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Epi-GraphReg_generalizable_test_multiple_runs.py
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Epi-GraphReg_generalizable_test_multiple_runs.py
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from __future__ import division
import sys
sys.path.insert(0,'../train')
from tensorflow.keras.layers import Input, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.regularizers import l2
from gat_layer import GraphAttention
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from tensorflow import keras
import matplotlib.pyplot as plt
import time
from scipy.stats import spearmanr
from scipy.stats import wilcoxon
from scipy.stats import ranksums
from tensorflow.keras import regularizers
import pyBigWig
from adjustText import adjust_text
import matplotlib.patches as mpatches
import mpl_scatter_density # adds projection='scatter_density'
from matplotlib.colors import LinearSegmentedColormap
from statannot import add_stat_annotation
##### Input
data_path = '/media/labuser/STORAGE/GraphReg' # data path
def poisson_loss_individual(y_true, mu_pred):
nll = tf.math.lgamma(y_true + 1) + mu_pred - y_true * tf.math.log(mu_pred)
return nll
def poisson_loss(y_true, mu_pred):
nll = tf.reduce_mean(tf.math.lgamma(y_true + 1) + mu_pred - y_true * tf.math.log(mu_pred))
return nll
def parse_proto(example_protos):
features = {
'last_batch': tf.io.FixedLenFeature([1], tf.int64),
'adj': tf.io.FixedLenFeature([], tf.string),
#'adj_real': tf.io.FixedLenFeature([], tf.string),
'tss_idx': tf.io.FixedLenFeature([], tf.string),
'X_1d': tf.io.FixedLenFeature([], tf.string),
'Y': tf.io.FixedLenFeature([], tf.string),
'bin_idx': tf.io.FixedLenFeature([], tf.string)
}
parsed_features = tf.io.parse_example(example_protos, features=features)
last_batch = parsed_features['last_batch']
adj = tf.io.decode_raw(parsed_features['adj'], tf.float16)
adj = tf.cast(adj, tf.float32)
#adj_real = tf.io.decode_raw(parsed_features['adj_real'], tf.float16)
#adj_real = tf.cast(adj_real, tf.float32)
tss_idx = tf.io.decode_raw(parsed_features['tss_idx'], tf.float16)
tss_idx = tf.cast(tss_idx, tf.float32)
X_epi = tf.io.decode_raw(parsed_features['X_1d'], tf.float16)
X_epi = tf.cast(X_epi, tf.float32)
Y = tf.io.decode_raw(parsed_features['Y'], tf.float16)
Y = tf.cast(Y, tf.float32)
bin_idx = tf.io.decode_raw(parsed_features['bin_idx'], tf.int64)
bin_idx = tf.cast(bin_idx, tf.int64)
return {'last_batch': last_batch, 'X_epi': X_epi, 'Y': Y, 'adj': adj, 'tss_idx': tss_idx, 'bin_idx': bin_idx}
def file_to_records(filename):
return tf.data.TFRecordDataset(filename, compression_type='ZLIB')
def dataset_iterator(file_name, batch_size):
dataset = tf.data.Dataset.list_files(file_name)
dataset = dataset.flat_map(file_to_records)
dataset = dataset.batch(batch_size)
dataset = dataset.map(parse_proto)
iterator = dataset.make_one_shot_iterator()
return iterator
def read_tf_record_1shot(iterator):
try:
next_datum = iterator.get_next()
data_exist = True
except tf.errors.OutOfRangeError:
data_exist = False
if data_exist:
T = 400
b = 50
F = 3
X_epi = next_datum['X_epi']
batch_size = tf.shape(X_epi)[0]
X_epi = tf.reshape(X_epi, [batch_size, 3*T*b, F])
adj = next_datum['adj']
adj = tf.reshape(adj, [batch_size, 3*T, 3*T])
last_batch = next_datum['last_batch']
tss_idx = next_datum['tss_idx']
tss_idx = tf.reshape(tss_idx, [3*T])
bin_idx = next_datum['bin_idx']
bin_idx = tf.reshape(bin_idx, [3*T])
if last_batch==0:
idx = tf.range(T, 2*T)
else:
idx = tf.range(T, 3*T)
#bin_idx = tf.gather(bin_idx, idx)
num_zero = np.sum(bin_idx.numpy()==0)
if (num_zero == T+T//2+1 and bin_idx[0] == 0):
start = bin_idx[T+T//2].numpy()
end = bin_idx[-1].numpy()+5000
pos1 = np.arange(start, end, 100).astype(int)
pad = -np.flip(np.arange(100, 3000000+100, 100).astype(int)) # -3000000, -2999900, ... , -200, -100
pos = np.append(pad, pos1).astype(int)
elif (num_zero == T//2+1 and bin_idx[0] == 0):
start = bin_idx[T//2].numpy()
end = bin_idx[-1].numpy()+5000
pos1 = np.arange(start, end, 100).astype(int)
pad = -np.flip(np.arange(100, 1000000+100, 100).astype(int)) # -1000000, -999900, ... , -200, -100
pos = np.append(pad, pos1).astype(int)
elif bin_idx[-1] == 0:
start = bin_idx[0].numpy()
i0 = np.where(bin_idx.numpy()==0)[0][0]
end = bin_idx[i0-1].numpy()+5000
pos1 = np.arange(start, end, 100).astype(int)
l = 60000 - len(pos1)
pad = 10**15 * np.ones(l)
pos = np.append(pos1, pad).astype(int)
else:
start = bin_idx[0].numpy()
end = bin_idx[-1].numpy()+5000
pos = np.arange(start, end, 100).astype(int)
Y = next_datum['Y']
#Y = tf.reshape(Y, [batch_size, 3*T, b])
#Y = tf.reduce_sum(Y, axis=2)
Y = tf.reshape(Y, [batch_size, 3*T])
else:
X_epi = 0
Y = 0
adj = 0
tss_idx = 0
idx = 0
pos = 0
last_batch = 10
return data_exist, X_epi, Y, adj, idx, tss_idx, pos, last_batch
def calculate_loss(model_gat, model_cnn, chr_list, valid_chr, test_chr, cell_line_train, cell_line_test,
assay_type_test, fdr_test, organism, genome, batch_size, write_bw):
loss_gat_all = np.array([])
loss_cnn_all = np.array([])
Y_hat_gat_all = np.array([])
Y_hat_cnn_all = np.array([])
Y_all = np.array([])
y_gene = np.array([])
y_hat_gene_gat = np.array([])
y_hat_gene_cnn = np.array([])
chr_pos = []
gene_pos = np.array([])
gene_names = np.array([])
gene_tss = np.array([])
gene_chr = np.array([])
n_contacts = np.array([])
n_tss_in_bin = np.array([])
y_bw = np.array([])
y_pred_gat_bw = np.array([])
y_pred_cnn_bw = np.array([])
chroms = np.array([])
starts = np.array([])
ends = np.array([])
T = 400
test_chr_str = [str(i) for i in test_chr]
test_chr_str = ','.join(test_chr_str)
valid_chr_str = [str(i) for i in valid_chr]
valid_chr_str = ','.join(valid_chr_str)
if write_bw == True and organism == 'human' and genome=='hg19':
# human
chr_length = [("chr1", 249250621), ("chr2", 243199373), ("chr3", 198022430), ("chr4", 191154276), ("chr5", 180915260), ("chr6", 171115067),
("chr7", 159138663), ("chr8", 146364022), ("chr9", 141213431), ("chr10", 135534747), ("chr11", 135006516), ("chr12", 133851895),
("chr13", 115169878), ("chr14", 107349540), ("chr15", 102531392), ("chr16", 90354753), ("chr17", 81195210), ("chr18", 78077248),
("chr19", 59128983), ("chr20", 63025520), ("chr21", 48129895), ("chr22", 51304566)]
bw_y_true = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/CAGE_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_true.addHeader(chr_length)
bw_y_pred_gat = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-GraphReg_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_gat.addHeader(chr_length)
bw_y_pred_cnn = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-CNN_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_cnn.addHeader(chr_length)
if write_bw == True and organism == 'human' and genome=='hg38':
# human
chr_length = [("chr1", 248956422), ("chr2", 242193529), ("chr3", 198295559), ("chr4", 190214555), ("chr5", 181538259), ("chr6", 170805979),
("chr7", 159345973), ("chr8", 145138636), ("chr9", 138394717), ("chr10", 133797422), ("chr11", 135086622), ("chr12", 133275309),
("chr13", 114364328), ("chr14", 107043718), ("chr15", 101991189), ("chr16", 90338345), ("chr17", 83257441), ("chr18", 80373285),
("chr19", 58617616), ("chr20", 64444167), ("chr21", 46709983), ("chr22", 50818468)]
bw_y_true = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/CAGE_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_true.addHeader(chr_length)
bw_y_pred_gat = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-GraphReg_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_gat.addHeader(chr_length)
bw_y_pred_cnn = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-CNN_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_cnn.addHeader(chr_length)
if write_bw == True and organism == 'mouse':
# mouse:
chr_length = [("chr1", 195465000), ("chr2", 182105000), ("chr3", 160030000), ("chr4", 156500000), ("chr5", 151825000), ("chr6", 149730000),
("chr7", 145435000), ("chr8", 129395000), ("chr9", 124590000), ("chr10", 130685000), ("chr11", 122075000), ("chr12", 120120000),
("chr13", 120415000), ("chr14", 124895000), ("chr15", 104035000), ("chr16", 98200000), ("chr17", 94980000), ("chr18", 90695000),
("chr19", 61425000)]
bw_y_true = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/CAGE_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_true.addHeader(chr_length)
bw_y_pred_gat = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-GraphReg_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_gat.addHeader(chr_length)
bw_y_pred_cnn = pyBigWig.open(data_path+'/results/bigwig/cage_prediction/Epi-models/Epi-CNN_CAGE_pred_'+cell_line_train+'_to_'+cell_line_test+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.bw', "w")
bw_y_pred_cnn.addHeader(chr_length)
for i in chr_list:
#print('chr :', i)
file_name = data_path+'/data/tfrecords/tfr_epi_RPGC_'+cell_line_test+'_'+assay_type_test+'_FDR_'+fdr_test+'_chr'+str(i)+'.tfr'
iterator = dataset_iterator(file_name, batch_size)
tss_pos = np.load(data_path+'/data/tss/'+organism+'/'+genome+'/tss_pos_chr'+str(i)+'.npy', allow_pickle=True)
gene_names_all = np.load(data_path+'/data/tss/'+organism+'/'+genome+'/tss_gene_chr'+str(i)+'.npy', allow_pickle=True)
n_tss = np.load(data_path+'/data/tss/'+organism+'/'+genome+'/tss_bins_chr'+str(i)+'.npy', allow_pickle=True)
tss_pos = tss_pos[tss_pos>0]
#print('tss_pos: ', len(tss_pos), tss_pos[0:10])
gene_names_all = gene_names_all[gene_names_all != ""]
#print('gene_names_all: ', len(gene_names_all), gene_names_all[0:10])
n_tss = n_tss[n_tss>=1]
#print('n_tss: ', len(n_tss), n_tss[0:10])
pos_bw = np.array([])
y_bw_ = np.array([])
y_pred_gat_bw_ = np.array([])
y_pred_cnn_bw_ = np.array([])
#print(tss_pos.shape[0], tss_pos[0:100])
while True:
data_exist, X_epi, Y, adj, idx, tss_idx, pos, last_batch = read_tf_record_1shot(iterator)
### Creating BigWig files for true and predicted CAGE tracks ###
if write_bw == True:
if data_exist:
pos_mid = pos[20000:40000]
if (pos_mid[0] > -1 and pos_mid[-1] < 10**15):
pos_bw = np.append(pos_bw, pos_mid)
Y_hat_cnn = model_cnn(X_epi)
Y_hat_gat, att = model_gat([X_epi, adj])
#att_1 = att[0]
#att_2 = att[1]
#att_3 = att[2]
Y_idx = tf.gather(Y, tf.range(T, 2*T), axis=1)
Y_hat_cnn_idx = tf.gather(Y_hat_cnn, tf.range(T, 2*T), axis=1)
Y_hat_gat_idx = tf.gather(Y_hat_gat, tf.range(T, 2*T), axis=1)
y1 = np.repeat(Y_idx.numpy().ravel(), 50)
y2 = np.repeat(Y_hat_gat_idx.numpy().ravel(), 50)
y3 = np.repeat(Y_hat_cnn_idx.numpy().ravel(), 50)
y_bw_ = np.append(y_bw_, y1)
y_pred_gat_bw_= np.append(y_pred_gat_bw_, y2)
y_pred_cnn_bw_ = np.append(y_pred_cnn_bw_, y3)
if data_exist:
if tf.reduce_sum(tf.gather(tss_idx, idx)) > 0:
Y_hat_cnn = model_cnn(X_epi)
Y_hat_gat, att = model_gat([X_epi, adj])
#att_1 = att[0]
#att_2 = att[1]
#att_3 = att[2]
Y_idx = tf.gather(Y, idx, axis=1)
Y_hat_cnn_idx = tf.gather(Y_hat_cnn, idx, axis=1)
Y_hat_gat_idx = tf.gather(Y_hat_gat, idx, axis=1)
loss_gat = poisson_loss(Y_idx,Y_hat_gat_idx)
loss_cnn = poisson_loss(Y_idx,Y_hat_cnn_idx)
loss_gat_all = np.append(loss_gat_all, loss_gat.numpy())
loss_cnn_all = np.append(loss_cnn_all, loss_cnn.numpy())
Y_hat_gat_all = np.append(Y_hat_gat_all, Y_hat_gat_idx.numpy().ravel())
Y_hat_cnn_all = np.append(Y_hat_cnn_all, Y_hat_cnn_idx.numpy().ravel())
Y_all = np.append(Y_all, Y_idx.numpy().ravel())
chr_pos.append('chr'+str(i)+'_'+str(pos[20000]))
## extract gene tss's ##
row_sum = tf.squeeze(tf.reduce_sum(adj, axis=-1))
num_contacts = np.repeat(row_sum.numpy().ravel(), 50)
if last_batch == 0:
tss_pos_1 = tss_pos[np.logical_and(tss_pos >= pos[20000], tss_pos < pos[40000])]
else:
tss_pos_1 = tss_pos[np.logical_and(tss_pos >= pos[20000], tss_pos < pos[-1])]
for j in range(len(tss_pos_1)):
idx_tss = np.where(pos == int(np.floor(tss_pos_1[j]/100)*100))[0][0]
#print(idx_tss)
idx_gene = np.where(tss_pos == tss_pos_1[j])[0]
y_true_ = np.repeat(Y.numpy().ravel(), 50)
y_hat_gat_ = np.repeat(Y_hat_gat.numpy().ravel(), 50)
y_hat_cnn_ = np.repeat(Y_hat_cnn.numpy().ravel(), 50)
y_gene = np.append(y_gene, y_true_[idx_tss]) # + y_true_[idx_tss-50] + y_true_[idx_tss+50])
y_hat_gene_gat = np.append(y_hat_gene_gat, y_hat_gat_[idx_tss]) # + y_hat_gat_[idx_tss-50] + y_hat_gat_[idx_tss+50])
y_hat_gene_cnn = np.append(y_hat_gene_cnn, y_hat_cnn_[idx_tss]) # + y_hat_cnn_[idx_tss-50] + y_hat_cnn_[idx_tss+50])
gene_pos = np.append(gene_pos, 'chr'+str(i)+'_tss_'+str(tss_pos_1[j]))
gene_tss = np.append(gene_tss, tss_pos_1[j])
gene_chr = np.append(gene_chr, 'chr'+str(i))
gene_names = np.append(gene_names, gene_names_all[idx_gene])
n_tss_in_bin = np.append(n_tss_in_bin, n_tss[idx_gene])
n_contacts = np.append(n_contacts, num_contacts[idx_tss])
else:
if write_bw == True:
assert len(pos_bw) == len(y_bw_) == len(y_pred_gat_bw_)
chroms_ = np.array(["chr"+str(i)] * len(pos_bw))
print('chr'+str(i), len(pos_bw))
starts_ = pos_bw.astype(np.int64)
ends_ = starts_ + 100
ends_ = ends_.astype(np.int64)
chroms = np.append(chroms, chroms_)
starts = np.append(starts, starts_)
ends = np.append(ends, ends_)
y_bw = np.append(y_bw, y_bw_)
y_pred_gat_bw = np.append(y_pred_gat_bw, y_pred_gat_bw_)
y_pred_cnn_bw = np.append(y_pred_cnn_bw, y_pred_cnn_bw_)
break
if write_bw == True:
print(len(chroms), len(starts), len(ends), len(y_bw))
starts = starts.astype(np.int64)
ends = ends.astype(np.int64)
y_bw = y_bw.astype(np.float64)
y_pred_gat_bw = y_pred_gat_bw.astype(np.float64)
y_pred_cnn_bw = y_pred_cnn_bw.astype(np.float64)
print(chroms)
print(starts)
print(ends)
print(y_bw)
bw_y_true.addEntries(chroms, starts, ends=ends, values=y_bw)
bw_y_pred_gat.addEntries(chroms, starts, ends=ends, values=y_pred_gat_bw)
bw_y_pred_cnn.addEntries(chroms, starts, ends=ends, values=y_pred_cnn_bw)
bw_y_true.close()
bw_y_pred_gat.close()
bw_y_pred_cnn.close()
print('len of test/valid Y: ', len(y_gene))
return y_gene, y_hat_gene_gat, y_hat_gene_cnn, chr_pos, gene_pos, gene_names, gene_tss, gene_chr, n_contacts, n_tss_in_bin
############################################################# load model #############################################################
cell_line_train_list = ['K562', 'K562', 'K562', 'GM12878', 'GM12878', 'GM12878', 'hESC', 'hESC', 'hESC']
cell_line_test_list = ['K562', 'GM12878', 'hESC', 'GM12878', 'K562', 'hESC', 'hESC', 'GM12878', 'K562']
batch_size = 1
organism = 'human'
write_bw = False
prediction = True
load_np = False
plot_violin = False
plot_box = False
plot_scatter = False
save_R_NLL_to_csv = True
def set_axis_style(ax, labels, positions_tick):
ax.get_xaxis().set_tick_params(direction='out')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_tick_params(labelsize=15)
ax.set_xticks(positions_tick)
ax.set_xticklabels(labels, fontsize=20)
def add_label(violin, labels, label):
color = violin["bodies"][0].get_facecolor().flatten()
labels.append((mpatches.Patch(color=color), label))
for c in range(len(cell_line_train_list)):
cell_line_train = cell_line_train_list[c]
cell_line_test = cell_line_test_list[c]
if cell_line_test == 'GM12878' or cell_line_test == 'K562':
genome='hg19'
assay_type_test_list = ['HiC', 'HiChIP']
elif cell_line_test == 'hESC':
genome='hg38'
assay_type_test_list = ['MicroC', 'HiCAR']
if cell_line_train == 'GM12878' or cell_line_train == 'K562':
assay_type_train_list = ['HiC', 'HiChIP']
elif cell_line_train == 'hESC':
assay_type_train_list = ['MicroC', 'HiCAR']
for assay_type_train in assay_type_train_list:
for assay_type_test in assay_type_test_list:
for fdr_train in ['1']:
for fdr_test in ['1']:
print('c: {}, assay_type_train: {}, assay_type_test: {}, fdr_train: {}, fdr_test: {}'.format(c,assay_type_train,assay_type_test,fdr_train,fdr_test))
if prediction == True:
valid_loss_gat = np.zeros([10,4])
valid_rho_gat = np.zeros([10,4])
valid_sp_gat = np.zeros([10,4])
valid_loss_cnn = np.zeros([10,4])
valid_rho_cnn = np.zeros([10,4])
valid_sp_cnn = np.zeros([10,4])
n_gene = np.zeros([10,4])
df_all_predictions = pd.DataFrame(columns=['chr', 'genes', 'n_tss', 'tss', 'tss_distance_from_center', 'n_contact', 'true_cage', 'pred_cage_epi_graphreg', 'pred_cage_epi_cnn', 'nll_epi_graphreg', 'nll_epi_cnn', 'delta_nll'])
for i in range(1,1+10):
iv2 = i+10
it2 = i+11
valid_chr_list = [i, iv2]
test_chr_list = [i+1, it2]
chr_list = test_chr_list.copy()
chr_list.sort()
test_chr_str = [str(i) for i in test_chr_list]
test_chr_str = ','.join(test_chr_str)
valid_chr_str = [str(i) for i in valid_chr_list]
valid_chr_str = ','.join(valid_chr_str)
if load_np == True:
y_gene = np.load(data_path+'/results/numpy/cage_prediction/true_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
y_hat_gene_gat = np.load(data_path+'/results/numpy/cage_prediction/Epi-GraphReg_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
y_hat_gene_cnn = np.load(data_path+'/results/numpy/cage_prediction/Epi-CNN_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
n_contacts = np.load(data_path+'/results/numpy/cage_prediction/n_contacts_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
else:
model_name_gat = data_path+'/models/'+cell_line_train+'/Epi-GraphReg_RPGC_'+cell_line_train+'_'+assay_type_train+'_FDR_'+fdr_train+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.h5'
model_gat = tf.keras.models.load_model(model_name_gat, custom_objects={'GraphAttention': GraphAttention})
model_gat.trainable = False
model_gat._name = 'Epi-GraphReg'
#model_gat.summary()
model_name = data_path+'/models/'+cell_line_train+'/Epi-CNN_RPGC_'+cell_line_train+'_valid_chr_'+valid_chr_str+'_test_chr_'+test_chr_str+'.h5'
model_cnn = tf.keras.models.load_model(model_name)
model_cnn.trainable = False
model_cnn._name = 'Epi-CNN'
#model_cnn.summary()
y_gene, y_hat_gene_gat, y_hat_gene_cnn, _, _, gene_names, gene_tss, gene_chr, n_contacts, n_tss_in_bin = calculate_loss(model_gat, model_cnn,
chr_list, valid_chr_list, test_chr_list, cell_line_train, cell_line_test, assay_type_test, fdr_test, organism, genome, batch_size, write_bw)
np.save(data_path+'/results/numpy/cage_prediction/true_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy', y_gene)
np.save(data_path+'/results/numpy/cage_prediction/Epi-GraphReg_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy', y_hat_gene_gat)
np.save(data_path+'/results/numpy/cage_prediction/Epi-CNN_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy', y_hat_gene_cnn)
np.save(data_path+'/results/numpy/cage_prediction/n_contacts_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy', n_contacts)
df_tmp = pd.DataFrame(columns=['chr', 'genes', 'n_tss', 'tss', 'tss_distance_from_center', 'n_contact', 'true_cage', 'pred_cage_epi_graphreg', 'pred_cage_epi_cnn', 'nll_epi_graphreg', 'nll_epi_cnn', 'delta_nll'])
df_tmp['chr'] = gene_chr
df_tmp['genes'] = gene_names
df_tmp['n_tss'] = n_tss_in_bin.astype(np.int64)
df_tmp['tss'] = gene_tss.astype(np.int64)
df_tmp['tss_distance_from_center'] = np.abs(np.mod(gene_tss, 5000) - 2500).astype(np.int64)
df_tmp['n_contact'] = n_contacts.astype(np.int64)
df_tmp['true_cage'] = y_gene
df_tmp['pred_cage_epi_graphreg'] = y_hat_gene_gat
df_tmp['pred_cage_epi_cnn'] = y_hat_gene_cnn
df_tmp['nll_epi_graphreg'] = poisson_loss_individual(y_gene, y_hat_gene_gat).numpy()
df_tmp['nll_epi_cnn'] = poisson_loss_individual(y_gene, y_hat_gene_cnn).numpy()
df_tmp['delta_nll'] = poisson_loss_individual(y_gene, y_hat_gene_cnn).numpy() - poisson_loss_individual(y_gene, y_hat_gene_gat).numpy() # if delta_nll > 0 then GraphReg prediction is better than CNN
df_all_predictions = df_all_predictions.append(df_tmp).reset_index(drop=True)
for j in range(4):
if j==0:
min_expression = 0
min_contact = 0
elif j==1:
min_expression = 5
min_contact = 0
elif j==2:
min_expression = 5
min_contact = 1
else:
min_expression = 5
min_contact = 5
idx = np.where(np.logical_and(n_contacts >= min_contact, y_gene >= min_expression))[0]
y_gene_idx = y_gene[idx]
y_hat_gene_gat_idx = y_hat_gene_gat[idx]
y_hat_gene_cnn_idx = y_hat_gene_cnn[idx]
valid_loss_gat[i-1,j] = poisson_loss(y_gene_idx, y_hat_gene_gat_idx).numpy()
valid_rho_gat[i-1,j] = np.corrcoef(np.log2(y_gene_idx+1),np.log2(y_hat_gene_gat_idx+1))[0,1]
valid_sp_gat[i-1,j] = spearmanr(np.log2(y_gene_idx+1),np.log2(y_hat_gene_gat_idx+1))[0]
valid_loss_cnn[i-1,j] = poisson_loss(y_gene_idx, y_hat_gene_cnn_idx).numpy()
valid_rho_cnn[i-1,j] = np.corrcoef(np.log2(y_gene_idx+1),np.log2(y_hat_gene_cnn_idx+1))[0,1]
valid_sp_cnn[i-1,j] = spearmanr(np.log2(y_gene_idx+1), np.log2(y_hat_gene_cnn_idx+1))[0]
n_gene[i-1,j] = len(y_gene_idx)
#print('NLL GAT: ', valid_loss_gat, ' rho: ', valid_rho_gat, ' sp: ', valid_sp_gat)
#print('NLL CNN: ', valid_loss_cnn, ' rho: ', valid_rho_cnn, ' sp: ', valid_sp_cnn)
#print('Mean Loss GAT: ', np.mean(valid_loss_gat, axis=0), ' +/- ', np.std(valid_loss_gat, axis=0), ' std')
#print('Mean Loss CNN: ', np.mean(valid_loss_cnn, axis=0), ' +/- ', np.std(valid_loss_cnn, axis=0), ' std \n')
#print('Mean R GAT: ', np.mean(valid_rho_gat, axis=0), ' +/- ', np.std(valid_rho_gat, axis=0), ' std')
#print('Mean R CNN: ', np.mean(valid_rho_cnn, axis=0), ' +/- ', np.std(valid_rho_cnn, axis=0), ' std \n')
#print('Mean SP GAT: ', np.mean(valid_sp_gat, axis=0), ' +/- ', np.std(valid_sp_gat, axis=0), ' std')
#print('Mean SP CNN: ', np.mean(valid_sp_cnn, axis=0), ' +/- ', np.std(valid_sp_cnn, axis=0), ' std')
w_loss = np.zeros(4)
w_rho = np.zeros(4)
w_sp = np.zeros(4)
p_loss = np.zeros(4)
p_rho = np.zeros(4)
p_sp = np.zeros(4)
for j in range(4):
w_loss[j], p_loss[j] = wilcoxon(valid_loss_gat[:,j], valid_loss_cnn[:,j], alternative='less')
w_rho[j], p_rho[j] = wilcoxon(valid_rho_gat[:,j], valid_rho_cnn[:,j], alternative='greater')
w_sp[j], p_sp[j] = wilcoxon(valid_sp_gat[:,j], valid_sp_cnn[:,j], alternative='greater')
#print('Wilcoxon Loss: ', w_loss, ' , p_values: ', p_loss)
#print('Wilcoxon R: ', w_rho, ' , p_values: ', p_rho)
#print('Wilcoxon SP: ', w_sp, ' , p_values: ', p_sp)
# write the prediction to csv file
df_all_predictions.to_csv(data_path+'/results/csv/cage_prediction/cell_to_cell/cage_predictions_epi_models_RPGC_'+cell_line_train+'_'+assay_type_train+'_FDR_'+fdr_train+'_to_'+cell_line_test+'_'+assay_type_test+'_FDR_'+fdr_test+'.csv', sep="\t", index=False)
##### write R and NLL for different 3D graphs and FDRs #####
if save_R_NLL_to_csv:
df = pd.DataFrame(columns=['Cell_train', 'Cell_test', 'Method', 'Set', 'valid_chr', 'test_chr', 'n_gene_test', '3D_data_train', '3D_data_test', 'FDR_train', 'FDR_test', 'R','NLL'])
fdr_dict = {'1': 0.1, '01': 0.01, '001': 0.001}
for i in range(1,1+10):
if organism == 'mouse' and i==9:
iv2 = i+10
it2 = 1
elif organism == 'mouse' and i==10:
iv2 = 1
it2 = 2
else:
iv2 = i+10
it2 = i+11
valid_chr_list = [i, iv2]
test_chr_list = [i+1, it2]
chr_list = test_chr_list.copy()
chr_list.sort()
test_chr_str = [str(i) for i in test_chr_list]
test_chr_str = ','.join(test_chr_str)
valid_chr_str = [str(i) for i in valid_chr_list]
valid_chr_str = ','.join(valid_chr_str)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-GraphReg', 'Set': 'All', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,0], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_gat[i-1,0], 'NLL': valid_loss_gat[i-1,0]}, ignore_index=True)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-GraphReg', 'Set': 'Expressed', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,1], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_gat[i-1,1], 'NLL': valid_loss_gat[i-1,1]}, ignore_index=True)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-GraphReg', 'Set': 'Interacted', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,2], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_gat[i-1,2], 'NLL': valid_loss_gat[i-1,2]}, ignore_index=True)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-CNN', 'Set': 'All', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,0], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_cnn[i-1,0], 'NLL': valid_loss_cnn[i-1,0]}, ignore_index=True)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-CNN', 'Set': 'Expressed', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,1], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_cnn[i-1,1], 'NLL': valid_loss_cnn[i-1,1]}, ignore_index=True)
df = df.append({'Cell_train': cell_line_train, 'Cell_test': cell_line_test, 'Method': 'Epi-CNN', 'Set': 'Interacted', 'valid_chr': valid_chr_str, 'test_chr': test_chr_str,
'n_gene_test': n_gene[i-1,2], '3D_data_train': assay_type_train, 'FDR_train': fdr_dict[fdr_train], '3D_data_test': assay_type_test, 'FDR_test': fdr_dict[fdr_test],
'R': valid_rho_cnn[i-1,2], 'NLL': valid_loss_cnn[i-1,2]}, ignore_index=True)
df.to_csv(data_path+'/results/csv/cage_prediction/cell_to_cell/R_NLL_epi_models_RPGC_'+cell_line_train+'_'+assay_type_train+'_FDR_'+fdr_train+'_to_'+cell_line_test+'_'+assay_type_test+'_FDR_'+fdr_test+'.csv', sep="\t", index=False)
##### plot violin plots #####
if plot_violin == True:
labels = []
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))
ax1.set_title(cell_line_train+' to '+cell_line_test, fontsize=20)
#ax1.set_title(cell_line_train, fontsize=20)
ax1.set_ylabel('R', fontsize=20)
positions1 = np.array([1,3,5,7])
parts11 = ax1.violinplot(valid_rho_gat, positions=positions1, showmeans = False, showextrema = True, showmedians = True)
for pc in parts11['bodies']:
pc.set_facecolor('orange')
pc.set_edgecolor('black')
pc.set_alpha(1)
for partname in ('cbars','cmins','cmaxes','cmedians'):
vp = parts11[partname]
vp.set_edgecolor('black')
vp.set_linewidth(1)
add_label(parts11, labels, "Epi-GraphReg")
positions2 = positions1 + .75
parts12 = ax1.violinplot(valid_rho_cnn, positions=positions2, showmeans = False, showextrema = True, showmedians = True)
for pc in parts12['bodies']:
pc.set_facecolor('deepskyblue')
pc.set_edgecolor('black')
pc.set_alpha(1)
for partname in ('cbars','cmins','cmaxes','cmedians'):
vp = parts12[partname]
vp.set_edgecolor('black')
vp.set_linewidth(1)
add_label(parts12, labels, "Epi-CNN")
tick_labels = ['Set A', 'Set B', 'Set C', 'Set D']
positions_tick = (positions1 + positions2)/2
set_axis_style(ax1, tick_labels, positions_tick)
ax1.grid(axis='y')
ax1.legend(*zip(*labels), loc=1, fontsize=15)
ax1.set_ylim((0.4,1))
for i in range(4):
if p_rho[i] <= 0.05:
x1, x2 = positions1[i], positions2[i]
y, h, col = np.max(np.append(valid_rho_gat[:,i],valid_rho_cnn[:,i])) + .02, .02, 'k'
ax1.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
ax1.text((x1+x2)*.5, y+h, "p="+"{:4.2e}".format(p_rho[i]), ha='center', va='bottom', color=col, fontsize=15)
ax2.set_ylabel('NLL', fontsize=20)
positions1 = np.array([1,3,5,7])
parts21 = ax2.violinplot(valid_loss_gat, positions=positions1, showmeans = False, showextrema = True, showmedians = True)
for pc in parts21['bodies']:
pc.set_facecolor('orange')
pc.set_edgecolor('black')
pc.set_alpha(1)
for partname in ('cbars','cmins','cmaxes','cmedians'):
vp = parts21[partname]
vp.set_edgecolor('black')
vp.set_linewidth(1)
positions2 = positions1 + .75
parts22 = ax2.violinplot(valid_loss_cnn, positions=positions2, showmeans = False, showextrema = True, showmedians = True)
for pc in parts22['bodies']:
pc.set_facecolor('deepskyblue')
pc.set_edgecolor('black')
pc.set_alpha(1)
for partname in ('cbars','cmins','cmaxes','cmedians'):
vp = parts22[partname]
vp.set_edgecolor('black')
vp.set_linewidth(1)
tick_labels = ['Set A', 'Set B', 'Set C', 'Set D']
positions_tick = (positions1 + positions2)/2
set_axis_style(ax2, tick_labels, positions_tick)
ax2.grid(axis='y')
k = 800
ax2.set_ylim((0,k))
for i in range(4):
if p_loss[i] <= 0.05:
x1, x2 = positions1[i], positions2[i]
y, h, col = np.max(np.append(valid_loss_gat[:,i],valid_loss_cnn[:,i])) + k/40, k/40, 'k'
ax2.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
ax2.text((x1+x2)*.5, y+h, "p="+"{:4.2e}".format(p_loss[i]), ha='center', va='bottom', color=col, fontsize=15)
plt.savefig('../figs/Epi-models/violinplot_'+cell_line_train+'_to_'+cell_line_test+'.png')
##### plot boxplots (only for C and D gene sets) #####
if plot_box == True:
df = pd.DataFrame(columns=['R','NLL','Method','Set'])
for i in range(10):
df = df.append({'R': valid_rho_gat[i,0], 'NLL': valid_loss_gat[i,0], 'Method': 'Epi-GraphReg', 'Set': 'A'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,1], 'NLL': valid_loss_gat[i,1], 'Method': 'Epi-GraphReg', 'Set': 'B'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,2], 'NLL': valid_loss_gat[i,2], 'Method': 'Epi-GraphReg', 'Set': 'C'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,3], 'NLL': valid_loss_gat[i,3], 'Method': 'Epi-GraphReg', 'Set': 'D'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,0], 'NLL': valid_loss_cnn[i,0], 'Method': 'Epi-CNN', 'Set': 'A'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,1], 'NLL': valid_loss_cnn[i,1], 'Method': 'Epi-CNN', 'Set': 'B'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,2], 'NLL': valid_loss_cnn[i,2], 'Method': 'Epi-CNN', 'Set': 'C'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,3], 'NLL': valid_loss_cnn[i,3], 'Method': 'Epi-CNN', 'Set': 'D'}, ignore_index=True)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 7))
#ax1.set_title(cell_line_train+' to '+cell_line_test, fontsize=20)
#ax1.set_title(cell_line_train, fontsize=20)
b=sns.boxplot(x='Set', y='R', hue='Method', data=df, palette={"Epi-GraphReg": "orange", "Epi-CNN": "deepskyblue"}, order=['C', 'D'], ax=ax1)
add_stat_annotation(ax1, data=df, x='Set', y='R', hue='Method',
box_pairs=[(("C", "Epi-GraphReg"), ("C", "Epi-CNN")),
(("D", "Epi-GraphReg"), ("D", "Epi-CNN"))],
test='Wilcoxon', text_format='star', loc='inside', verbose=0, order=['C', 'D'], fontsize='x-large', comparisons_correction=None)
ax1.yaxis.set_tick_params(labelsize=20)
ax1.xaxis.set_tick_params(labelsize=20)
b.set_xlabel("Set",fontsize=20)
b.set_ylabel("R",fontsize=20)
plt.setp(ax1.get_legend().get_texts(), fontsize='15')
plt.setp(ax1.get_legend().get_title(), fontsize='15')
#ax1.set_ylim((.4,.75))
b = sns.boxplot(x='Set', y='NLL', hue='Method', data=df, palette={"Epi-GraphReg": "orange", "Epi-CNN": "deepskyblue"}, order=['C', 'D'], ax=ax2)
add_stat_annotation(ax2, data=df, x='Set', y='NLL', hue='Method',
box_pairs=[(("C", "Epi-GraphReg"), ("C", "Epi-CNN")),
(("D", "Epi-GraphReg"), ("D", "Epi-CNN"))],
test='Wilcoxon', text_format='star', loc='inside', verbose=2, order=['C', 'D'], fontsize='x-large', comparisons_correction=None)
ax2.yaxis.set_tick_params(labelsize=20)
ax2.xaxis.set_tick_params(labelsize=20)
b.set_xlabel("Set",fontsize=20)
b.set_ylabel("NLL",fontsize=20)
plt.setp(ax2.get_legend().get_texts(), fontsize='15')
plt.setp(ax2.get_legend().get_title(), fontsize='15')
ax2.set_ylim((250,550))
#fig.tight_layout()
fig.suptitle(cell_line_train, fontsize=25)
#fig.suptitle(cell_line_train+' to '+cell_line_test, fontsize=25)
fig.tight_layout(rect=[0, 0, 1, .93])
plt.savefig('../figs/Epi-models/boxplot_'+cell_line_train+'_to_'+cell_line_test+'_CD.png')
##### plot boxplots (all gene sets) #####
df = pd.DataFrame(columns=['R','NLL','Method','Set'])
for i in range(10):
df = df.append({'R': valid_rho_gat[i,0], 'NLL': valid_loss_gat[i,0], 'Method': 'Epi-GraphReg', 'Set': 'A'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,1], 'NLL': valid_loss_gat[i,1], 'Method': 'Epi-GraphReg', 'Set': 'B'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,2], 'NLL': valid_loss_gat[i,2], 'Method': 'Epi-GraphReg', 'Set': 'C'}, ignore_index=True)
df = df.append({'R': valid_rho_gat[i,3], 'NLL': valid_loss_gat[i,3], 'Method': 'Epi-GraphReg', 'Set': 'D'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,0], 'NLL': valid_loss_cnn[i,0], 'Method': 'Epi-CNN', 'Set': 'A'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,1], 'NLL': valid_loss_cnn[i,1], 'Method': 'Epi-CNN', 'Set': 'B'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,2], 'NLL': valid_loss_cnn[i,2], 'Method': 'Epi-CNN', 'Set': 'C'}, ignore_index=True)
df = df.append({'R': valid_rho_cnn[i,3], 'NLL': valid_loss_cnn[i,3], 'Method': 'Epi-CNN', 'Set': 'D'}, ignore_index=True)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 7))
#ax1.set_title(cell_line_train+' to '+cell_line_test, fontsize=20)
#ax1.set_title(cell_line_train, fontsize=20)
b=sns.boxplot(x='Set', y='R', hue='Method', data=df, palette={"Epi-GraphReg": "orange", "Epi-CNN": "deepskyblue"}, order=['A', 'B', 'C', 'D'], ax=ax1)
add_stat_annotation(ax1, data=df, x='Set', y='R', hue='Method',
box_pairs=[(("A", "Epi-GraphReg"), ("A", "Epi-CNN")),
(("B", "Epi-GraphReg"), ("B", "Epi-CNN")),
(("C", "Epi-GraphReg"), ("C", "Epi-CNN")),
(("D", "Epi-GraphReg"), ("D", "Epi-CNN"))],
test='Wilcoxon', text_format='star', loc='inside', verbose=0, order=['A', 'B', 'C', 'D'], fontsize='x-large', comparisons_correction=None)
ax1.yaxis.set_tick_params(labelsize=20)
ax1.xaxis.set_tick_params(labelsize=20)
b.set_xlabel("Set",fontsize=20)
b.set_ylabel("R",fontsize=20)
plt.setp(ax1.get_legend().get_texts(), fontsize='15')
plt.setp(ax1.get_legend().get_title(), fontsize='15')
#ax1.set_ylim((.4,.75))
b = sns.boxplot(x='Set', y='NLL', hue='Method', data=df, palette={"Epi-GraphReg": "orange", "Epi-CNN": "deepskyblue"}, order=['A', 'B', 'C', 'D'], ax=ax2)
add_stat_annotation(ax2, data=df, x='Set', y='NLL', hue='Method',
box_pairs=[(("A", "Epi-GraphReg"), ("A", "Epi-CNN")),
(("B", "Epi-GraphReg"), ("B", "Epi-CNN")),
(("C", "Epi-GraphReg"), ("C", "Epi-CNN")),
(("D", "Epi-GraphReg"), ("D", "Epi-CNN"))],
test='Wilcoxon', text_format='star', loc='inside', verbose=2, order=['A', 'B', 'C', 'D'], fontsize='x-large', comparisons_correction=None)
ax2.yaxis.set_tick_params(labelsize=20)
ax2.xaxis.set_tick_params(labelsize=20)
b.set_xlabel("Set",fontsize=20)
b.set_ylabel("NLL",fontsize=20)
plt.setp(ax2.get_legend().get_texts(), fontsize='15')
plt.setp(ax2.get_legend().get_title(), fontsize='15')
#ax2.set_ylim((250,900))
#fig.tight_layout()
#fig.suptitle(cell_line_train, fontsize=25)
fig.suptitle(cell_line_train+' to '+cell_line_test, fontsize=25)
fig.tight_layout(rect=[0, 0, 1, .93])
plt.savefig('../figs/Epi-models/boxplot_'+cell_line_train+'_to_'+cell_line_test+'_all.png')
##### scatter plots #####
if plot_scatter == True:
for i in range(1,1+10):
y_gene = np.load(data_path+'/results/numpy/cage_prediction/true_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
y_hat_gene_gat = np.load(data_path+'/results/numpy/cage_prediction/Epi-GraphReg_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
y_hat_gene_cnn = np.load(data_path+'/results/numpy/cage_prediction/Epi-CNN_predicted_cage_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
n_contacts = np.load(data_path+'/results/numpy/cage_prediction/n_contacts_'+cell_line_train+'_to_'+cell_line_test+'_'+str(i)+'.npy')
idx_0 = np.where(n_contacts==0)[0]
idx_1 = np.where(n_contacts>=1)[0]
n_contacts_idx_1 = n_contacts[idx_1]
valid_loss_gat_n0 = poisson_loss(y_gene[idx_0], y_hat_gene_gat[idx_0]).numpy()
valid_rho_gat_n0 = np.corrcoef(np.log2(y_gene[idx_0]+1),np.log2(y_hat_gene_gat[idx_0]+1))[0,1]
valid_loss_gat_n1 = poisson_loss(y_gene[idx_1], y_hat_gene_gat[idx_1]).numpy()
valid_rho_gat_n1 = np.corrcoef(np.log2(y_gene[idx_1]+1),np.log2(y_hat_gene_gat[idx_1]+1))[0,1]
valid_loss_cnn_n0 = poisson_loss(y_gene[idx_0], y_hat_gene_cnn[idx_0]).numpy()
valid_rho_cnn_n0 = np.corrcoef(np.log2(y_gene[idx_0]+1),np.log2(y_hat_gene_cnn[idx_0]+1))[0,1]
valid_loss_cnn_n1 = poisson_loss(y_gene[idx_1], y_hat_gene_cnn[idx_1]).numpy()
valid_rho_cnn_n1 = np.corrcoef(np.log2(y_gene[idx_1]+1),np.log2(y_hat_gene_cnn[idx_1]+1))[0,1]
plt.figure(figsize=(8,7))
cm = plt.cm.get_cmap('viridis_r')
idx=np.argsort(n_contacts_idx_1)
sc = plt.scatter(np.log2(y_gene[idx]+1),np.log2(y_hat_gene_gat[idx]+1), c=np.log2(n_contacts_idx_1[idx]+1), s=100, cmap=cm, alpha=.7, edgecolors='')
plt.xlim((-.5,13.5))
plt.ylim((-.5,13.5))
#plt.title('Epi-GraphReg, '+cell_line_train+' to '+cell_line_test, fontsize=20)
plt.title('Epi-GraphReg, '+cell_line_train, fontsize=20)
plt.xlabel("log2 (true + 1)", fontsize=20)
plt.ylabel("log2 (pred + 1)", fontsize=20)
plt.tick_params(axis='x', labelsize=15)
plt.tick_params(axis='y', labelsize=15)
plt.grid(alpha=.5)
props = dict(boxstyle='round', facecolor='white', alpha=1)
#plt.text(0,15, 'n=0: R= '+"{:5.3f}".format(valid_rho_gat_n0) + ', NLL= '+str(np.float16(valid_loss_gat_n0))+'\n'+
# 'n>0: R= '+"{:5.3f}".format(valid_rho_gat_n1) + ', NLL= '+str(np.float16(valid_loss_gat_n1)),
# horizontalalignment='left', verticalalignment='top', bbox=props, fontsize=20)
plt.text(0,13, 'n>0: R= '+"{:5.3f}".format(valid_rho_gat_n1) + ', NLL= '+str(np.float16(valid_loss_gat_n1)),
horizontalalignment='left', verticalalignment='top', bbox=props, fontsize=20)
cbar = plt.colorbar(sc)
cbar.set_label(label='log2 (n + 1)', size=20)
cbar.ax.tick_params(labelsize=15)
#plt.show()
plt.tight_layout()
plt.savefig('../figs/Epi-models/scatter_plots/Epi-GraphReg_scatterplot_'+cell_line_train+'_to_'+cell_line_test+'.png')
plt.figure(figsize=(8,7))
cm = plt.cm.get_cmap('viridis_r')
idx=np.argsort(n_contacts_idx_1)
sc = plt.scatter(np.log2(y_gene[idx]+1),np.log2(y_hat_gene_cnn[idx]+1), c=np.log2(n_contacts_idx_1[idx]+1), s=100, cmap=cm, alpha=.7, edgecolors='')
plt.xlim((-.5,13.5))
plt.ylim((-.5,13.5))
#plt.title('Epi-CNN, '+cell_line_train+' to '+cell_line_test, fontsize=20)
plt.title('Epi-CNN, '+cell_line_train, fontsize=20)
plt.xlabel("log2 (true + 1)", fontsize=20)
plt.ylabel("log2 (pred + 1)", fontsize=20)
plt.tick_params(axis='x', labelsize=15)
plt.tick_params(axis='y', labelsize=15)
plt.grid(alpha=.5)
props = dict(boxstyle='round', facecolor='white', alpha=1)
#plt.text(0,15, 'n=0: R= '+"{:5.3f}".format(valid_rho_cnn_n0) + ', NLL= '+str(np.float16(valid_loss_cnn_n0))+'\n'+
# 'n>0: R= '+"{:5.3f}".format(valid_rho_cnn_n1) + ', NLL= '+str(np.float16(valid_loss_cnn_n1)),
# horizontalalignment='left', verticalalignment='top', bbox=props, fontsize=20)
plt.text(0,13, 'n>0: R= '+"{:5.3f}".format(valid_rho_cnn_n1) + ', NLL= '+str(np.float16(valid_loss_cnn_n1)),
horizontalalignment='left', verticalalignment='top', bbox=props, fontsize=20)
cbar = plt.colorbar(sc)
cbar.set_label(label='log2 (n + 1)', size=20)
cbar.ax.tick_params(labelsize=15)
#plt.show()
plt.tight_layout()
plt.savefig('../figs/Epi-models/scatter_plots/Epi-CNN_scatterplot_'+cell_line_train+'_to_'+cell_line_test+'.png')