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###
#######
#processing of datasets
######
## TCGA dataset
dataset_ssgsea = "/liulab/asahu/data/ssgsea/xiaoman/TCGA_ssgsva.txt"
dataset_ssgsea = "/liulab/asahu/data/ssgsea/xiaoman/TCGA_ALLTPM.txt"
pathway_order = "/liulab/asahu/data/ssgsea/xiaoman/ssgsea.order_tcga.txt"
dataset_phenotype = "/liulab/asahu/data/ssgsea/xiaoman/tcga_biom_oxphos.txt"
phenotype_order = "/liulab/asahu/data/ssgsea/xiaoman/processed/tcga_phenotypes.RData"
output.dir = "~/project/deeplearning/icb/data/tcga/PCA/"
output.dir = "~/project/deeplearning/icb/data/tcga.blca/neoantigen/"
# ICB dataset
dataset_ssgsea = "/liulab/asahu/data/ssgsea/xiaoman/Avin/ICB_GSVA.txt"
dataset_ssgsea = "/liulab/asahu/data/ssgsea/xiaoman/Genetech_expression_TPM.txt"
pathway_order = "/liulab/asahu/data/ssgsea/xiaoman/ssgsea.order_tcga.txt"
dataset_phenotype = "/liulab/asahu/data/ssgsea/xiaoman/Avin/clinical_ICB_oxphos.txt"
phenotype_order = "/liulab/asahu/data/ssgsea/xiaoman/processed/tcga_phenotypes.RData"
output.dir = "~/project/deeplearning/icb/data/pancancer_all_immune/all_icb"
output.dir = "~/project/deeplearning/icb/data/pancancer_all_immune/genetech"
output.dir = "~/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed"
output.dir = "~/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/pca/"
output.dir = "~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/"
dataset.prefix = "Mel_CTLA4_Chiappinelli"
dataset.prefix = "mGC_PD1_Kim"
dataset.prefix = "RCC_PD1_Miao"
# dataset_ssgsea = sprintf("/liulab/asahu/data/ssgsea/xiaoman/expression/%s.expression",dataset.prefix)
dataset_ssgsea = sprintf("/liulab/asahu/data/ssgsea/xiaoman/expression/annot/%s.annot",dataset.prefix)
output.dir = sprintf("~/project/deeplearning/icb/data/%s", dataset.prefix)
pca_obj.RData = "/homes6/asahu/project/deeplearning/icb/data/tcga.blca/neoantigen/pca_obj.RData"
fix_patient_name =F; ICB_dataset =T
# precog dataset
dataset_ssgsea = "/liulab/asahu/data/ssgsea/xiaoman/Avin/Precog_GSVA.txt"
pathway_order = "/liulab/asahu/data/ssgsea/xiaoman/ssgsea.order_tcga.txt"
dataset_phenotype = "/liulab/asahu/data/ssgsea/xiaoman/Avin/clinical_Precog_oxphos.txt"
phenotype_order = "/liulab/asahu/data/ssgsea/xiaoman/processed/tcga_phenotypes.RData"
output.dir = "~/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm"
output.dir = "~/project/deeplearning/icb/data/tcga.blca/tcga.blca.pca/"
#######
######
#######
######
#######
######
#######
######
data_dir="/homes6/asahu/project/deeplearning/icb/data/ssgsea/datasets/"
model_dir="/homes6/asahu/project/deeplearning/icb/data/ssgsea/models/"
# python train.py --data_dir ~/Dropbox/project/code/deeplearning/icb/results/simulation/datasets
python train.py --data_dir ~/Dropbox/project/code/deeplearning/icb/results/simulation5jan/datasets --prefix "tcga"
python train.py --data_dir ~/Dropbox/project/code/deeplearning/icb/results/simulation15Feb/datasets_list.txt --model_dir ~/Dropbox/project/code/deeplearning/icb/results/simulation15Feb/
python train.py --data_dir $data_dir --model_dir $model_dir
python train.py --data_dir ~/Dropbox/project/code/deeplearning/icb/results/simulation5jan/datasets_list.txt --prefix "tcga"
# simulation from pixie
python train.py --data_dir ../results/simulation5Feb/datasets_list.txt --model_dir ../results/simulation5Feb/
python train.py --prefix tcga --data_dir ~/local_data/processed/datasets/ --model_dir /homes6/asahu/project/deeplearning/icb/deepImmune/experiments/tcga_43
ipython -pdb train.py --prefix tcga --data_dir ~/local_data/processed/datasets/ --model_dir /homes6/asahu/project/deeplearning/icb/deepImmune/experiments/tcga_43
python train.py --prefix tcga --data_dir ~/local_data/processed/datasets/ --model_dir /homes6/asahu/project/deeplearning/icb/deepImmune/experiments/tcga_embedding8
python train.py --prefix tcga --data_dir experiments/tcga_feb9/datasets_list.txt --model_dir experiments/tcga_feb9
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/ --restore_file tcga_survial
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/survival/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/survival/ --restore_file tcga_survial
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_icb/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_icb/
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_icb/icb/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_icb/icb/ --restore_file tcga_icb_last
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_only/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_only/
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_only/icb_survival/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/tcga_only/icb_survival/ --restore_file intialize
#precog
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/
#tcga
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/combined/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/combined/
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/tcga.blca/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/tcga.blca
python train.py --data_dir ../data/tcga.blca/genentech.pca/datasets_list.txt --model_dir ../data/tcga.blca/genentech.pca --restore_file ../data/tcga.blca/pca/tensorboardLog/20190307-140948/best.pth.tar
python evaluate.py --data_dir /Users/avi/Dropbox/project/code/deeplearning/icb/data/tcga.blca/genentech.pca/eval/datasets_list.txt --model_dir /Users/avi/Dropbox/project/code/deeplearning/icb/data/tcga.blca/pca/tensorboardLog/20190307-140948/ --restore_file /Users/avi/Dropbox/project/code/deeplearning/icb/data/tcga.blca/pca/tensorboardLog/20190307-140948/best.pth.tar
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/ --restore_file ~/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/last.best.pthr.tar
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/pretrain_tcga/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/pancancer_all_immune/genetech.imputed.same.survival/pretrain_tcga/
--restore_file ~/project/deeplearning/icb/data/pancancer_all_immune/precog.oxphos/norm/tcga/my.best.pth.tar
python train.py --data_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/tcga_genentech/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/tcga_genentech/
python evaluate.py --data_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/tcga_genentech/datasets_list.txt --model_dir /homes6/asahu/project/deeplearning/icb/data/tcga.oxphos/tcga_genentech/ --restore_file best
python train.py --data_dir ../data/genentech.tpm/datasets_list.txt --model_dir ../data/genentech.tpm/. --tensorboard_prefix only_auc_
python train.py --data_dir ../data/genentech.tpm/genentech.pca.tpm.phenotypes.32/datasets_list.txt --model_dir ../data/genentech.tpm/genentech.pca.tpm.phenotypes/ --tensorboard_prefix "32_"
--restore_file ../data/genentech.tpm/genentech.pca.phenotypes/tensorboardLog/surv_20190310-214230/best.pth.tar
## Neoantigen
python train.py --data_dir ../data/tcga/PCA/datasets_list.txt --model_dir ../data/tcga/PCA/.
python evaluate.py --data_dir ../data/tcga/PCA/datasets_list.txt --model_dir ../data/tcga/PCA/. --restore_file ../data/tcga/PCA/./tensorboardLog/20190322-014624/best.pth.tar
python evaluate.py --data_dir ../data/tcga/PCA/prediction_genentech/datasets_list.txt --model_dir ../data/tcga/PCA/prediction_genentech/. --restore_file ../data/tcga/PCA/./tensorboardLog/20190322-014624/best.pth.tar
#Neoantigen in BCB
python train.py --data_dir ../data/tcga.blca/neoantigen/datasets_list.txt --model_dir ../data/tcga.blca/neoantigen/.
python evaluate.py --data_dir ../data/tcga/PCA/datasets_list.txt --model_dir ../data/tcga/PCA/. --restore_file ../data/tcga/PCA/tensorboardLog/20190315-150358/best.pth.tar
python evaluate.py --data_dir ../data/genentech.tpm/Neoantigen/evaluate/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen/. --restore_file ../data/tcga/PCA/tensorboardLog/20190315-150358/best.pth.tar
python train.py --data_dir ../data/genentech.tpm/Neoantigen/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen/. --restore_file ../data/tcga/PCA/tensorboardLog/20190315-150358/best.pth.tar
python train.py --data_dir ../data/genentech.tpm/Neoantigen/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen/. --restore_file ../data/tcga/PCA/tensorboardLog/20190315-150358/best.pth.tar --tensorboard_prefix pretrain_tcga_
## Response prediction
python train.py --data_dir ../data/genentech.tpm/Neoantigen.imputed/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen.imputed/. --tensorboard_prefix auc_
python train.py --data_dir ../data/genentech.tpm/Neoantigen.imputed/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen.imputed/. --tensorboard_prefix auc_neo_ml_ --hyper_param "input_indices=range(50)"
python -m ipdb train.py --data_dir ../data/genentech.tpm/Neoantigen.imputed/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen.imputed/. --tensorboard_prefix outputs_10_ --hyper_param "params.binary_phenotype_indices=[45,1,44,47,46,41];params.continuous_phenotype_indices=[0,42,43,49,50]"
python -m ipdb train.py --data_dir ../data/genentech.tpm/Neoantigen.imputed/datasets_list.txt --model_dir ../data/genentech.tpm/Neoantigen.imputed/. --tensorboard_prefix outputs_10_ --hyper_param "params.continuous_phenotype_indices=[0,42,43,49,50]"
# run run_hyper.R with ii = 57
## mGC_PD1_Kim
python evaluate.py \
--data_dir ../data/mGC_PD1_Kim/datasets_list.txt \
--model_dir ../data/mGC_PD1_Kim/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.binary_phenotype_indices=[42,43,45,1,44,47,46,41];params.continuous_phenotype_indices=[0,50,49,55,54,56,53,17,51,52,29,13,14,31,10];params.input_indices=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,60,56]"
# imputing neoantigen etc. from tcga model
python evaluate.py \
--data_dir ../data/mGC_PD1_Kim/Neoantigen/datasets_list.txt \
--model_dir ../data/mGC_PD1_Kim/Neoantigen/. \
--restore_file ../data/tcga/PCA/tensorboardLog/20190322-015246/best.pth.tar
python evaluate.py \
--data_dir ../data/mGC_PD1_Kim/imputed/datasets_list.txt \
--model_dir ../data/mGC_PD1_Kim/imputed/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.binary_phenotype_indices=[42,43,45,1,44,47,46,41];params.continuous_phenotype_indices=[0,50,49,55,54,56,53,17,51,52,29,13,14,31,10];params.input_indices=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,60,56]"
## RCC_PD1_Miao
python evaluate.py \
--data_dir ../data/RCC_PD1_Miao/datasets_list.txt \
--model_dir ../data/RCC_PD1_Miao/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar
# Mel_CTLA4_VanAllen
python evaluate.py \
--data_dir ../data/Mel_CTLA4_VanAllen/datasets_list.txt \
--model_dir ../data/Mel_CTLA4_VanAllen/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.cuda=False"
python evaluate.py \
--data_dir ../data/Mel_CTLA4_VanAllen/Neoantigen/datasets_list.txt \
--model_dir ../data/Mel_CTLA4_VanAllen/Neoantigen/. \
--restore_file ../data/tcga/PCA/tensorboardLog/20190322-015246/best.pth.tar
python evaluate.py \
--data_dir ../data/Mel_CTLA4_VanAllen/imputed/datasets_list.txt \
--model_dir ../data/Mel_CTLA4_VanAllen/imputed/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.binary_phenotype_indices=[42,43,45,1,44,47,46,41];params.continuous_phenotype_indices=[0,50,49,55,54,56,53,17,51,52,29,13,14,31,10];params.input_indices=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,60,56]"
# Mel_CTLA4_VanAllen
python evaluate.py \
--data_dir ../data/Mel_PD1_Hugo/datasets_list.txt \
--model_dir ../data/Mel_PD1_Hugo/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.cuda=False"
python evaluate.py \
--data_dir ../data/Mel_PD1_Hugo/Neoantigen/datasets_list.txt \
--model_dir ../data/Mel_PD1_Hugo/Neoantigen/. \
--restore_file ../data/tcga/PCA/tensorboardLog/20190322-015246/best.pth.tar
## cancer type specific prediction
## Note TCGA data is being used in the code
##
# imputing neoantigen etc. from tcga model
python evaluate.py \
--data_dir ../data/tcga/PCA/Neoantigen/datasets_list.txt \
--model_dir ../data/tcga/PCA/Neoantigen/. \
--restore_file ../data/tcga/PCA/tensorboardLog/20190322-015246/best.pth.tar
python evaluate.py \
--data_dir ../data/tcga/PCA/imputed/datasets_list.txt \
--model_dir ../data/tcga/PCA/imputed/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.binary_phenotype_indices=[42,43,45,1,44,47,46,41];params.continuous_phenotype_indices=[0,50,49,55,54,56,53,17,51,52,29,13,14,31,10];params.input_indices=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,60,56]"
## genetech response prediction
python evaluate.py \
--data_dir ../data/genentech.tpm/Neoantigen.imputed/evaluate/datasets_list.txt \
--model_dir ../data/genentech.tpm/Neoantigen.imputed/evaluate/. \
--restore_file ~/project/deeplearning/icb/data/genentech.tpm/Neoantigen.imputed/tensorboardLog/output_22/inps_61_5720190321-104715/best.pth.tar \
--hyper_param "params.binary_phenotype_indices=[42,43,45,1,44,47,46,41];params.continuous_phenotype_indices=[0,50,49,55,54,56,53,17,51,52,29,13,14,31,10];params.input_indices=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,60,56]"
## version > 0.3
# ../data/tcga/neoantigen.v2
python train.py --data_dir ../data/tcga/neoantigen.v2/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/. --tensorboard_prefix msi_only_
python train.py --data_dir ../data/ya/datasets_list.txt --model_dir ../data/ya/.
# attention model
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/.
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/. --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/20190407-213630/best.pth.tar
python evaluate_attention.py --data_dir ../data/genentech.tpm/neoantigen.v2/impute/datasets_list.txt --model_dir ../data/genentech.tpm/neoantigen.v2/impute/. --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/20190412-151621/best.pth.tar
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/.
python train.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/.
## ya's model
python train.py --data_dir ../data/ya/datasets_tsne_list.txt --model_dir ../data/ya/.
## imputation
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/. --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/20190422-080613/best.pth.tar --type_file="['']"
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/survival20190422-081755/datasets_impute_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/survival20190422-081755/. --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/survival20190422-081755/best.pth.tar --type_file="all"
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/genentech.imputed/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/genentech.imputed/.
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/. --restore_file ~/Dropbox/project/code/deeplearning/icb/data/tcga/neoantigen.v2/attention/tcga.imputed/tensorboardLog/SGD_20190426-131226//best.pth.tar
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/. --restore_file ~/Dropbox/project/code/deeplearning/icb/data/tcga/neoantigen.v2/attention/tcga.imputed/tensorboardLog/SGD_20190426-131226//best.pth.tar
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/. --restore_file ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/best.pth.tar
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/datasets_genentech_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/. --restore_file ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/best.pth.tar
# kindney
python train.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/. --tensorboard_prefix new_residual_
python -m ipdb evaluate.py --data_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/best.pth.tar
python -m ipdb evaluate.py --data_dir ../data/RCC_PD1_Miao/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/best.pth.tar
python evaluate.py --data_dir ../data/RCC_PD1_Miao/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/best.pth.tar
python evaluate.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/datasets_genentech_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190515-114038/best.pth.tar
## evaluating kidney cancer
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/drug/.
python train_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/.
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/. --restore_file ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/tensorboardLog/three_var_20190622-191219/best.pth.tar
python evaluate_attention.py --data_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/. --restore_file ../data/tcga/neoantigen.v2/attention/tcga.imputed/brca/tensorboardLog/20190623-230228/best.pth.tar
# scrRNA
python train.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/.
python train.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/. --tensorboard_prefix restored_ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-233202/best.pth.tar
python evaluate.py --data_dir ../data/tcga/neoantigen.v2/attention/datasets_val_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-041025/best.pth.tar
python evaluate.py --data_dir ../data/Getz_scRNA/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-041025/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-041025/best.pth.tar
# in pixie
python evaluate.py --data_dir ../data/Getz_scRNA/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-233202/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/new_residual_20190625-233202/best.pth.tar
python evaluate.py --data_dir ../data/Getz_scRNA/datasets_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/tensorboardLog/restored_20190627-185716/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/restored_20190627-185716/best.pth.tar
dca /homes6/asahu/project/deeplearning/icb/data/Getz_scRNA/data/count.mat.txt /homes6/asahu/project/deeplearning/icb/data/Getz_scRNA/data/dca_impute/
dca ~/project/deeplearning/icb/data/sc/GSE123814_human_aPD1/dca/count.mat.txt ~/project/deeplearning/icb/data/sc/GSE123814_human_aPD1/dca/dca_impute
# tar -xf /rna-seq/dbGAP_tar/Post_P16_CD45+.tar --wildcards --no-anchored '*.tab' -C /rna-seq/counts/.
# scRNA version deepImmune
python train.py --data_dir ../data/tcga//scrna.v1/datasets_tsne_list.txt --model_dir ../data/tcga//scrna.v1/.
python evaluate.py --data_dir ../data/Getz_scRNA/dca/datasets_list.txt --model_dir ../data/Getz_scRNA/dca/. --restore_file ../data/tcga/scrna.v1/saved_model/20190715-115243/best.pth.tar
python evaluate.py --data_dir ../data/tcga//scrna.v1/datasets_test_list.txt --model_dir ../data/tcga/scrna.v1/saved_model/20190715-115243/ --restore_file ../data/tcga/scrna.v1/saved_model/20190715-115243/best.pth.tar
python train.py --data_dir ../data/tcga//scrna.v1/datasets_tsne_list.txt --model_dir ../data/tcga//scrna.v1/. --restore_file ../data/tcga/scrna.v1/tensorboardLog/20190715-115243/best.pth.tar --tensorboard_prefix boosting_
python train.py --data_dir ../data/tcga//scrna.v2/datasets_tsne_list.txt --model_dir ../data/tcga//scrna.v2/. --restore_file ../data/tcga/scrna.v2/tensorboardLog/20190717-195353/best.pth.tar --tensorboard_prefix boosting_
python train.py --data_dir ../data/tcga//scrna.v2/datasets_tsne_list.txt --model_dir ../data/tcga//scrna.v2/. --tensorboard_prefix indel_only_1ouptput_
python train.py --data_dir ../data/tcga//scrna.v3/datasets_tsne_list.txt --model_dir ../data/tcga//scrna.v3/.
python train.py --data_dir ../data/tcga/scrna.v4.pcs/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.pcs/.
python train.py --data_dir ../data/tcga/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/. --tensorboard_prefix no_pipeline_
python train.py --data_dir ../data/tcga/scrna.v4.allgenes.nopcs/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.allgenes.nopcs/. --tensorboard_prefix no_pipeline_
python evaluate.py --data_dir ../data/Getz_scRNA/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/best.pth.tar
python evaluate.py --data_dir ../data/tcga/scrna.v4.genes/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/best.pth.tar
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/Getz_scRNA/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/epoch-52.pth.tar
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/tcga/scrna.v4.genes/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/epoch-52.pth.tar
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/oxphos/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/no_pipeline_20190724-201216/epoch-52.pth.tar
### TCR Amino acid training
CUDA_VISIBLE_DEVICES=1 python train.py --data_dir ../data/Getz_scRNA/TCR.AA/datasets_tsne_list.txt --model_dir ../data/Getz_scRNA/TCR.AA/. --tensorboard_prefix emb_n_immuneFactors_
python evaluate.py --data_dir ../data/Getz_scRNA/TCR.AA/datasets_test_list.txt --model_dir ../data/Getz_scRNA/TCR.AA/autoencoder_20190820-140204/. --restore_file ../data/Getz_scRNA/TCR.AA/tensorboardLog/autoencoder_20190820-140204/best.pth.tar
# with VAE
# mac
python train.py --data_dir ../data/tcga/neoantigen.v2/attention//datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/
python evaluate.py --data_dir ../data/tcga/neoantigen.v2/attention//datasets_test_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/ --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/20200222-154343/best.pth.tar
python evaluate.py --data_dir ../data/tcga/neoantigen.v2/attention//datasets_test_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/params.json --restore_file ../data/tcga/neoantigen.v2/attention/tensorboardLog/20190822-142707/best.pth.tar
python train.py --data_dir ../data/tcga/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/. --tensorboard_prefix nopipeline_vae_
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/Getz_scRNA/scrna.v4.genes/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/nopipeline_vae_20190819-161104/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/nopipeline_vae_20190819-161104/epoch-142.pth.tar
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/tcga/scrna.v4.genes/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/tensorboardLog/nopipeline_vae_20190819-161104/. --restore_file ../data/tcga/scrna.v4.genes/tensorboardLog/nopipeline_vae_20190819-161104/epoch-142.pth.tar
## tcr tcga
CUDA_VISIBLE_DEVICES=2 python train.py --data_dir ../data/tcga/scrna.v4.genes/TCR.AA/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA/.
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100/.
CUDA_VISIBLE_DEVICES=1 python train.py --data_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/datasets_tsne_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/.
## evaluate
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/. --restore_file ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/tensorboardLog/20190823-014612/epoch-10.pth.tar
CUDA_VISIBLE_DEVICES=2 python evaluate.py --data_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/. --restore_file ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/tensorboardLog/20190826-152330/epoch-5.pth.tar
### TCR Amino acid training
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ../data/Getz_scRNA/TCR.AA.V2/datasets_tsne_list.txt --model_dir ../data/Getz_scRNA/TCR.AA.V2/. --tensorboard_prefix all_
CUDA_VISIBLE_DEVICES=3 python evaluate.py --data_dir ../data/Getz_scRNA/TCR.AA.V2/datasets_test_list.txt --model_dir ../data/Getz_scRNA/TCR.AA.V2/. --restore_file ../data/Getz_scRNA/TCR.AA.V2/tensorboardLog/all_20190827-110811/epoch-332.pth.tar
CUDA_VISIBLE_DEVICES=3 python evaluate.py --data_dir ../data/Getz_scRNA/TCR.AA.V2/datasets_test_list.txt --model_dir ../data/Getz_scRNA/TCR.AA.V2/. --restore_file ../data/Getz_scRNA/TCR.AA.V2/tensorboardLog/all_small_20190828-175706/epoch-20.pth.tar
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ../data/Getz_scRNA/TCR.AA.patient.independent/datasets_tsne_list.txt --model_dir ../data/Getz_scRNA/TCR.AA.patient.independent/.
CUDA_VISIBLE_DEVICES=3 python evaluate.py --data_dir ../data/Getz_scRNA/TCR.AA.patient.independent/datasets_test_list.txt --model_dir ../data/Getz_scRNA/TCR.AA.patient.independent/tensorboardLog/20190829-102554/params.json --restore_file ../data/Getz_scRNA/TCR.AA.patient.independent/tensorboardLog/20190829-102554/epoch-28.pth.tar
## learn on tcga and test on scRNA
CUDA_VISIBLE_DEVICES=3 python evaluate.py --data_dir ../data/Getz_scRNA/TCR.AA.patient.independent/datasets_test_list.txt --model_dir ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/tensorboardLog/20190826-152330/params.getz.json --restore_file ../data/tcga/scrna.v4.genes/TCR.AA.top100.nonintersect/tensorboardLog/20190826-152330/epoch-497.pth.tar
## runnning protien encoder
CUDA_VISIBLE_DEVICES=2 python train_similarity.py --lm /liulab/asahu/projects/icb/data/pretrained/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav --batch-size 16 -d -1
CUDA_VISIBLE_DEVICES=3 python eval_transmembrane.py /liulab/asahu/projects/icb/data/pretrained/pretrained_models/ssa_L1_100d_lstm3x512_lm_i512_mb64_tau0.5_lambda0.1_p0.05_epoch100.sav
CUDA_VISIBLE_DEVICES=3 python -m ipdb train_similarity100_temp.py --lm /liulab/asahu/projects/icb/data/pretrained/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav -d -1
python -m ipdb mytrain_lm_pfam.py --lm /liulab/asahu/projects/icb/data/pretrained/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav --hidden-dim 1024 -d -1
# b /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/mytrain_lm_pfam.py:162
# c
# CUDA_VISIBLE_DEVICES=3 python eval_pfam.py --lm /liulab/asahu/projects/icb/data/pretrained/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav -f /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/data/Getz/getz.cdr3.fa
CUDA_VISIBLE_DEVICES=3 python eval_pfam.py --models_dir /liulab/asahu/projects/icb/data/pretrained/pretrained_models/ -f /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/data/Getz/getz.cdr3.fa
python -m ipdb eval_pfam.py --models_dir /liulab/asahu/projects/icb/data/pretrained/pretrained_models/ -f /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/data/Getz/getz.cdr3.100.fa
python -m ipdb train_lm_cdr3b.py --lm /liulab/asahu/projects/icb/data/pretrained/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav --train /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/data/Getz/getz.cdr3.100.fa --test /liulab/asahu/softwares/protein-sequence-embedding-iclr2019/data/Getz/getz.cdr3.100.fa -d -1
python train_lm_cdr3b.py --train ../protein-sequence-embedding-iclr2019/data/pfam/Pfam-A100.train.fasta --test ../protein-sequence-embedding-iclr2019/data/pfam/Pfam-A100.train.fasta --lm ../protein-sequence-embedding-iclr2019/pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav
python train_lm_cdr3b.py --train data/tcga/tcga.train.cdr3.fa --test data/tcga/tcga.train.cdr3.fa --lm pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav
python train_lm_cdr3b.py --train data/tcga/tcga.train.cdr3.fa --test data/tcga/tcga.train.cdr3.fa --lm pretrained_models/pfam_lm_lstm2x1024_tied_mb64.sav -d -1 --hidden-dim 32 --save-prefix pretrained_models/tcga/cdr3b --num-epochs 100
python train_lm_cdr3b.py --train data/tcga/tcga.train.cdr3.fa --test data/tcga/tcga.train.cdr3.fa --lm pretrained_models/me_L1_100d_lstm3x512_lm_i512_mb64_tau0.5_p0.05_epoch100.sav -d 2 --hidden-dim 32 --save-prefix pretrained_models/tcga/cdr3b_me_L1_100d_lstm3x512 --num-epochs 100
python train_lm_cdr3b.py --train data/tcga/tcga.train.cdr3.fa --test data/tcga/tcga.train.cdr3.fa -d 2 --hidden-dim 32 --save-prefix pretrained_models/tcga/cdr3b_no_pretrain --num-epochs 100
python mytrain_lm_pfam.py --train data/tcga/tcga.train.cdr3.fa --test data/tcga/tcga.train.cdr3.fa -d 1 --hidden-dim 512 --save-prefix pretrained_models/tcga/mypfam_no_pretrain --num-epochs 100
python eval_pfam.py --models_dir pretrained_models/runs/ -f data/Getz/getz.cdr3.fa -d -1
#citokines
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/citokines.v1/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/citokines.v1/.
CUDA_VISIBLE_DEVICES=2 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/citokines.v1/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/citokines.v1/. --dont_save_every_epoch --hyper_param "params.loss_excluded_from_training=[-1,-2]; params.save_tsne_figs=False; params.learning_rate=1e-5; params.num_epochs=10" --restore_file ~/project/deeplearning/icb/data/tcga/citokines.v1/tensorboardLog/20190924-011458/best.pth.tar --tensorboard_prefix 20190924-011458/personalization/surv12_
python train.py --data_dir ../data/tcga/neoantigen.v2/attention//datasets_tsne_list.txt --model_dir ../data/tcga/neoantigen.v2/attention/ --dont_save_every_epoch --hyper_param "params.loss_excluded_from_training=[-1,-2,-3]"
## cytokines genentech data.
python evaluate.py --data_dir ../data/genentech.tpm/citokines.v1/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/citokines.v1/tensorboardLog/20190924-011458/params.json --restore_file ~/project/deeplearning/icb/data/tcga/citokines.v1/tensorboardLog/20190924-011458/best.pth.tar
# all genes
python train.py --data_dir ~/project/deeplearning/icb/data/tcga/all.genes.v2/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/all.genes.v2/.
python evaluate.py --data_dir ../data/genentech.tpm/all.genes.v2/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/all.genes.v2/tensorboardLog/20191116-183310/params.json --restore_file ~/project/deeplearning/icb/data/tcga/all.genes.v2/tensorboardLog/20191116-183310/epoch-108.pth.tar
# deepimmune out of all genes to train genentech
python train.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/deepImmune.out/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/genentech.tpm/deepImmune.out/.
## deepEss
CUDA_VISIBLE_DEVICES=2 python train.py --data_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/debug/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/debug/.
CUDA_VISIBLE_DEVICES=4 python train.py --data_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/small/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/small/.
CUDA_VISIBLE_DEVICES=2 python train.py --data_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/deepEss/exp.go.coexp/.
#citokines
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/scrna.v4.genes/simple/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/scrna.v4.genes/simple/.
#TF
CUDA_VISIBLE_DEVICES=2 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/tf/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf/. --tensorboard_prefix bottleneck
# predicion without fine-tunning
CUDA_VISIBLE_DEVICES=1 python evaluate.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf/tensorboardLog/bottleneck20200206-101221/. --restore_file ~/project/deeplearning/icb/data/tcga/tf/tensorboardLog/bottleneck20200206-101221/best.pth.tar --hyper_param "params.add_noise=0"
# finetune
CUDA_VISIBLE_DEVICES=1 python train.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/. --tensorboard_prefix fine_tune --restore_file ~/project/deeplearning/icb/data/tcga/tf/tensorboardLog/bottleneck20200206-101221/best.pth.tar
CUDA_VISIBLE_DEVICES=1 python evaluate.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/tensorboardLog/fine_tune20200213-061915/params.json --restore_file ~/project/deeplearning/icb/data/genentech.tpm/tf/tensorboardLog/fine_tune20200213-061915/best.pth.tar --hyper_param "params.add_noise=0"
# test normalizations
CUDA_VISIBLE_DEVICES=3 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/tf/binarize/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf/binarize/. --tensorboard_prefix surv_noaug_bin_
CUDA_VISIBLE_DEVICES=1 python evaluate.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/binarize/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/surv_noaug_20200218-011346/. --restore_file ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/surv_noaug_20200218-011346/epoch-70.pth.tar
cp ../data/genentech.tpm/tf/binarize//val_prediction.csv ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/surv_noaug_20200218-011346/genentech_70.csv
CUDA_VISIBLE_DEVICES=1 python evaluate.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/binarize/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/Neo_20200217-233533/. --restore_file ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/Neo_20200217-233533/last.pth.tar
mv ../data/genentech.tpm/tf/binarize//val_prediction.csv ~/project/deeplearning/icb/data/tcga/tf/binarize/tensorboardLog/Neo_20200217-233533/genentech_100.csv
## finetune neoantigen genentech data.
CUDA_VISIBLE_DEVICES=1 python evaluate.py --data_dir ~/project/deeplearning/icb/data/genentech.tpm/tf/binarize/datasets_test_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/Neo_20200217-233533/. --restore_file ~/project/deeplearning/icb/data/tcga/tf//binarize/tensorboardLog/Neo_20200217-233533/last.pth.tar
## gamma-delta t-cells.
# see gdt project directory
CUDA_VISIBLE_DEVICES=1 python eval_pfam.py --models_dir /liulab/asahu/projects/icb/data/pretrained/pretrained_models/ -f ~/liulab_home/softwares/protein-sequence-embedding-iclr2019/data/tcga/for.gdt.cdr3.fa -d -1
## For drugInducedtranscripts.Rmd
CUDA_VISIBLE_DEVICES=1 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/nanostring/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/nanostring/. --tensorboard_prefix survival
## Image Slides
CUDA_VISIBLE_DEVICES=0 python train.py --data_dir ~/project/deeplearning/icb/data/tcga/imageSlides/datasets_tsne_list.txt --model_dir ~/project/deeplearning/icb/data/tcga/imageSlides/. --tensorboard_prefix all_pheno