run_CCST.py

##exocrine GCNG with normalized graph matrix 
import os
import sys
import matplotlib
matplotlib.use('Agg')
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
#import pylab as pl
#from mpl_toolkits.axes_grid1.inset_locator import inset_axes

from sklearn import metrics
from scipy import sparse
#from sklearn.metrics import roc_curve, auc, roc_auc_score

import numpy as np
import pickle
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, ChebConv, GATConv, DeepGraphInfomax, global_mean_pool, global_max_pool  # noqa
from torch_geometric.data import Data, DataLoader
from datetime import datetime 

rootPath = os.path.dirname(sys.path[0])
os.chdir(rootPath+'/CCST')


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    # ================Specify data type firstly===============
    parser.add_argument( '--data_type', default='nsc', help='"sc" or "nsc", \
        refers to single cell resolution datasets(e.g. MERFISH) and \
        non single cell resolution data(e.g. ST) respectively') 
    # =========================== args ===============================
    parser.add_argument( '--data_name', type=str, default='V1_Breast_Cancer_Block_A_Section_1', help="'MERFISH' or 'V1_Breast_Cancer_Block_A_Section_1") 
    parser.add_argument( '--lambda_I', type=float, default=0.3) #0.8 on MERFISH, 0.3 on ST
    parser.add_argument( '--data_path', type=str, default='generated_data/', help='data path')
    parser.add_argument( '--model_path', type=str, default='model') 
    parser.add_argument( '--embedding_data_path', type=str, default='Embedding_data') 
    parser.add_argument( '--result_path', type=str, default='results') 
    parser.add_argument( '--DGI', type=int, default=1, help='run Deep Graph Infomax(DGI) model, otherwise direct load embeddings')
    parser.add_argument( '--load', type=int, default=0, help='Load pretrained DGI model')
    parser.add_argument( '--num_epoch', type=int, default=5000, help='numebr of epoch in training DGI')
    parser.add_argument( '--hidden', type=int, default=256, help='hidden channels in DGI') 
    parser.add_argument( '--PCA', type=int, default=1, help='run PCA or not')   
    parser.add_argument( '--cluster', type=int, default=1, help='run cluster or not')
    parser.add_argument( '--n_clusters', type=int, default=5, help='number of clusters in Kmeans, when ground truth label is not avalible.') #5 on MERFISH, 20 on Breast
    parser.add_argument( '--draw_map', type=int, default=1, help='run drawing map')
    parser.add_argument( '--diff_gene', type=int, default=0, help='Run differential gene expression analysis')
    args = parser.parse_args() 

    args.embedding_data_path = args.embedding_data_path +'/'+ args.data_name +'/'
    args.model_path = args.model_path +'/'+ args.data_name +'/'
    args.result_path = args.result_path +'/'+ args.data_name +'/'
    if not os.path.exists(args.embedding_data_path):
        os.makedirs(args.embedding_data_path) 
    if not os.path.exists(args.model_path):
        os.makedirs(args.model_path) 
    args.result_path = args.result_path+'lambdaI'+str(args.lambda_I) +'/'
    if not os.path.exists(args.result_path):
        os.makedirs(args.result_path) 
    print ('------------------------Model and Training Details--------------------------')
    print(args) 

    if args.data_type == 'sc': # should input a single cell resolution dataset, e.g. MERFISH
        from CCST_merfish_utils import CCST_on_MERFISH
        CCST_on_MERFISH(args)
    elif args.data_type == 'nsc': # should input a non-single cell resolution dataset, e.g. V1_Breast_Cancer_Block_A_Section_1
        from CCST_ST_utils import CCST_on_ST
        CCST_on_ST(args)
    else:
        print('Data type not specified')