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SENSE-PPI
Commit 2fe8d626 by Konstantin Volzhenin
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0.1.9 minor bugfix

parent a65a0e1a
Showing with 81 additions and 140 deletions
senseppi/commands/predict.py
......@@ -3,7 +3,6 @@ import pytorch_lightning as pl
from itertools import permutations, product
import numpy as np
import pandas as pd
import logging
import pathlib
import argparse
from ..dataset import PairSequenceData
......@@ -97,11 +96,7 @@ def main(params):
compute_embeddings(params)
# WARNING: due to some internal issues of pytorch, the mps backend is temporarily disabled
if params.device == 'mps':
logging.warning('WARNING: due to some internal issues of torch, the mps backend is temporarily disabled.'
'The cpu backend will be used instead.')
params.device = 'cpu'
block_mps(params)
logging.info('Predicting...')
preds = predict(params)
......@@ -116,8 +111,8 @@ def main(params):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = add_args(parser)
params = parser.parse_args()
pred_parser = argparse.ArgumentParser()
pred_parser = add_args(pred_parser)
pred_params = pred_parser.parse_args()
main(params)
main(pred_params)
senseppi/commands/predict_string.py
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from torchmetrics import AUROC, Accuracy, Precision, Recall, F1Score, MatthewsCorrCoef
import networkx as nx
import seaborn as sns
......@@ -10,19 +8,17 @@ from matplotlib.patches import Rectangle
import argparse
import matplotlib.pyplot as plt
import glob
import logging
from ..model import SensePPIModel
from ..utils import *
from ..network_utils import *
from ..esm2_model import add_esm_args, compute_embeddings
from ..dataset import PairSequenceData
from .predict import predict
def main(params):
LABEL_THRESHOLD = params.score / 1000.
PRED_THRESHOLD = params.pred_threshold / 1000.
label_threshold = params.score / 1000.
pred_threshold = params.pred_threshold / 1000.
pairs_file = 'protein.pairs_string.tsv'
fasta_file = 'sequences.fasta'
......@@ -30,22 +26,18 @@ def main(params):
get_interactions_from_string(params.genes, species=params.species, add_nodes=params.nodes,
required_score=params.score, network_type=params.network_type)
process_string_fasta(fasta_file, min_len=params.min_len, max_len=params.max_len)
generate_pairs_string(fasta_file, output_file=pairs_file, with_self=False, delete_proteins=params.delete_proteins)
generate_pairs_string(fasta_file, output_file=pairs_file, delete_proteins=params.delete_proteins)
params.fasta_file = fasta_file
compute_embeddings(params)
# WARNING: due to some internal issues of pytorch, the mps backend is temporarily disabled
if params.device == 'mps':
logging.warning('WARNING: due to some internal issues of torch, the mps backend is temporarily disabled.'
'The cpu backend will be used instead.')
params.device = 'cpu'
block_mps(params)
preds = predict(params)
# open the actions tsv file as dataframe and add the last column with the predictions
data = pd.read_csv('protein.pairs_string.tsv', delimiter='\t', names=["seq1", "seq2", "string_label"])
data['binary_label'] = data['string_label'].apply(lambda x: 1 if x > LABEL_THRESHOLD else 0)
data['binary_label'] = data['string_label'].apply(lambda x: 1 if x > label_threshold else 0)
data['preds'] = preds
print(data.sort_values(by=['preds'], ascending=False).to_string())
......@@ -53,13 +45,18 @@ def main(params):
# Calculate torch metrics based on data['binary_label'] and data['preds']
torch_labels = torch.tensor(data['binary_label'])
torch_preds = torch.tensor(data['preds'])
print('Accuracy: ', Accuracy(threshold=PRED_THRESHOLD, task='binary')(torch_preds, torch_labels))
print('Precision: ', Precision(threshold=PRED_THRESHOLD, task='binary')(torch_preds, torch_labels))
print('Recall: ', Recall(threshold=PRED_THRESHOLD, task='binary')(torch_preds, torch_labels))
print('F1Score: ', F1Score(threshold=PRED_THRESHOLD, task='binary')(torch_preds, torch_labels))
print('Accuracy: ',
Accuracy(threshold=pred_threshold, task='binary')(torch_preds, torch_labels))
print('Precision: ',
Precision(threshold=pred_threshold, task='binary')(torch_preds, torch_labels))
print('Recall: ',
Recall(threshold=pred_threshold, task='binary')(torch_preds, torch_labels))
print('F1Score: ',
F1Score(threshold=pred_threshold, task='binary')(torch_preds, torch_labels))
print('MatthewsCorrCoef: ',
MatthewsCorrCoef(num_classes=2, threshold=PRED_THRESHOLD, task='binary')(torch_preds, torch_labels))
print('ROCAUC: ', AUROC(task='binary')(torch_preds, torch_labels))
MatthewsCorrCoef(num_classes=2, threshold=pred_threshold, task='binary')(torch_preds, torch_labels))
print('ROCAUC: ',
AUROC(task='binary')(torch_preds, torch_labels))
string_ids = {}
string_tsv = pd.read_csv('string_interactions.tsv', delimiter='\t')[
......@@ -74,37 +71,6 @@ def main(params):
data_to_save = data_to_save.sort_values(by=['preds'], ascending=False)
data_to_save.to_csv(params.output + '.tsv', sep='\t', index=False)
# This part was needed to color the pairs belonging to the train data, temporarily removed
# print('Fetching gene names for training set from STRING...')
#
# if not os.path.exists('all_genes_train.tsv'):
# all_genes = generate_dscript_gene_names(
# file_path=actions_path,
# only_positives=True,
# species=str(hparams.species))
# all_genes.to_csv('all_genes_train.tsv', sep='\t', index=False)
# else:
# all_genes = pd.read_csv('all_genes_train.tsv', sep='\t')
# full_train_data = pd.read_csv(actions_path,
# delimiter='\t', names=['seq1', 'seq2', 'label'])
#
# if all_genes is not None:
# full_train_data = full_train_data.merge(all_genes, left_on='seq1', right_on='QueryString', how='left').merge(
# all_genes, left_on='seq2', right_on='QueryString', how='left')
#
# full_train_data = full_train_data[['preferredName_x', 'preferredName_y', 'label']]
#
# positive_train_data = full_train_data[full_train_data['label'] == 1][['preferredName_x', 'preferredName_y']]
# full_train_data = full_train_data[['preferredName_x', 'preferredName_y']]
#
# full_train_data = [tuple(x) for x in full_train_data.values]
# positive_train_data = [tuple(x) for x in positive_train_data.values]
# else:
# full_train_data = None
# positive_train_data = None
if params.graphs:
# Create two subpolots but make a short gap between them
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 5), gridspec_kw={'width_ratios': [1, 1], 'wspace': 0.2})
......@@ -154,14 +120,6 @@ def main(params):
np.triu_indices_from(data_heatmap.values)]
labels_heatmap.fillna(value=-1, inplace=True)
# This part was needed to color the pairs belonging to the train data, temporarily removed
# if full_train_data is not None:
# for i, row in labels_heatmap.iterrows():
# for j, _ in row.items():
# if (i, j) in full_train_data or (j, i) in full_train_data:
# labels_heatmap.loc[i, j] = -1
cmap = matplotlib.cm.get_cmap('coolwarm').copy()
cmap.set_bad("black")
......@@ -179,53 +137,40 @@ def main(params):
for i in range(len(labels_heatmap)):
ax1.add_patch(Rectangle((i, i), 1, 1, fill=True, color='white', alpha=1, zorder=100))
G = nx.Graph()
pred_graph = nx.Graph()
for i, row in data.iterrows():
if row['string_label'] > LABEL_THRESHOLD:
G.add_edge(row['seq1'], row['seq2'], color='black', weight=row['string_label'], style='dotted')
if row['preds'] > PRED_THRESHOLD and G.has_edge(row['seq1'], row['seq2']):
G[row['seq1']][row['seq2']]['style'] = 'solid'
G[row['seq1']][row['seq2']]['color'] = 'limegreen'
if row['preds'] > PRED_THRESHOLD and row['string_label'] <= LABEL_THRESHOLD:
G.add_edge(row['seq1'], row['seq2'], color='red', weight=row['preds'], style='solid')
if row['string_label'] > label_threshold:
pred_graph.add_edge(row['seq1'], row['seq2'], color='black', weight=row['string_label'], style='dotted')
if row['preds'] > pred_threshold and pred_graph.has_edge(row['seq1'], row['seq2']):
pred_graph[row['seq1']][row['seq2']]['style'] = 'solid'
pred_graph[row['seq1']][row['seq2']]['color'] = 'limegreen'
if row['preds'] > pred_threshold and row['string_label'] <= label_threshold:
pred_graph.add_edge(row['seq1'], row['seq2'], color='red', weight=row['preds'], style='solid')
for node in pred_graph.nodes():
pred_graph.nodes[node]['color'] = 'lightgrey'
# Replace the string ids with gene names
G = nx.relabel_nodes(G, string_ids)
# This part was needed to color the pairs belonging to the train data, temporarily removed
# if positive_train_data is not None:
# for edge in G.edges():
# if (edge[0], edge[1]) in positive_train_data or (edge[1], edge[0]) in positive_train_data:
# print('TRAINING EDGE: ', edge)
# G[edge[0]][edge[1]]['color'] = 'darkblue'
# # G[edge[0]][edge[1]]['weight'] = 1
# Make nodes red if they are present in training data
for node in G.nodes():
# if all_genes is not None and node in all_genes['preferredName'].values:
# G.nodes[node]['color'] = 'orange'
# else:
G.nodes[node]['color'] = 'lightgrey'
pos = nx.spring_layout(G, k=2., iterations=100)
nx.draw(G, pos=pos, with_labels=True, ax=ax2,
edge_color=[G[u][v]['color'] for u, v in G.edges()], width=[G[u][v]['weight'] for u, v in G.edges()],
style=[G[u][v]['style'] for u, v in G.edges()],
node_color=[G.nodes[node]['color'] for node in G.nodes()])
pred_graph = nx.relabel_nodes(pred_graph, string_ids)
pos = nx.spring_layout(pred_graph, k=2., iterations=100)
nx.draw(pred_graph, pos=pos, with_labels=True, ax=ax2,
edge_color=[pred_graph[u][v]['color'] for u, v in pred_graph.edges()],
width=[pred_graph[u][v]['weight'] for u, v in pred_graph.edges()],
style=[pred_graph[u][v]['style'] for u, v in pred_graph.edges()],
node_color=[pred_graph.nodes[node]['color'] for node in pred_graph.nodes()])
legend_elements = [
# Line2D([0], [0], marker='_', color='darkblue', label='PP from training data', markerfacecolor='darkblue',
# markersize=10),
Line2D([0], [0], marker='_', color='limegreen', label='PP', markerfacecolor='limegreen', markersize=10),
Line2D([0], [0], marker='_', color='red', label='FP', markerfacecolor='red', markersize=10),
Line2D([0], [0], marker='_', color='black', label='FN - based on STRING', markerfacecolor='black',
markersize=10, linestyle='dotted')]
plt.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(1.2, 0.0), ncol=1, fontsize=8)
savepath = '{}_graph_{}_{}.pdf'.format(params.output, '_'.join(params.genes), params.species)
plt.savefig(savepath, bbox_inches='tight', dpi=600)
print("The graphs were saved to: ", savepath)
save_path = '{}_graph_{}_{}.pdf'.format(params.output, '_'.join(params.genes), params.species)
plt.savefig(save_path, bbox_inches='tight', dpi=600)
print("The graphs were saved to: ", save_path)
plt.show()
plt.close()
......@@ -235,6 +180,7 @@ def main(params):
os.remove(f)
os.remove('string_interactions.tsv')
def add_args(parser):
parser = add_general_args(parser)
......@@ -242,8 +188,8 @@ def add_args(parser):
parser._action_groups[0].add_argument("model_path", type=str,
help="A path to .ckpt file that contains weights to a pretrained model.")
parser._action_groups[0].add_argument("genes", type=str, nargs="+",
help="Name of gene to fetch from STRING database. Several names can be typed (separated by "
"whitespaces)")
help="Name of gene to fetch from STRING database. Several names can be "
"typed (separated by whitespaces).")
string_pred_args.add_argument("-s", "--species", type=int, default=9606,
help="Species from STRING database. Default: 9606 (H. Sapiens)")
string_pred_args.add_argument("-n", "--nodes", type=int, default=10,
......@@ -252,7 +198,8 @@ def add_args(parser):
help="Score threshold for STRING connections. Range: (0, 1000). Default: 500")
string_pred_args.add_argument("-p", "--pred_threshold", type=int, default=500,
help="Prediction threshold. Range: (0, 1000). Default: 500")
string_pred_args.add_argument("--graphs", action='store_true', help="Enables plotting the heatmap and a network graph.")
string_pred_args.add_argument("--graphs", action='store_true',
help="Enables plotting the heatmap and a network graph.")
string_pred_args.add_argument("-o", "--output", type=str, default="preds_from_string",
help="A path to a file where the predictions will be saved. "
"(.tsv format will be added automatically)")
......@@ -269,6 +216,8 @@ def add_args(parser):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = add_args(parser)
params = parser.parse_args()
\ No newline at end of file
pred_parser = argparse.ArgumentParser()
pred_parser = add_args(pred_parser)
pred_params = pred_parser.parse_args()
main(pred_params)
senseppi/commands/test.py
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import pandas as pd
import logging
import pathlib
import argparse
from ..dataset import PairSequenceData
......@@ -72,11 +71,7 @@ def main(params):
compute_embeddings(params)
# WARNING: due to some internal issues of pytorch, the mps backend is temporarily disabled
if params.device == 'mps':
logging.warning('WARNING: due to some internal issues of torch, the mps backend is temporarily disabled.'
'The cpu backend will be used instead.')
params.device = 'cpu'
block_mps(params)
logging.info('Evaluating...')
test_metrics = test(params)[0]
......@@ -87,7 +82,7 @@ def main(params):
if __name__ == '__main__':
test_parser = argparse.ArgumentParser()
parser = add_args(test_parser)
params = test_parser.parse_args()
test_parser = add_args(test_parser)
test_params = test_parser.parse_args()
main(params)
main(test_params)
senseppi/commands/train.py
......@@ -2,8 +2,7 @@ import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
import pathlib
import argparse
import logging
from ..utils import add_general_args
from ..utils import *
from ..model import SensePPIModel
from ..dataset import PairSequenceData
from ..esm2_model import add_esm_args, compute_embeddings
......@@ -15,11 +14,7 @@ def main(params):
compute_embeddings(params)
# WARNING: due to some internal issues of pytorch, the mps backend is temporarily disabled
if params.device == 'mps':
logging.warning('WARNING: due to some internal issues of torch, the mps backend is temporarily disabled.'
'The cpu backend will be used instead.')
params.device = 'cpu'
block_mps(params)
dataset = PairSequenceData(emb_dir=params.output_dir_esm, actions_file=params.pairs_file,
max_len=params.max_len, labels=True)
......@@ -80,8 +75,8 @@ def add_args(parser):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = add_args(parser)
params = parser.parse_args()
train_parser = argparse.ArgumentParser()
train_parser = add_args(train_parser)
train_params = train_parser.parse_args()
main(params)
\ No newline at end of file
main(train_params)
\ No newline at end of file
senseppi/network_utils.py
......@@ -15,18 +15,13 @@ import shutil
DOWNLOAD_LINK_STRING = "https://stringdb-downloads.org/download/"
def generate_pairs_string(fasta_file, output_file, with_self=False, delete_proteins=None):
def generate_pairs_string(fasta_file, output_file, delete_proteins=None):
ids = []
for record in SeqIO.parse(fasta_file, "fasta"):
ids.append(record.id)
if with_self:
all_pairs = [p for p in product(ids, repeat=2)]
else:
all_pairs = [p for p in permutations(ids, 2)]
pairs = []
for p in all_pairs:
for p in [p for p in permutations(ids, 2)]:
if (p[1], p[0]) not in pairs and (p[0], p[1]) not in pairs:
pairs.append(p)
......
senseppi/utils.py
......@@ -2,6 +2,7 @@ from Bio import SeqIO
import os
from senseppi import __version__
import torch
import logging
def add_general_args(parser):
......@@ -29,6 +30,18 @@ def determine_device():
return device
def block_mps(params):
# WARNING: due to some internal issues of pytorch, the mps backend is temporarily disabled
if hasattr(params, 'device'):
if params.device == 'mps':
logging.warning('WARNING: due to some internal issues of torch, the mps backend is temporarily disabled.'
'The cpu backend will be used instead.')
if torch.cuda.is_available():
params.device = 'gpu'
else:
params.device = 'cpu'
def process_string_fasta(fasta_file, min_len, max_len):
with open('file.tmp', 'w') as f:
for record in SeqIO.parse(fasta_file, "fasta"):
......
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