ADEPT tutorial
0. import packages and select GPU if accessible
[1]:
import warnings
import sys
sys.path.append('../')
import pandas as pd
import numpy as np
import scanpy as sc
from scipy import sparse
import os
# from imputation.impute import impute_
import GAAE
import argparse
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
import time
import seaborn as sns
from GAAE.utils import impute, impute_, DE_num_calc, initialize, filter_num_calc, downstream_analyses
warnings.filterwarnings("ignore")
[ ]:
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default="./", help="root dir for input data")
parser.add_argument('--gt_dir', type=str, default="./", help="root dir for data ground truth")
parser.add_argument('--input_data', type=str, default="151673", help="input data section id")
parser.add_argument('--impute_cluster_num', type=str, default="7", help="diff cluster numbers for imputation")
parser.add_argument('--cluster_num', type=int, default=7, help="input data cluster number")
parser.add_argument('--radius', type=int, default=150, help="input data radius")
parser.add_argument("--de_candidates", type=str, default="200", help="candidate de list for imputation, separated by comma")
parser.add_argument('--no_de', type=int, default=0, help='switch on/off DEG selection module')
parser.add_argument("--use_mean", type=int, default=0, help="use mean value in de list or not")
parser.add_argument("--impute_runs", type=int, default=2, help="time of runs for imputation")
parser.add_argument("--runs", type=int, default=20, help="total runs for the data")
parser.add_argument('--gt', type=int, default=1, help="ground truth for the input data")
parser.add_argument('--use_hvgs', type=int, default=3000, help="select highly variable genes before training")
parser.add_argument('--use_preprocessing', type=int, default=1, help='use preprocessed input or raw input')
parser.add_argument('--save_fig', type=int, default=1, help='saving output visualization')
parser.add_argument('--filter_nzr', type=float, default=0.15, help='suggested nzr threshold for filtering')
parser.add_argument('--filter_num', type=int, default=None, help='suggested gene threshold for filtering')
parser.add_argument('--de_nzr_min', type=float, default=0.299, help='suggested min nzr threshold after de selection')
parser.add_argument('--de_nzr_max', type=float, default=0.399, help='suggested max nzr threshold after de selection')
parser.add_argument('--use_gpu_id', type=str, default='1', help='use which GPU, only applies when you have multiple gpu')
args = parser.parse_args()
args.impute_cluster_num = args.impute_cluster_num.split(",") # ["5", "6", "7"]
# device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
print('Using device: ' + args.use_gpu_id)
# params.device = device
iters=5
1. DLPFC dataset
set dir to ‘path/to/your/DLPFC/data’
[ ]:
"""DLPFC"""
# the number of clusters
setting_combinations = [[7, '151507'], [7, '151508'], [7, '151509'], [7, '151510'], [5, '151669'], [5, '151670'], [5, '151671'], [5, '151672'], [7, '151673'], [7, '151674'], [7, '151675'], [7, '151676']]
# setting_combinations = [[7, '151674'], [7, '151675'], [7, '151676']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/DLPFC12'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 150
args.use_preprocessing = 1
args.use_hvgs = 0
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/DLPFC' + dataset + '.txt'):
with open('./cache/DLPFC' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/DLPFC' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
for iter_ in range(iters):
de_list_epoch = []
adata_list = []
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
"""result of imputed data"""
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('DLPFC' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
2. BC/MA datasets
[ ]:
"""BC"""
# the number of clusters
setting_combinations = [[20, 'section1']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/BC'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 450
args.use_preprocessing = 1
args.use_hvgs = 0
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/BC' + dataset + '.txt'):
with open('./cache/BC' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/BC' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
for iter_ in range(iters):
de_list_epoch = []
adata_list = []
if de_top_k_list != []:
print("performing DEGs selection")
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
else:
print("skip performing DEGs selection")
imputed_ad = adata
"""result of imputed data"""
if de_top_k_list != []:
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
else:
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ARI, adata_out = GAAE.train_ADEPT(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('BC' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
[ ]:
"""MA"""
setting_combinations = [[52, 'MA']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/mMAMP'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 150
args.use_preprocessing = 1
args.use_hvgs = 0
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/MA' + dataset + '.txt'):
with open('./cache/MA' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/DLPFC' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
for iter_ in range(iters):
de_list_epoch = []
adata_list = []
if de_top_k_list != []:
print("performing DEGs selection")
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
else:
print("skip performing DEGs selection")
imputed_ad = adata
"""result of imputed data"""
if de_top_k_list != []:
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
else:
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ARI, adata_out = GAAE.train_ADEPT(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('mAB' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
3. mVC/mPFC datasets
[ ]:
"""mVC"""
setting_combinations = [[7, 'STARmap_20180505_BY3_1k.h5ad']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/STARmap_mouse_visual_cortex'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 450
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/DLPFC' + dataset + '.txt'):
with open('./cache/DLPFC' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/DLPFC' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
adata_list = []
for iter_ in range(iters):
de_list_epoch = []
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
"""result of imputed data"""
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('mVC ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
[ ]:
"""mPFC"""
# the number of clusters [4, '20180417_BZ5_control'], [4, '20180419_BZ9_control'],
setting_combinations = [[4, '20180424_BZ14_control']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/STARmap_mouse_PFC'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 450
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/mPFC' + dataset + '.txt'):
with open('./cache/mPFC' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/mPFC' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
adata_list = []
for iter_ in range(iters):
de_list_epoch = []
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
"""result of imputed data"""
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('mPFC' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
4. mHypothalamus dataset
[ ]:
"""mHypo"""
setting_combinations = [[8, '-0.04'], [8, '-0.09'], [8, '-0.14'], [8, '-0.19'], [8, '-0.24']]
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/mHypothalamus'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 150
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/mHypo' + dataset + '.txt'):
with open('./cache/mHypo' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/mHypo' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
adata_list = []
for iter_ in range(iters):
if de_top_k_list != []:
print("performing DEGs selection")
adata_list = []
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
else:
print("skip performing DEGs selection")
imputed_ad = adata
"""result of imputed data"""
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
print(aris)
with open('adept_aris.txt', 'a+') as fp:
fp.write('mHypothalamus' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')
5. Her2Tumor dataset
[ ]:
"""Her2st"""
setting_combinations = [[6, 'A1'], [5, 'B1'], [4, 'C1'], [4, 'D1'], [4, 'E1'], [4, 'F1'], [7, 'G2'], [7, 'H1']]
#
for setting_combi in setting_combinations:
args.data_dir = '/home/yunfei/spatial_benchmarking/benchmarking_data/Her2_tumor'
args.de_candidates = "None"
dataset = args.input_data = setting_combi[1]
args.cluster_num = setting_combi[0]
args.impute_cluster_num = [setting_combi[0]]
args.radius = 200
args.use_preprocessing = 1
args.use_hvgs = 0
aris = []
if args.input_data not in ['20180417_BZ5_control', '20180419_BZ9_control', '20180424_BZ14_control', 'STARmap_20180505_BY3_1k.h5ad'] :
filter_num = filter_num_calc(args, args.filter_num)
print("optimized filter number = ", filter_num)
else:
filter_num = 0
adata, adata_ori = initialize(args, filter_num)
if args.de_candidates == "None":
if os.path.exists('./cache/Her2st' + dataset + '.txt'):
with open('./cache/Her2st' + dataset + '.txt', 'r') as fp:
line = fp.readlines()[0]
split_ = line.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("previously cached de list = ", de_top_k_list)
else:
de_top_k_list = DE_num_calc(args, adata)
print("optimized de list = ", de_top_k_list)
with open('./cache/Her2st' + dataset + '.txt', 'a+') as fp:
# fp.write('de list: ')
fp.write(','.join([str(i) for i in de_top_k_list]))
# fp.write('\n')
else:
split_ = args.de_candidates.strip().split(",")
de_top_k_list = []
for e in split_:
de_top_k_list.append(int(e))
print("manually defined de list = ", de_top_k_list)
for iter_ in range(iters):
de_list_epoch = []
if de_top_k_list != []:
print("performing DEGs selection")
adata_list = []
for de_ in de_top_k_list:
for cluster_n in args.impute_cluster_num:
print("cluster_n = ", cluster_n)
GAAE.get_kNN(adata, rad_cutoff=args.radius)
ari_ini, ari_final, de_list, adata_out = GAAE.train_ADEPT_use_DE(adata, n_epochs=1000,
num_cluster=int(cluster_n),
dif_k=de_, device_id=args.use_gpu_id)
de_list_epoch.append(de_list)
adata_list.append(adata_out)
g_union = set.union(*de_list_epoch)
imputed_ad = impute(args, adata_list, g_union, de_top_k_list)
else:
print("skip performing DEGs selection")
imputed_ad = adata
"""result of imputed data"""
GAAE.get_kNN(imputed_ad, rad_cutoff=args.radius)
ari_ini, ARI, de_list, adata_out = GAAE.train_ADEPT_use_DE(imputed_ad, n_epochs=1000, num_cluster=args.cluster_num, device_id=args.use_gpu_id)
print('Dataset:', dataset)
print('ARI:', ARI)
aris.append(ARI)
print(aris)
print('Dataset:', dataset)
print(aris)
print(np.mean(aris))
with open('adept_aris.txt', 'a+') as fp:
fp.write('Her2tumor' + dataset + ' ')
fp.write(' '.join([str(i) for i in aris]))
fp.write('\n')