing.py:97: UserWarning: An issue occurred while importing 'torch-cluster'. Disabling its usage. Stacktrace: /home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
warnings.warn(f"An issue occurred while importing 'torch-cluster'. "
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_geometric/typing.py:113: UserWarning: An issue occurred while importing 'torch-spline-conv'. Disabling its usage. Stacktrace: /home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_spline_conv/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
warnings.warn(
/home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_geometric/typing.py:124: UserWarning: An issue occurred while importing 'torch-sparse'. Disabling its usage. Stacktrace: /home/tim/miniforge3/envs/spacec/lib/python3.10/site-packages/torch_sparse/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
warnings.warn(f"An issue occurred while importing 'torch-sparse'. "
# 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, results_dict = 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=12, # 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)
# the results_dict contains the results of the permutation test as well as the observed and shuffled distances
results_dict.keys()
dict_keys(['distance_pvals', 'triangulation_distances_observed', 'triangulation_distances_iterated'])
# the distance_pvals contains the p-values for each cell type pair and is automatically added to the adata.uns
adata.uns['triDist']
| unique_region | celltype1_index | celltype1 | celltype1_X | celltype1_Y | celltype2_index | celltype2 | celltype2_X | celltype2_Y | distance | condition | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | reg002 | 3675 | CD4+ T cell | 612 | 1209 | 3752 | Plasma cell | 620 | 1220 | 13.601471 | tonsillitis |
| 1 | reg002 | 15372 | CD8+ T cell | 1691 | 310 | 15416 | Plasma cell | 1697 | 296 | 15.231546 | tonsillitis |
| 2 | reg002 | 6507 | CD8+ T cell | 897 | 651 | 6508 | CD4+ T cell | 897 | 672 | 21.000000 | tonsillitis |
| 3 | reg002 | 7976 | GCB | 1033 | 1410 | 8070 | GCB | 1043 | 1425 | 18.027756 | tonsillitis |
| 4 | reg002 | 22014 | CD4+ T cell | 2435 | 1409 | 22050 | B cell | 2444 | 1419 | 13.453624 | tonsillitis |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 79446 | reg001 | 49351 | B cell | 2835 | 847 | 49307 | Epithelial cell | 2841 | 871 | 24.738634 | tonsil |
| 79447 | reg001 | 46044 | Epithelial cell | 2457 | 425 | 46027 | Epithelial cell | 2459 | 448 | 23.086793 | tonsil |
| 79448 | reg001 | 47417 | M2 Macrophage | 2598 | 2274 | 47298 | Mast cell | 2609 | 2290 | 19.416488 | tonsil |
| 79449 | reg001 | 43846 | CD4+ T cell | 2234 | 1393 | 43625 | cDC1 | 2252 | 1390 | 18.248288 | tonsil |
| 79450 | reg001 | 30272 | CD8+ T cell | 1124 | 230 | 30103 | Vessel | 1140 | 231 | 16.031220 | tonsil |
280510 rows × 11 columns
# save adata
adata.write(output_dir + "adata_nn_demo_annotated_cn.h5ad")
Filter for most significant results
In this example the results are filtered twice. First to remove rare cell types from the analysis because they are overrepresented when comparing distances, and then we filter on statistical significance as well as absolute log fold change.
distance_pvals_filt = sp.tl.remove_rare_cell_types(adata,
distance_pvals,
cell_type_column="cell_type",
min_cell_type_percentage=1)
Cell types that belong to less than 1% of total cells:
['Mast cell', 'NK cell', 'Neutrophil']
Categories (15, object): ['B cell', 'CD4+ T cell', 'CD8+ T cell', 'DC', ..., 'Plasma cell', 'Treg', 'Vessel', 'cDC1']
# 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_filt,
pvalue = 0.05,
logfold_group_abs = 0.1,
comparison = 'condition')
print(dist_table_filt.shape)
dist_data_filt
(30, 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 | [17.44611626424993, 17.240214223537937, 17.384... | 17.508022 | True | [21.0, 14.7648230602334, 23.706539182259394, 2... | 16.568973 | True | 2.649916e-54 | -0.079532 | B cell --> CD4+ T cell | 0.079532 | B cell_CD4+ T cell |
| 3 | B cell | CD4+ T cell | tonsillitis | [17.991364990576724, 17.796405842536796, 17.37... | 17.596348 | True | [22.255004511542957, 14.317821063276353, 27.29... | 16.143926 | True | 1.783677e-80 | -0.124285 | B cell --> CD4+ T cell | 0.124285 | B cell_CD4+ T cell |
| 4 | B cell | CD8+ T cell | tonsil | [17.578718052603264, 17.373822784572745, 17.52... | 17.668738 | True | [14.142135623730951, 15.908466048591219, 13.60... | 16.833803 | True | 1.343202e-32 | -0.069838 | B cell --> CD8+ T cell | 0.069838 | B cell_CD8+ T cell |
| 5 | B cell | CD8+ T cell | tonsillitis | [17.220703294913235, 17.586321595513557, 17.69... | 17.679001 | True | [13.92838827718412, 12.0, 22.47220505424423, 1... | 16.493192 | True | 4.567463e-59 | -0.100166 | B cell --> CD8+ T cell | 0.100166 | B cell_CD8+ T cell |
| 8 | B cell | Epithelial cell | tonsil | [17.112417871305603, 17.32405435992097, 17.480... | 17.645577 | True | [25.495097567963924, 11.180339887498949, 32.0,... | 17.582085 | True | 8.317085e-06 | -0.005200 | B cell --> Epithelial cell | 0.005200 | B cell_Epithelial cell |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 399 | Vessel | Epithelial cell | tonsillitis | [18.656136722237417, 17.23539386972536, 17.452... | 17.697952 | True | [20.27019348662979, 25.553864678361276, 20.538... | 19.468968 | True | 4.654472e-02 | 0.137594 | Vessel --> Epithelial cell | 0.137594 | Vessel_Epithelial cell |
| 400 | Vessel | GCB | tonsil | [17.123726173924652, 17.86949875671633, 17.259... | 17.727516 | True | [23.219544457292887, 22.561028345356956, 31.57... | 22.337618 | True | 1.868711e-05 | 0.333485 | Vessel --> GCB | 0.333485 | Vessel_GCB |
| 401 | Vessel | GCB | tonsillitis | [17.73771013605273, 18.023692483603742, 18.473... | 17.674742 | True | [27.730849247724095, 18.679380239274902, 13.34... | 19.167979 | True | 3.128661e-01 | 0.117009 | Vessel --> GCB | 0.117009 | Vessel_GCB |
| 428 | cDC1 | Epithelial cell | tonsil | [18.463345515951513, 18.99715862104245, 17.564... | 17.649416 | True | [19.235384061671343, 20.518284528683193, 21.93... | 19.038398 | True | 6.141596e-03 | 0.109292 | cDC1 --> Epithelial cell | 0.109292 | cDC1_Epithelial cell |
| 429 | cDC1 | Epithelial cell | tonsillitis | [17.422664720988926, 17.507966079507376, 17.26... | 17.610267 | True | [15.639410298049853, 15.71062198086001, 23.600... | 18.774279 | True | 5.173701e-02 | 0.092341 | cDC1 --> Epithelial cell | 0.092341 | cDC1_Epithelial cell |
64 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
CD8+ T cell_Epithelial cell -0.131908 0.124787 0.256695
Epithelial cell_CD8+ T cell -0.143348 0.104529 0.247876
Vessel_GCB 0.333485 0.117009 0.216476
GCB_Vessel 0.332078 0.131780 0.200298
B cell_Epithelial cell -0.005200 0.194998 0.200199
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,)
(64, 13)
