This repository is the official implementation of Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study.
We have developed a deep learning-based multiple instance learning (MIL) model predicting tumor purity of a sample from its H&E stained digital histopathology slides. The model's predictions are consistent with genomic tumor purity values, which are computationally inferred from genomic data and accepted as the golden standard. Furthermore, we have obtained spatially resolved tumor purity maps using the trained model to better understand the tumor microenvironment, a primary determinant of therapeutic response.
We have successfully tested our MIL models on eight different cohorts in The Cancer Genome Atlas (TCGA) and a local Singapore cohort. For all cohorts, we use the same model architecture. Here, we will go through the TCGA LUAD cohort, yet it applies to all.
Folder structure:
SRTPMs
├── _filtered_manifest_files
│ ├── gdc_manifest__BRCA__filtered.txt
│ ├── gdc_manifest__GBM__filtered.txt
│ ├── gdc_manifest__KIRC__filtered.txt
│ ├── gdc_manifest__LGG__filtered.txt
│ ├── gdc_manifest__LUAD__filtered.txt
│ ├── gdc_manifest__LUSC__filtered.txt
│ ├── gdc_manifest__OV__filtered.txt
│ ├── gdc_manifest__PRAD__filtered.txt
│ ├── gdc_manifest__THCA__filtered.txt
│ ├── gdc_manifest__UCEC__filtered.txt
│ └── tcga_cohorts__patient_ids__slide_ids.xlsx
├── _images
├── LUAD
│ ├── Images
│ ├── WSIs
│ ├── dataset
│ ├── mil_dpf_regression
│ ├── pre_processing
│ ├── prepare_dataset
│ └── tcga_data
├── README.md
└── requirements.txtWe will explain the following steps one-by-one:
- Required Python Packages
- Collecting TCGA Data
- Pre-processing
- Prepare Dataset
- Multiple Instance Learning Model
All the experiments were run in a virtual environment created with pip on a Linux machine.
To install requirements:
pip install -r requirements.txtThis part explains downloading (i) whole slide images (WSIs) and (ii) files containing information about slides and analytes from TCGA data portal.
We download the manifest file and filter out the slides of samples that have genomic tumor purity values obtained from genomic data using ABSOLUTE. We present the filtered manifest file within "WSIs" folder. Note that filtered manifest files for all cohorts are given inside "_filtered_manifest_files" folder in the main directory.
WSIs
└── gdc_manifest__LUAD__filtered.txtWe download the slides by using gdc-client:
gdc-client download --manifest gdc_manifest__LUAD__filtered.txtFinal content of the folder:
WSIs
├── gdc_manifest__LUAD__filtered.txt
├── 225f3bad-a221-44fc-9409-373cece21bd8
│ ├── logs
│ └── TCGA-05-4244-01A-01-TS1.6c46d8c3-d7b4-4513-af31-9170244d60a7.svs
├── b79109c9-b3ce-4f4d-bce4-0fae9c49cf2d
│ ├── logs
│ └── TCGA-05-4244-01A-01-BS1.90f043f6-e8d5-4cfd-b4e7-84207bc499a6.svs
├── fe63d8b5-c173-45bd-9404-f89c49296641
│ ├── logs
│ └── TCGA-05-4420-01A-01-TS1.ca743cbe-1c69-4096-97d6-1b83489d1b79.svs
├── 62786dda-fc84-4138-9b60-72d3448676d9
│ ├── logs
│ └── TCGA-05-4420-01A-01-BS1.d5af72a1-39d5-4060-93ee-aafefce8622d.svs
...We download the files "slide.tsv" and "analyte.tsv" from TCGA data portal, and put them into the "tcga_data" folder.
Initial content of the folder:
tcga_data
├── analyte.tsv
├── slide.tsv
├── analyze_tcga_pathologist_predictions.sh
├── collate_data_files.py
└── slide_level_to_sample_level.pyTo obtain slide-level and sample-level percent tumor nuclei estimates of pathologists, we run:
analyze_tcga_pathologist_predictions.shFinal content of the folder:
tcga_data
├── analyte.tsv
├── slide.tsv
├── analyze_tcga_pathologist_predictions.sh
├── collate_data_files.py
├── slide_level_to_sample_level.py
├── analyte_portion_submitter_ids.txt
├── slide_ids__percent_tumor_nuclei_estimates.txt
├── sample_id__slide_portion_ids__analyte_portion_ids.txt
├── slide_id__analyte_portion_id__percent_tumor_nuclei.txt
└── sample_id__analyte_portion_id__percent_tumor_nuclei.txtSlide-level percent tumor nuclei estimates (slide_id__analyte_portion_id__percent_tumor_nuclei.txt):
# slide_id analyte_portion_id percent_tumor_nuclei
TCGA-05-4244-01A-01-TS1 01 80.0
TCGA-05-4244-01A-01-BS1 01 80.0
TCGA-05-4420-01A-01-TS1 01 80.0
TCGA-05-4420-01A-01-BS1 01 75.0
...Sample-level percent tumor nuclei estimates (sample_id__analyte_portion_id__percent_tumor_nuclei.txt):
# sample_id analyte_portion_id percent_tumor_nuclei
TCGA-05-4244-01A 01 80.0
TCGA-05-4420-01A 01 77.5
...This part explains (i) obtaining WSI paths, WSI IDs, and patient IDs and (ii) constructing tissue masks and cropping patches.
Initial content of the folder:
pre_processing
├── construct_wsi_path_and_patient_id_files.sh
├── pre_process.sh
├── tissue_mask_construction.py
└── crop_all_small_patches_over_tissue_mask.pyWe update the full folder path WSI_DIR="/mnt/Tumor_Purity/LUAD/WSIs" in the shell script. Then, to obtain WSI paths, WSI IDs, and Patient IDs, we run:
construct_wsi_path_and_patient_id_files.shWSI paths of primary solid tumor samples (WSI_filelist_primary_solid_tumor):
/mnt/Tumor_Purity/LUAD/WSIs/225f3bad-a221-44fc-9409-373cece21bd8/TCGA-05-4244-01A-01-TS1.6c46d8c3-d7b4-4513-af31-9170244d60a7.svs
/mnt/Tumor_Purity/LUAD/WSIs/b79109c9-b3ce-4f4d-bce4-0fae9c49cf2d/TCGA-05-4244-01A-01-BS1.90f043f6-e8d5-4cfd-b4e7-84207bc499a6.svs
/mnt/Tumor_Purity/LUAD/WSIs/fe63d8b5-c173-45bd-9404-f89c49296641/TCGA-05-4420-01A-01-TS1.ca743cbe-1c69-4096-97d6-1b83489d1b79.svs
/mnt/Tumor_Purity/LUAD/WSIs/62786dda-fc84-4138-9b60-72d3448676d9/TCGA-05-4420-01A-01-BS1.d5af72a1-39d5-4060-93ee-aafefce8622d.svs
...WSI IDs of primary solid tumor samples (wsi_id_primary_solid_tumor):
TCGA-05-4244-01A-01-TS1
TCGA-05-4244-01A-01-BS1
TCGA-05-4420-01A-01-TS1
TCGA-05-4420-01A-01-BS1
...Patient IDs of primary solid tumor samples (patient_id_primary_solid_tumor):
TCGA-05-4244
TCGA-05-4420
...The same set of files are also obtained for solid tissue normal samples. Please note that patient id for a solid tissue normal sample is the same with the one for primary solid tumor sample (since they were collected from the same patient).
Final content of the folder:
pre_processing
├── construct_wsi_path_and_patient_id_files.sh
├── pre_process.sh
├── tissue_mask_construction.py
├── crop_all_small_patches_over_tissue_mask.py
├── WSI_filelist_primary_solid_tumor
├── wsi_id_primary_solid_tumor
├── patient_id_primary_solid_tumor
├── WSI_filelist_solid_tissue_normal
├── wsi_id_solid_tissue_normal
└── patient_id_solid_tissue_normalTo construct tissue masks showing the tissue regions in the slides (tissue_mask_construction.py) and crop patches over the tissue regions (crop_all_small_patches_over_tissue_mask.py), we run:
pre_process.shHere is an example WSI, corresponding tissue mask and a few cropped pathces:
Tissue mask folders and cropped patches folders are created inside "Images":
Images
├── primary_solid_tumor_tissue_masks_level6
├── solid_tissue_normal_tissue_masks_level6
├── all_cropped_patches_primary_solid_tumor__level1__stride512__size512
└── all_cropped_patches_solid_tissue_normal__level1__stride512__size512The cropped patches over the slides of a patient are stored in a folder named with the patient id inside cropped patches folder. A text file (cropped_patches_filelist.txt) storing the slide and location info of each cropped patch is also created within each patient folder. Besides, a global file (cropped_patches_info_patient_id_primary_solid_tumor_all.txt) containing the number of cropped patches for each patient is created in the main folder.
all_cropped_patches_primary_solid_tumor__level1__stride512__size512
├── cropped_patches_info_patient_id_primary_solid_tumor_all.txt
├── TCGA-05-4244
│ ├── cropped_patches_filelist.txt
│ ├── 0.jpeg
│ ├── 1.jpeg
│ ├── ...
│
├── TCGA-05-4420
│ ├── cropped_patches_filelist.txt
│ ├── 0.jpeg
│ ├── 1.jpeg
│ ├── ...
│
...The number of patches cropped over the slides of each patient (cropped_patches_info_patient_id_primary_solid_tumor_all.txt):
# patient_id number_of_patches
TCGA-05-4244 921
TCGA-05-4420 783
...The slide id from which a patch is cropped and location of the patch within the slide (TCGA-05-4244/cropped_patches_filelist.txt):
# patch_id wsi_id mask_row mask_col bg_ratio
0 TCGA-05-4244-01A-01-TS1 16 32 0.5
1 TCGA-05-4244-01A-01-TS1 16 48 0.4
...This part explains preparing machine learning dataset.
Content of the folder "prepare_dataset":
prepare_dataset
├── compare_imaging_and_genomic_data.py
├── generate_5_fold_data.py
├── cropped_patches_info_patient_id_primary_solid_tumor_all.txt
├── cropped_patches_info_patient_id_solid_tissue_normal_all.txt
└── purity_ABSOLUTE.txtThe files containing number of patches for each patient are copied from the previous section and genomic tumor purity values are obtained from genomic data.
Genomic tumor purity values ("purity_ABSOLUTE.txt"):
# patient_id purity
TCGA-05-4244 0.44
TCGA-05-4420 0.59
...We use the data of patients who have both imaging and genomic data. To obtain those patients, we run:
python compare_imaging_and_genomic_data.pyThis creates the dataset folder and info files for all patients:
dataset
└── all_patches__level1__stride512__size512
├── all_patients_info_file.txt
└── all_patients_solid_tissue_normal_info_file.txtHere is the info file for tumor samples (all_patients_info_file.txt):
# patient_id num_patches purity_score group_id
TCGA-05-4244 921 0.44 4
TCGA-05-4420 783 0.59 5
...Then, to split the data into 5 folds at patient-level, we run:
python generate_5_fold_data.pyFinal content of the "dataset" folder:
dataset
└── all_patches__level1__stride512__size512
├── all_patients_info_file.txt
├── all_patients_solid_tissue_normal_info_file.txt
├── fold0_info_file.txt
├── fold1_info_file.txt
├── fold2_info_file.txt
├── fold3_info_file.txt
└── fold4_info_file.txtWe formulate tumor purity prediction as a MIL task such that a sample is represented as a bag of patches cropped from the sample's slides, and the sample's genomic tumor purity value is used as the bag's label. We train the model on the training set (fold0, fold1, and fold2), choose the best model based on the validation set (fold3), and finally tested on the unseen patients of the hold-out test set (fold4).
This section explains each step from training to testing the model. The python and shell scripts used in each step are listed in the corresponding section. Hyperparameters inside the scripts are set to the default values used in the paper.
Scripts used in this section:
mil_dpf_regression
├── distribution_pooling_filter.py
├── resnet_no_bn.py
├── model.py
├── dataset.py
├── train.py
└── plot_loss.pyTo train a model:
python train.pyMetrics collected during training are written into "loss_data" folder and model weights are saved in regular intervals into "saved_models" folder.
mil_dpf_regression
├── loss_data
│ ├── loss_metrics__2020_12_04__15_36_52.txt
│ ├── ...
│
├── saved_models
│ ├── model_weights__2020_12_04__15_36_52__10.pth
│ ├── model_weights__2020_12_04__15_36_52__20.pth
│ ├── ...
│
...To plot loss curves:
python plot_loss.py --data_file loss_data/loss_metrics__2020_12_04__15_36_52.txt --filter_size 10Example loss curves:
For each sample, we create 100 bags from the sample's slides and obtain predictions from the trained model. Then, the average of 100 predictions is used as sample-level tumor purity prediction for that sample.
Scripts used in this section:
mil_dpf_regression
├── dataset_patient.py
├── test_patient.py
└── collect_statistics_over_bag_predictions__sample_level.pyTo obtain sample-level tumor purity predictions from a trained model:
python test_patient.py --init_model_file saved_models/model_weights__2020_12_04__15_36_52__1660.pthMetrics collected during inference are written into "test_metrics".
mil_dpf_regression
├── test_metrics
├── 2020_12_04__15_36_52__1660
└── test
├── TCGA-05-4426-01
│ └── bag_predictions_TCGA-05-4426-01.txt
├── TCGA-05-4433-01
│ └── bag_predictions_TCGA-05-4433-01.txt
...Example bag-level predictions for one patient in the test set (bag_predictions_TCGA-05-4426-01.txt):
# bag_id truth pred
TCGA-05-4426-01_0 0.700 0.754
TCGA-05-4426-01_1 0.700 0.737
TCGA-05-4426-01_2 0.700 0.716
...To obtain sample-level predictions:
python collect_statistics_over_bag_predictions__sample_level.py --data_folder_path test_metrics/2020_12_04__15_36_52__1660/testCollated bag-level predictions file, sample-level predictions file and scatter plots:
mil_dpf_regression
├── test_metrics
├── 2020_12_04__15_36_52__1660
└── test
├── bag_predictions_all_patients.txt
├── bag_level_scatter_plot.png
├── patient_predictions_mpp.txt
├── patient_level_scatter_plot_mpp.png
...Sample-level predictions (patient_predictions_mpp.txt):
# patient_id truth pred
TCGA-05-4426-01 0.700 0.740
TCGA-05-4433-01 0.620 0.452
...Scripts used in this section:
mil_dpf_regression
├── collate__pathologists_estimates__mil_predictions.py
├── scatter_plot.py
└── statistical_tests__mil_predictions__pathologists.pyTo collate pathologists' estimates and the MIL model's predictions:
python collate__pathologists_estimates__mil_predictions.py --data_folder_path test_metrics/2020_12_04__15_36_52__1660/testmil_dpf_regression
├── test_metrics
├── 2020_12_04__15_36_52__1660
└── test
├── sample_id__percent_tumor_nuclei__purity__mil_pred.txt
...Collated file content (sample_id__percent_tumor_nuclei__purity__mil_pred.txt):
# sample_id percent_tumor_nuclei truth pred
TCGA-05-4382-01 0.700 0.210 0.384
TCGA-05-4389-01 0.750 0.480 0.549
...To obtain scatter plots and performance metrics for MIL predictions and pathologists' estimates:
python scatter_plot.py --data_folder_path test_metrics/2020_12_04__15_36_52__1660/testScatter plots and summary statistics files:
mil_dpf_regression
├── test_metrics
├── 2020_12_04__15_36_52__1660
└── test
├── patient_level_scatter_plot__mil.pdf
├── patient_level_scatter_plot__pathologists.pdf
├── summary_spearmann_corr_coeff_and_abs_err__mil.txt
├── summary_spearmann_corr_coeff_and_abs_err__pathologists.txt
...Scatter plot for MIL predictions (patient_level_scatter_plot__mil.pdf):
Scatter plot for pathologists' estimates (patient_level_scatter_plot__pathologists.pdf):
Summary statistics for MIL predictions (summary_spearmann_corr_coeff_and_abs_err__mil.txt):
# rho rho_lower rho_upper p_value mean_abs_err std_abs_err median_abs_err Q1_abs_err Q3_abs_err
0.515 0.320 0.660 2.1e-07 0.132 0.109 0.112 0.060 0.175Summary statistics for pathologists' estimates (summary_spearmann_corr_coeff_and_abs_err__pathologists.txt):
# rho rho_lower rho_upper p_value mean_abs_err std_abs_err median_abs_err Q1_abs_err Q3_abs_err
0.255 0.036 0.448 1.5e-02 0.280 0.151 0.275 0.170 0.395To statistically compare the performance of the MIL model's predictions and pathologists' percent tumor nuclei estimates in terms of correlation and absolute error with respect to genomic tumor purity values:
python statistical_tests__mil_predictions__pathologists.py --data_folder_path test_metrics/2020_12_04__15_36_52__1660/testSummary of statistical tests file:
mil_dpf_regression
├── test_metrics
├── 2020_12_04__15_36_52__1660
└── test
├── statistical_tests__mil__pathologists__summary.txt
...Summary of statistical tests (statistical_tests__mil__pathologists__summary.txt):
# Statistical tests to compare mil predictions vs. percent tumor nuclei estimates
##### comparing correlated correlation coefficients using method of Meng et al. 1991 #####
...
## Statistical test summary on correlation coefficients ##
# rho1 pval1 rho_lower1 rho_upper1 rho2 pval2 rho_lower2 rho_upper2 p_val
0.515 2.1e-07 0.320 0.660 0.255 1.5e-02 0.036 0.448 1.2e-02
##### comparing absolute errors using Wilcoxon signed-rank test #####
...
## Statistical test summary on absolute errors ##
# mean_abs_error1 std_abs_error1 median_abs_error1 Q1_abs_error1 Q3_abs_error1 mean_abs_error2 std_abs_error2 median_abs_error2 Q1_abs_error2 Q3_abs_error2 p_val
0.132 0.109 0.112 0.060 0.175 0.280 0.151 0.275 0.170 0.395 3.9e-09For each slide, we create 100 bags and obtain predictions from the trained model. Then, the average of 100 predictions is used as slide-level tumor purity prediction.
Scripts used in this section:
mil_dpf_regression
├── dataset_slide.py
├── test_slide.py
└── collect_statistics_over_bag_predictions__slide_level.pyTo obtain slide-level tumor purity predictions from a trained model:
python test_slide.py --init_model_file saved_models/model_weights__2020_12_04__15_36_52__1660.pthMetrics collected during inference are written into "test_metrics_slide".
mil_dpf_regression
├── test_metrics_slide
├── 2020_12_04__15_36_52__1660
└── test
├── TCGA-05-4426-01A-01-BS1
│ └── bag_predictions_TCGA-05-4426-01A-01-BS1.txt
├── TCGA-05-4426-01A-01-TS1
│ └── bag_predictions_TCGA-05-4426-01A-01-TS1.txt
...Example bag-level predictions for one slide in the test set (bag_predictions_TCGA-05-4426-01A-01-BS1.txt):
# bag_id truth pred
TCGA-05-4426-01A-01-BS1_0 0.700 0.724
TCGA-05-4426-01A-01-BS1_1 0.700 0.751
TCGA-05-4426-01A-01-BS1_2 0.700 0.712
...To obtain slide-level predictions:
python collect_statistics_over_bag_predictions__slide_level.py --data_folder_path test_metrics_slide/2020_12_04__15_36_52__1660/testCollated bag-level predictions file, slide-level predictions file and scatter plots:
mil_dpf_regression
├── test_metrics_slide
├── 2020_12_04__15_36_52__1660
└── test
├── bag_predictions_all_slides.txt
├── bag_level_scatter_plot.png
├── slide_predictions_mpp.txt
├── slide_level_scatter_plot_mpp.png
...Slide-level predictions (slide_predictions_mpp.txt):
# slide_id truth pred
TCGA-05-4426-01A-01-BS1 0.700 0.736
TCGA-05-4426-01A-01-TS1 0.700 0.741
...Scripts used in this section:
mil_dpf_regression
├── statistical_tests__using_two_slides_vs_one_slide.py
└── statistical_test__compare_top_bottom_slides_of_a_sample.pyTo obtain statistics about slide-level predictions and to statistically compare predicting sample-level tumor purity using two slides together and using only one slide:
python statistical_tests__using_two_slides_vs_one_slide.py --data_folder_path test_metrics_slide/2020_12_04__15_36_52__1660/testSummary files for statistical tests:
mil_dpf_regression
├── test_metrics_slide
├── 2020_12_04__15_36_52__1660
└── test
├── abs_difference__between_slides_predictions__histogram.pdf
├── statistical_test__using_two_slides_vs_one_slide__summary.txt
...Histogram of absolute differences between top and bottom slides of samples (abs_difference__between_slides_predictions__histogram.pdf):
Summary of statistical tests (statistical_test__using_two_slides_vs_one_slide__summary.txt):
##### slide-level prediction statistics and summary of statistical test #####
### abs_difference = np.abs(slide_pred_arr0 - slide_pred_arr1) ###
mean_abs_difference: 0.100, std_abs_difference: 0.110, median_abs_difference: 0.059, Q1_abs_difference: 0.023, Q3_abs_difference: 0.125
### abs_error_sample = np.abs(truth_arr - sample_pred_arr) ###
### mean_abs_error_slides = (abs_error0 + abs_error1)/2 ###
### difference = abs_error_sample - mean_abs_error_slides ###
mean_abs_error_sample: 0.118, std_abs_error_sample: 0.102, median_abs_error_sample: 0.084, Q1_abs_error_sample: 0.050, Q3_abs_error_sample: 0.168
mean_mean_abs_error_slides: 0.138, std_mean_abs_error_slides: 0.102, median_mean_abs_error_slides: 0.121, Q1_mean_abs_error_slides: 0.067, Q3_mean_abs_error_slides: 0.181
wilcoxon test: statistic=178.000, p-value=3.7e-04
# mean_abs_error_sample std_abs_error_sample median_abs_error_sample Q1_abs_error_sample Q3_abs_error_sample mean_mean_abs_error_slides std_mean_abs_error_slides median_mean_abs_error_slides Q1_mean_abs_error_slides Q3_mean_abs_error_slides pvalue
0.118 0.102 0.084 0.050 0.168 0.138 0.102 0.121 0.067 0.181 3.7e-04To statistically compare if top and bottom slides of a sample are different in tumor purity:
python statistical_test__compare_top_bottom_slides_of_a_sample.py --data_folder_path test_metrics_slide/2020_12_04__15_36_52__1660/testSummary files for statistical tests:
mil_dpf_regression
├── test_metrics_slide
├── 2020_12_04__15_36_52__1660
└── test__processed
├── statistical_test__compare_top_bottom_slides_of_a_sample__pvalues.txt
├── pvalues__boxplot.pdf
...Box plot of pvalues obtained from statistical tests (pvalues__boxplot.pdf):
Statistical tests' pvalues file (statistical_test__compare_top_bottom_slides_of_a_sample__pvalues.txt):
# sample_id p_val
TCGA-05-4382-01 2.2e-02
TCGA-05-4389-01 3.9e-18
TCGA-05-4420-01 3.9e-18
...We first extract features of patches in a slide using the trained "feature extractor module" of the MIL model. Then, for each patch location, we predict tumor purity over a region-of-interest (ROI) centered at that location using trained the "transformation module" on distributions estimated from the features of patches within the ROI. By repeating this step for all patches in the slide, we obtained a tumor purity map.
Scripts used in this section:
mil_dpf_regression
├── distribution_pooling_filter.py
├── resnet_no_bn.py
├── model.py
├── dataset_patient_patch.py
├── extract_features.py
├── dataset_distribution_closest_patches.py
├── kde_np.py
├── get_purity_around_a_patch.py
└── obtain_purity_map_from_patch_scores.pyTo exract patch features:
python extract_features.py --init_model_file saved_models/model_weights__2020_12_04__15_36_52__1660.pthExtracted features files:
mil_dpf_regression
├── extracted_features
├── 2020_12_04__15_36_52__1660
└── test
├── TCGA-05-4426-01
│ └── extracted_features_TCGA-05-4426-01.txt
├── TCGA-05-4433-01
│ └── extracted_features_TCGA-05-4433-01.txt
...To get purity scores over ROIs containing 16 closest patches around each patch:
python get_purity_around_a_patch.py --init_model_file saved_models/model_weights__2020_12_04__15_36_52__1660.pth --num_instances 16Patch score files:
mil_dpf_regression
├── patch_scores__16
├── 2020_12_04__15_36_52__1660
└── test
├── TCGA-05-4426-01A-01-BS1
│ └── patch_scores_TCGA-05-4426-01A-01-BS1.txt
├── TCGA-05-4426-01A-01-TS1
│ └── patch_scores_TCGA-05-4426-01A-01-TS1.txt
...Example patch score file (patch_scores_TCGA-05-4426-01A-01-BS1.txt):
# row_id col_id score
32 144 0.7472
32 160 0.7868
32 176 0.8677
...To get tumor purity maps:
python obtain_purity_map_from_patch_scores.py --data_folder_path patch_scores__16/2020_12_04__15_36_52__1660/testExample tumor purity map:
