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upload json scripts
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@panushri25
panushri25 committed Aug 27, 2024
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3 changes: 3 additions & 0 deletions upload_jsons/upload_scripts/README.md
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python run_train_test_making_bias.py
python run_conversion_bias.py

67 changes: 67 additions & 0 deletions upload_jsons/upload_scripts/dnase_run_train_test_making_bias.py
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import pandas as pd
import os

#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",", header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv",sep=",", header=None)
#model_atac=pd.read_csv("bias_models_atlas.csv", sep=',', header=None)
model_atac=pd.read_csv("model_dir_dnase_v2.1_bias.csv", sep=',', header=None)




encode_id = {"K562": "ENCSR868FGK",
"GM12878": "ENCSR637XSC",
"HEPG2": "ENCSR291GJU",
"IMR90": "ENCSR200OML",
"H1ESC": "ENCDUMMY"}

encode_id = {"K562": "ENCSR000EOT",
"GM12878": "ENCSR000EMT",
"HEPG2": "ENCSR149XIL",
"IMR90": "ENCSR477RTP",
"H1ESC": "ENCSR000EMU"}


for i,r in model_atac.iterrows():
fold = r[0]
name = r[1]
model_path = r[2]

#input_peaks=os.path.join(model_path,"chrombpnet_model/filtered.peaks.bed")
input_nonpeaks=os.path.join(model_path,"bias_model/filtered.bias_nonpeaks.bed")
#test_nonpeaks="/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE/"+encode_id[name]+"/negatives_data/test/test."+fold+".filtered.negatives_with_summit.bed"
fold="/mnt/lab_data2/anusri/chrombpnet/splits/"+fold+".json"
output_path=os.path.join(model_path,"train_test_regions_bias_may_7_2024/")

if not os.path.isfile(input_nonpeaks):
cellline=input_nonpeaks.split("/")[10]
biasth=input_nonpeaks.split("/")[11].split("_")[6]
foldn=input_nonpeaks.split("/")[11].split("_")[8]
#print(cellline,biasth,foldn)
if cellline in ["K562", "HEPG2"]:
ddatype="DNASE_PE"
elif cellline in ["H1ESC"]:
ddatype="DNASE_SE"
else:
print(cellline)
break
outputdir=os.path.join(model_path,"bias_model/newgen/")
if not os.path.isfile(os.path.join(model_path,"bias_model/newgen/filtered.bias_nonpeaks.bed")):
os.makedirs(outputdir, exist_ok=True)
print(outputdir)
command = "bash make_missing_bed_regions.sh "+cellline+" "+biasth+" "+foldn+" "+outputdir+" "+ddatype
os.system(command)
print(command)
input_nonpeaks=os.path.join(model_path,"bias_model/newgen/filtered.bias_nonpeaks.bed")
else:
input_nonpeaks=os.path.join(model_path,"bias_model/newgen/filtered.bias_nonpeaks.bed")

if not os.path.isfile(output_path+"nonpeaks.validationset.bed.gz"):
print(output_path)
os.makedirs(output_path, exist_ok=True)
command=["python get_train_test_regions_bias.py "]+["-inp"]+[input_nonpeaks]+["-f"]+[fold]+["-o"]+[output_path]
command = " ".join(command)
print(command)
os.system(command)
else:
print(output_path)
4 changes: 3 additions & 1 deletion upload_jsons/upload_scripts/get_new_tf_model_format.py
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import argparse
from tensorflow.keras.utils import get_custom_objects
import os
from tensorflow.keras.layers import Input, Cropping1D, add, Conv1D, GlobalAvgPool1D, Dense, Add, Concatenate, Lambda, Flatten

parser = argparse.ArgumentParser(description="converting model types")
parser.add_argument("-i", "--input_model")
Expand All @@ -13,7 +14,8 @@


output_path=os.path.join(args.output_dir, args.file_path+"/")
custom_objects={"tf": tf}
#custom_objects={"tf": tf}
custom_objects={"logcount_predictions": Lambda(lambda x: tf.math.reduce_logsumexp(x, axis=-1, keepdims=True)), "tf": tf}
get_custom_objects().update(custom_objects)
model=load_model(args.input_model,compile=False)
model.save(output_path)
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6 changes: 3 additions & 3 deletions upload_jsons/upload_scripts/get_train_test_regions.py
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Expand Up @@ -15,7 +15,7 @@

peaks = pd.read_csv(args.input_peaks, sep="\t", header=None, names=NARROWPEAK_SCHEMA)
nonpeaks = pd.read_csv(args.input_nonpeaks, sep="\t", header=None, names=NARROWPEAK_SCHEMA)
nonpeaks_test = pd.read_csv(args.input_nonpeaks_test, sep="\t", header=None, names=NARROWPEAK_SCHEMA)
#nonpeaks_test = pd.read_csv(args.input_nonpeaks_test, sep="\t", header=None, names=NARROWPEAK_SCHEMA)

splits_dict=json.load(open(args.chr_fold_path))

Expand All @@ -41,7 +41,7 @@
path_nonpeaks_valid = os.path.join(args.output_path,"nonpeaks.validationset.bed.gz")
nonpeaks_valid.to_csv(path_nonpeaks_valid,sep="\t", index=False, header=False, compression='gzip')

path_nonpeaks_test = os.path.join(args.output_path,"nonpeaks.testset.bed.gz")
nonpeaks_test.to_csv(path_nonpeaks_test,sep="\t", index=False, header=False, compression='gzip')
#path_nonpeaks_test = os.path.join(args.output_path,"nonpeaks.testset.bed.gz")
#nonpeaks_test.to_csv(path_nonpeaks_test,sep="\t", index=False, header=False, compression='gzip')


35 changes: 35 additions & 0 deletions upload_jsons/upload_scripts/make_missing_bed_regions.sh
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cellline=$1
biasth=$2
foldn=$3
outputdir=$4
ddatype=$5
echo "python /mnt/lab_data2/anusri/chrombpnet/src/helpers/hyperparameters/find_bias_hyperparams.py \\
--genome=/mnt/lab_data2/anusri/chrombpnet/reference/hg38.genome.fa \\
--bigwig=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/data/$cellline"_unstranded.bw" \\
--peaks=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/data/peaks_no_blacklist.bed \\
--nonpeaks=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/negatives_data_$foldn/negatives_with_summit.be>
--outlier_threshold=0.99 \\
--chr_fold_path=/mnt/lab_data2/anusri/chrombpnet/splits/fold_$foldn.json \\
--inputlen=2114 \\
--outputlen=1000 \\
--max_jitter=0 \\
--filters=128 \\
--n_dilation_layers=4 \\
--bias_threshold_factor=$biasth \\
--output_dir $outputdir"

python /mnt/lab_data2/anusri/chrombpnet/src/helpers/hyperparameters/find_bias_hyperparams.py \
--genome=/mnt/lab_data2/anusri/chrombpnet/reference/hg38.genome.fa \
--bigwig=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/data/$cellline"_unstranded.bw" \
--peaks=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/data/peaks_no_blacklist.bed \
--nonpeaks=/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/$ddatype/$cellline/negatives_data_$foldn/negatives_with_summit.bed \
--outlier_threshold=0.99 \
--chr_fold_path=/mnt/lab_data2/anusri/chrombpnet/splits/fold_$foldn.json \
--inputlen=2114 \
--outputlen=1000 \
--max_jitter=0 \
--filters=128 \
--n_dilation_layers=4 \
--bias_threshold_factor=$biasth \
--output_dir $outputdir

19 changes: 19 additions & 0 deletions upload_jsons/upload_scripts/model_dir_dnase_v2.1_bias.csv
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fold_0,K562,/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_PE/K562/nautilus_runs_may18/K562_05.13.2022_bias_128_4_1234_0.5_fold_0/
fold_1,K562,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/K562/K562_07.17.2022_bias_128_4_1234_0.4_fold_1_data_type_DNASE_PE/
fold_2,K562,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/K562/K562_07.07.2022_bias_128_4_1234_0.5_fold_2_data_type_DNASE_PE/
fold_3,K562,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/K562/K562_07.07.2022_bias_128_4_1234_0.5_fold_3_data_type_DNASE_PE/
fold_4,K562,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/K562/K562_07.07.2022_bias_128_4_1234_0.5_fold_4_data_type_DNASE_PE/
fold_0,HEPG2,/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_PE/HEPG2/copy_HEPG2_06.08.2022_bias_128_4_1234_0.8_fold_0/
fold_1,HEPG2,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/HEPG2/HEPG2_07.13.2022_bias_128_4_1234_0.8_fold_1_data_type_DNASE_PE/
fold_2,HEPG2,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/HEPG2/HEPG2_07.07.2022_bias_128_4_1234_0.8_fold_2_data_type_DNASE_PE/
fold_3,HEPG2,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/HEPG2/HEPG2_07.07.2022_bias_128_4_1234_0.8_fold_3_data_type_DNASE_PE/
fold_4,HEPG2,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/HEPG2/HEPG2_07.07.2022_bias_128_4_1234_0.8_fold_4_data_type_DNASE_PE/
fold_0,H1ESC,/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/H1ESC/nautilus_runs_apr12/H1ESC_04.09.2022_bias_128_4_1234_0.8_fold_0/
fold_1,H1ESC,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/H1ESC/H1ESC_07.07.2022_bias_128_4_1234_0.8_fold_1_data_type_DNASE_SE/
fold_2,H1ESC,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/H1ESC/H1ESC_07.07.2022_bias_128_4_1234_0.8_fold_2_data_type_DNASE_SE/
fold_3,H1ESC,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/H1ESC/H1ESC_07.07.2022_bias_128_4_1234_0.8_fold_3_data_type_DNASE_SE/
fold_4,H1ESC,/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/chrombpnet/folds/DNASE/H1ESC/H1ESC_07.07.2022_bias_128_4_1234_0.8_fold_4_data_type_DNASE_SE/




38 changes: 38 additions & 0 deletions upload_jsons/upload_scripts/new_run_train_test_making.py
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import pandas as pd
import os

#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",", header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv",sep=",", header=None)
model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/v1/model_dir_dnase_v2.2.csv",sep=",",header=None)

#encode_id = {"K562": "ENCSR868FGK",
# "GM12878": "ENCSR637XSC",
# "HEPG2": "ENCSR291GJU",
# "IMR90": "ENCSR200OML",
# "H1ESC": "ENCDUMMY"}

encode_id = {"K562": "ENCSR000EOT",
"GM12878": "ENCSR000EMT",
"HEPG2": "ENCSR149XIL",
"IMR90": "ENCSR477RTP",
"H1ESC": "ENCSR000EMU"}


for i,r in model_atac.iterrows():
fold = r[0]
name = r[1]
model_path = r[2]

input_peaks=os.path.join(model_path,"filtered.peaks.bed")
input_nonpeaks=os.path.join(model_path,"filtered.nonpeaks.bed")
test_nonpeaks="/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/ATAC/"+encode_id[name]+"/negatives_data/test/test."+fold+".filtered.negatives_with_summit.bed"
fold="/mnt/lab_data2/anusri/chrombpnet/splits/"+fold+".json"
output_path=os.path.join(model_path,"train_test_regions_may_7_2024/")

if not os.path.isfile(output_path+"nonpeaks.validationset.bed.gz"):
os.makedirs(output_path, exist_ok=True)
command=["python get_train_test_regions.py -ip"]+[input_peaks]+["-inp"]+[input_nonpeaks]+["-inpt"]+[test_nonpeaks]+["-f"]+[fold]+["-o"]+[output_path]
command = " ".join(command)
print(command)
os.system(command)

29 changes: 18 additions & 11 deletions upload_jsons/upload_scripts/run_conversion.py
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import pandas as pd
import os

model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/v1/model_dir_dnase_v2.1.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/v1/model_dir_subsample_atac.csv",sep=",",header=None)
model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/upload_jsons/upload_scripts/model_dir_dnase_v2.1_bias.csv", sep=",", header=None)



for i,r in model_atac.iterrows():
fold = r[0]
name = r[1]
#model_path = r[3]
model_path = r[2]

input_path=os.path.join(model_path,"chrombpnet_model/chrombpnet.h5")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats/chrombpnet")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/chrombpnet")
output_dir=os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/")
print(output_path)
if not os.path.isfile(output_path+".tar"):

os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=5 python get_new_tf_model_format.py -i "+input_path+" -o "+output_path
os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f chrombpnet"
print(command)
os.system(command)

input_path=os.path.join(model_path,"chrombpnet_model/chrombpnet_wo_bias.h5")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats/chrombpnet_wo_bias")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/chrombpnet_wo_bias")

if not os.path.isfile(output_path+".tar"):
os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=5 python get_new_tf_model_format.py -i "+input_path+" -o "+output_path
os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f chrombpnet_wo_bias"
print(command)
os.system(command)

input_path=os.path.join(model_path,"chrombpnet_model/bias_model_scaled.h5")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats/bias_model_scaled")
output_path=os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/bias_model_scaled")

if not os.path.isfile(output_path+".tar"):
os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=5 python get_new_tf_model_format.py -i "+input_path+" -o "+output_path
os.makedirs(os.path.join(model_path,"chrombpnet_model/new_model_formats_may_7_24/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f bias_model_scaled"
print(command)
os.system(command)


18 changes: 13 additions & 5 deletions upload_jsons/upload_scripts/run_conversion_bias.py
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import pandas as pd
import os
import tensorflow as tf
from keras.models import load_model

#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",",header=None)
model_atac = pd.read_csv("bias_models_atlas.csv",sep=",",header=None)
model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",",header=None)
#model_atac = pd.read_csv("bias_models_atlas.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv",sep=",",header=None)
#model_atac = pd.read_csv("model_dir_dnase_v2.1_bias.csv",sep=",",header=None)


for i,r in model_atac.iterrows():
fold = r[0]
name = r[1]
model_path = r[2]
input_path=os.path.join(model_path,"bias_model/bias.h5")
output_path=os.path.join(model_path,"bias_model/new_model_formats_v1/bias")
output_dir=os.path.join(model_path,"bias_model/new_model_formats_v1/")
output_path=os.path.join(model_path,"bias_model/new_model_formats_vf/bias")
output_dir=os.path.join(model_path,"bias_model/new_model_formats_vf/")
file_path="bias"
if not os.path.isfile(input_path):
#if "IMR90_07.17.2022_bias_128_4_1234_0.3_fold_1_data_type_ATAC_PE" in input_path:
# continue
print("ERROR bias model not found")
print(input_path)
#ppath=os.path.join(model_path,"bias_model/new_model_formats/bias/")
#modelf = tf.keras.models.load_model(ppath)
#modelf.save(input_path)
continue

print(output_path)
if not os.path.isfile(output_path+".tar"):

os.makedirs(os.path.join(model_path,"bias_model/new_model_formats_v1/"), exist_ok=True)
os.makedirs(os.path.join(model_path,"bias_model/new_model_formats_vf/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=2 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f "+file_path
print(command)
os.system(command)
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50 changes: 50 additions & 0 deletions upload_jsons/upload_scripts/run_conversion_new.py
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import pandas as pd
import os

#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv",sep=",",header=None)
#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv",sep=",",header=None)

#model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/v1/model_dir_dnase_v2.2.csv",sep=",",header=None)
model_atac = pd.read_csv("/mnt/lab_data2/anusri/chrombpnet/upload_jsons/upload_scripts/model_dir_dnase_v2.1_bias.csv", sep=",", header=None)

for i,r in model_atac.iterrows():
fold = r[0]
name = r[1]
model_path = r[2]
#input_path=os.path.join(model_path,"new_model_formats_may_7_24/chrombpnet/")
#input_path=os.path.join(model_path,"chrombpnet/")
input_path=os.path.join(model_path,"chrombpnet_model/chrombpnet.h5")

output_path=os.path.join(model_path,"new_model_formats_may_7_24_vf/chrombpnet")
output_dir=os.path.join(model_path,"new_model_formats_may_7_24_vf/")
print(output_path)
if not os.path.isfile(output_path+".tar"):

os.makedirs(os.path.join(model_path,"new_model_formats_may_7_24_vf/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f chrombpnet"
print(command)
os.system(command)

#input_path=os.path.join(model_path,"chrombpnet_wo_bias.h5")
input_path=os.path.join(model_path,"chrombpnet_model/chrombpnet_wo_bias.h5")

output_path=os.path.join(model_path,"new_model_formats_may_7_24_vf/chrombpnet_wo_bias")

if not os.path.isfile(output_path+".tar"):
os.makedirs(os.path.join(model_path,"new_model_formats_may_7_24_vf/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f chrombpnet_wo_bias"
print(command)
os.system(command)

#input_path=os.path.join(model_path,"bias_model_scaled.h5")
input_path=os.path.join(model_path,"chrombpnet_model/bias_model_scaled.h5")

output_path=os.path.join(model_path,"new_model_formats_may_7_24_vf/bias_model_scaled")

if not os.path.isfile(output_path+".tar"):
os.makedirs(os.path.join(model_path,"new_model_formats_may_7_24_vf/"), exist_ok=True)
command = "CUDA_VISIBLE_DEVICES=1 python get_new_tf_model_format.py -i "+input_path+" -o "+output_dir+" -f bias_model_scaled"
print(command)
os.system(command)


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