SY日记方法分享--高精度空转（Xenium、CosMx）距离度量

import spacec as sp

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

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

# 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("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'

# 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']

distance_pvals_filt = sp.tl.remove_rare_cell_types(adata, 
                       distance_pvals, 
                       cell_type_column="cell_type", 
                       min_cell_type_percentage=1)

# 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

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,
)

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,)