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run_CCC_RGCN.py
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run_CCC_RGCN.py
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##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( '--meu', type=float, default=0.2)
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=0, help='run drawing map')
parser.add_argument( '--diff_gene', type=int, default=0, help='Run differential gene expression analysis')
parser.add_argument( '--cluster_alg', type=str, default='kmeans' , help='Run which clustering at the end')
parser.add_argument( '--all_distance', type=int, default=0, help='Run differential gene expression analysis')
parser.add_argument( '--retrain', type=int, default=0 , help='Run which clustering at the end')
parser.add_argument( '--model_load_path', type=str, default='model')
parser.add_argument( '--model_name', type=str, default='r1')
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 +'/'
args.model_load_path = args.model_load_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+'/'
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')