Step0: KNN-graph construction

from sklearn.neighbors import kneighbors_graph
import numpy as np
import pandas as pd
import datetime


# Hyperparameters
InputFolderName = "./MIBI_TNBC_KNNgraph_Input/"
KNN_K = 72

# Import image name list.
Region_filename = InputFolderName + "ImageNameList.txt"
region_name_list = pd.read_csv(
        Region_filename,
        sep="\t",  # tab-separated
        header=None,  # no heading row
        names=["Image"],  # set our own names for the columns
    )

for graph_index in range(0, len(region_name_list)):

    print(graph_index)
    # Import target graph x/y coordinates.
    region_name = region_name_list.Image[graph_index]
    GraphCoord_filename = InputFolderName + region_name + "_Coordinates.txt"
    x_y_coordinates = np.loadtxt(GraphCoord_filename, dtype='float', delimiter="\t")

    K = KNN_K
    print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
    print('Constructing KNN graph...')
    KNNgraph_sparse = kneighbors_graph(x_y_coordinates, K, mode='connectivity', include_self=False, n_jobs=-1)  #should NOT include itself as a nearest neighbor. Checked. "-1" means using all available cores.
    print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
    KNNgraph_AdjMat = KNNgraph_sparse.toarray()
    # Make the graph to undirected.
    print('Making KNN graph bidirectional...')
    KNNgraph_AdjMat_fix = KNNgraph_AdjMat + KNNgraph_AdjMat.T  #2min and cost one hundred memory.
    # Extract and write the edge index of the undirected graph.
    KNNgraph_EdgeIndex = np.argwhere(KNNgraph_AdjMat_fix > 0)  #1min
    filename0 = InputFolderName + region_name + "_EdgeIndex.txt"
    np.savetxt(filename0, KNNgraph_EdgeIndex, delimiter='\t', fmt='%i')  #save as integers. Checked the bidirectional edges.
    print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))

Hyperparameters

• KNN_K: The K value in the KNN gragh.

Input

The example input data of this part is a KNN graph constructed based on MIBI-TNBC data, including a image name list and cell type label, cell spatial coordinate, edge index, gragh index, gragh label and node attribute files. Here we take patient1 for an example as follows:

── MIBI-TNBC data
   ├─ patient1_CellTypeLabel.txt
   ├─ patient1_Coordinates.txt
   ├─ patient1_GraghIndex.txt
   ├─ patient1_GraghLabel.txt
   └─ patient1_NodeAttr.txt

Output

We use Step0 to construct KNN graghs and prepare data for the subsequent steps, and this step produces “EdgeIndex.txt” file for each input image.

── MIBI-TNBC data
   ├─ patient1_CellTypeLabel.txt
   ├─ patient1_Coordinates.txt
   ├─ patient1_GraghIndex.txt
   ├─ patient1_GraghLabel.txt
   ├─ patient1_NodeAttr.txt
   └─ patient1_EdgeIndex.txt