1. Spatial Transcriptomics (ST) prediction tutorial¶

⚠️ Note:
The following is an example demonstration. Here, the ST prediction model is trained on only 5 samples, using a single inner fold and a single outer fold.
The training process runs for a maximum of 50 epochs.
In the manuscript, the model was trained for 500 epochs on 25 samples from 14 patients,
utilizing 14 outer folds and 7 inner folds, resulting in a total of 98 trained models (14 × 7).

Load packages

In [75]:
import numpy as np
import pandas as pd
import os
import subprocess
import sys
import pkg_resources
import time
import platform
import psutil
import pickle
from scipy.spatial import cKDTree
from pathlib import Path
In [76]:
# Function to get system information
def get_system_info():
    print("\n--- System Information ---")
    print(f"Operating System: {platform.system()} {platform.release()} ({platform.version()})")
    print(f"Processor: {platform.processor()}")
    print(f"CPU Cores: {psutil.cpu_count(logical=False)} (Physical), {psutil.cpu_count(logical=True)} (Logical)")
    print(f"Total RAM: {psutil.virtual_memory().total / (1024**3):.2f} GB")

    # Check for GPU availability (if NVIDIA GPU is installed)
    try:
        gpu_info = subprocess.run("nvidia-smi --query-gpu=name --format=csv,noheader", 
                                  shell=True, capture_output=True, text=True, check=True)
        print(f"GPU: {gpu_info.stdout.strip()}")
    except subprocess.CalledProcessError:
        print("GPU: Not needed")
get_system_info()

--- System Information ---
Operating System: Linux 4.18.0-425.19.2.el8_7.x86_64 (#1 SMP Tue Apr 4 22:38:11 UTC 2023)
Processor: x86_64
CPU Cores: 28 (Physical), 56 (Logical)
Total RAM: 251.50 GB
GPU: Tesla K80

1.1. Feature extraction¶

Load input/output folder and metadata

In [77]:
path2input = f'../input_data/'  # inputs folder 
path2output = f'../output_data/' # outputs folder
In [78]:
# Load the meta file
meta_path = f"{path2input}metadata.csv"
metadata = pd.read_csv(meta_path)
In [63]:
# List all samples in meta
samples=metadata['sample_ID']
print("Samples in meta:", samples)

Samples in meta: 0    092B
1    093B
2    396A
3    397A
4    398B
Name: sample_ID, dtype: object

Feature extraction for the samples¶

In [20]:
# Measure execution time
start_time = time.time()

try:
    # Loop over the slides and run the feature extraction for each of the samples
    for i_slide in range(len(samples)):
        command = f"python ../scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py {path2input} {path2output} {i_slide}"
        print(command)
        result = subprocess.run(command, shell=True, check=True, capture_output=True, text=True, cwd=os.getcwd())
        print("Command Output:\n", result.stdout)
except subprocess.CalledProcessError as e:
    print("Error occurred:\n", e.stderr)

# Calculate elapsed time
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Execution Time: {elapsed_time:.2f} seconds")

python /vf/users/Ruppin_AI/st/zenodo/Path2Space/scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py /vf/users/Ruppin_AI/st/zenodo/Path2Space/input_data/ /vf/users/Ruppin_AI/st/zenodo/Path2Space/output_data/ 0
Command Output:
 device: cuda
device: cuda
i_slide: 0
Figure(4000x2000)
n_tiles: 1352
tiles.shape: torch.Size([1352, 3, 224, 224])
features.shape: (1352, 768)

python /vf/users/Ruppin_AI/st/zenodo/Path2Space/scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py /vf/users/Ruppin_AI/st/zenodo/Path2Space/input_data/ /vf/users/Ruppin_AI/st/zenodo/Path2Space/output_data/ 1
Command Output:
 device: cuda
device: cuda
i_slide: 1
Figure(4000x2000)
n_tiles: 1146
tiles.shape: torch.Size([1146, 3, 224, 224])
features.shape: (1146, 768)

python /vf/users/Ruppin_AI/st/zenodo/Path2Space/scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py /vf/users/Ruppin_AI/st/zenodo/Path2Space/input_data/ /vf/users/Ruppin_AI/st/zenodo/Path2Space/output_data/ 2
Command Output:
 device: cuda
device: cuda
i_slide: 2
Figure(4000x2000)
n_tiles: 553
tiles.shape: torch.Size([553, 3, 224, 224])
features.shape: (553, 768)

python /vf/users/Ruppin_AI/st/zenodo/Path2Space/scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py /vf/users/Ruppin_AI/st/zenodo/Path2Space/input_data/ /vf/users/Ruppin_AI/st/zenodo/Path2Space/output_data/ 3
Command Output:
 device: cuda
device: cuda
i_slide: 3
Figure(4000x2000)
n_tiles: 924
tiles.shape: torch.Size([924, 3, 224, 224])
features.shape: (924, 768)

python /vf/users/Ruppin_AI/st/zenodo/Path2Space/scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction.py /vf/users/Ruppin_AI/st/zenodo/Path2Space/input_data/ /vf/users/Ruppin_AI/st/zenodo/Path2Space/output_data/ 4
Command Output:
 device: cuda
device: cuda
i_slide: 4
Figure(4000x2000)
n_tiles: 629
tiles.shape: torch.Size([629, 3, 224, 224])
features.shape: (629, 768)

Execution Time: 2899.31 seconds

Collect features¶

Create a single pickle file containing all the features

In [79]:
feature_path=f'{path2output}features/'
features = []

# Iterate through all files in the directory
for filename in os.listdir(feature_path):
    if filename.endswith(".pkl"):
        file_path = os.path.join(feature_path, filename)
        x=np.load(file_path, allow_pickle=True)
        features.append((filename, x))
In [7]:
with open(f'{path2output}/features.pkl', 'wb') as f:
    pickle.dump(features, f)

View features for one spot

In [83]:
features[0]
Out[83]:
('01_092B_10x100.pkl',
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        -2.39419937e-01, -4.40853089e-02,  3.24613452e-01, -1.74817219e-01,
        -1.13756686e-01,  2.07915708e-01, -6.81077540e-02,  1.20965511e-01,
         2.94481888e-02,  1.74825042e-01,  1.56636670e-01,  5.32507360e-01,
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         8.29654634e-02,  3.53045091e-02,  8.90630931e-02, -1.77031204e-01,
        -1.11402143e-02, -1.10420868e-01, -1.99949846e-01,  3.43523562e-01,
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        -3.76139693e-02, -2.74843574e-01,  1.72560841e-01,  2.14335099e-02,
        -1.97129816e-01,  1.88732803e-01,  2.51415908e-01,  9.20654982e-02,
         1.40927672e-01,  8.04072767e-02, -1.39051422e-01, -8.07370543e-02,
         9.18529835e-03,  3.12853232e-02,  1.37809038e-01, -8.60377923e-02,
         9.63857397e-02, -4.44471575e-02,  9.66779441e-02,  1.95071220e-01,
        -8.36386308e-02, -2.09051073e-02,  2.41695791e-01,  6.69163242e-02,
         5.15889488e-02, -3.06147814e-01, -4.85827923e-02,  4.90279570e-02,
        -2.93022394e-02,  1.35610327e-01, -9.98105779e-02,  7.70724267e-02,
        -6.00924976e-02, -1.00163817e-01,  7.18366951e-02, -1.19214445e-01,
         7.65648438e-03,  1.16288364e-01,  1.08143292e-01, -8.61057565e-02,
        -5.74962310e-02, -7.70935193e-02,  9.62597877e-02, -7.03946920e-04,
         1.78729042e-01,  7.25205839e-02,  2.35247202e-02,  9.27590430e-02,
         1.10739201e-01, -1.73132401e-02,  1.20784687e-02, -3.45810205e-01,
         7.76101425e-02, -1.61295727e-01,  8.49397108e-02,  5.58736809e-02,
         6.07301258e-02,  1.64249659e-01,  6.12687580e-02,  3.69201154e-02,
        -2.03923792e-01, -2.70327646e-02,  9.16342437e-02,  1.39949083e-01,
         9.42087695e-02,  1.35681361e-01,  6.94272518e-02,  1.49722071e-02,
        -2.63409130e-02,  6.02116762e-03,  4.24321704e-02, -6.70676604e-02,
        -2.46162742e-01, -1.69358868e-02,  9.02379155e-02,  2.48299222e-02,
        -6.89595714e-02,  3.39369737e-02,  1.60543174e-01, -1.45407110e-01,
        -8.18309709e-02,  1.83216315e-02, -1.34991914e-01, -6.13780133e-02,
         8.74837190e-02, -1.25442013e-01, -5.83080277e-02, -1.30316854e-01,
        -2.31559463e-02, -4.30523679e-02,  9.49881449e-02, -8.97088572e-02,
        -4.32207249e-02,  1.14923790e-02,  3.47515196e-02, -1.50968460e-02,
        -3.12420521e-02,  8.69997069e-02,  3.72876972e-02, -2.60844707e-01,
         5.44360653e-02,  2.42607817e-01,  2.74234153e-02, -5.17884456e-02,
         1.59547761e-01, -1.92688257e-02,  1.71144441e-01,  2.43071970e-02,
         1.52212068e-01,  2.07957476e-01,  3.13755833e-02,  4.71814116e-03,
         2.02903926e-01,  5.66636771e-03, -5.40881753e-02,  3.40890363e-02,
         9.91758034e-02,  1.57027453e-01, -1.35958433e-01, -1.12751730e-01,
         3.86208333e-02, -9.56841856e-02, -1.98908746e-01,  2.52441992e-03,
        -3.03306710e-02,  1.34130582e-01, -2.21352592e-01,  1.44426888e-02,
         9.74281952e-02,  8.06700811e-02, -2.42046282e-01, -6.41992688e-02,
         2.92708706e-02, -1.19211741e-01,  4.28062351e-03,  5.81018925e-01,
        -5.88369481e-02, -1.53965831e-01,  1.69160112e-03, -3.93524468e-02,
        -5.29663749e-02,  7.85565451e-02,  4.08855081e-02, -9.89483111e-03,
         1.35682702e-01,  4.47031297e-02, -1.08934678e-01, -1.37776583e-01,
        -9.56171453e-02, -1.09696100e-02,  9.24261212e-02,  6.84888884e-02,
         1.44111767e-01, -1.58654768e-02, -2.06564635e-01,  1.91454321e-01,
        -9.13801119e-02, -2.36673504e-01, -8.87821764e-02, -3.37554663e-02,
        -5.56394905e-02,  6.51014745e-02,  1.96394548e-01,  6.61615729e-02,
         2.77996898e-01, -8.63036811e-01, -1.38852643e-02, -1.22280695e-01,
        -1.24892682e-01, -9.03077498e-02,  8.18398744e-02,  2.67975062e-01,
         4.07496616e-02, -4.07935716e-02,  1.79799289e-01,  7.38511654e-03,
        -1.65202618e-02, -1.21862344e-01, -2.57932727e-04,  5.39154708e-02,
        -2.12313794e-02,  1.28011713e-02,  9.36827958e-02, -9.87139493e-02,
         8.12445506e-02, -3.99992839e-02, -2.36591071e-01,  2.29170114e-01],
       dtype=float32))

1.2. Regression: Training the regression model for one fold¶

Load the split file. Here, since we have 5 slides, we use a split file for 5x4 nested cross validation

In [84]:
# Load the .npz file
file_path = f"{path2input}train_valid_test_idx.npz"
split_file = np.load(file_path, allow_pickle = True)
In [85]:
# Explore the contents of each key
for key in split_file.files:
    print(f"\nKey: {key}")
    print("Shape:", split_file[key].shape)

Key: train_idx
Shape: (5, 4)

Key: valid_idx
Shape: (5, 4)

Key: test_idx
Shape: (5,)

Load the gene file

In [86]:
genes=pd.read_pickle(f'{path2input}gene_file.pkl')['gene']

Define the model parameters

In [87]:
ik_fold=0 # to fully train the model, the regression model should be trained for ik_folds=[0,1,2,3,4]
il_fold=0 # to fully train the model, the regression model should be trained for il_folds=[0,1,2,3]
max_epochs=10  # to fully train the model, the regression model should be trained with max_epochs=500

Define the command

In [88]:
command = f"python ../scripts/1.ST_prediction/1.2.Regression/1main_regression.py {path2input} {path2output}  {ik_fold} {il_fold} {max_epochs}"
print(command)

python ../scripts/1.ST_prediction/1.2.Regression/1main_regression.py ../input_data/ ../output_data/  0 0 10

Execute the command

In [43]:
# Measure execution time
start_time = time.time()

try:
    result = subprocess.run(command, shell=True, check=True, capture_output=True, text=True, cwd=os.getcwd())
    print("Command Output:\n", result.stdout)
except subprocess.CalledProcessError as e:
    print("Error occurred:\n", e.stderr)

# Calculate elapsed time
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Execution Time: {elapsed_time:.2f} seconds")

Command Output:
 device: cuda
genes: ['AL627309.5' 'LINC01409' 'LINC01128' ... 'MT-CYB' 'AC011043.1'
 'AC007325.4']
len(genes): 14068
../output_data//result_0_0
len(features): 4604
rna_file: ../input_data/ge_actual.pkl
 
 --- fit --- 
0, train_loss: 0.0498, coef: 0.1754,            valid_loss: 0.0461, coef: 0.0256
1, train_loss: 0.0438, coef: 0.2751,            valid_loss: 0.0530, coef: 0.0359
2, train_loss: 0.0424, coef: 0.2947,            valid_loss: 0.0534, coef: 0.0438
3, train_loss: 0.0417, coef: 0.3073,            valid_loss: 0.0557, coef: 0.0478
4, train_loss: 0.0411, coef: 0.3171,            valid_loss: 0.0527, coef: 0.0532
5, train_loss: 0.0407, coef: 0.3255,            valid_loss: 0.0541, coef: 0.0561
6, train_loss: 0.0403, coef: 0.3317,            valid_loss: 0.0528, coef: 0.0596
7, train_loss: 0.0400, coef: 0.3376,            valid_loss: 0.0560, coef: 0.0601
8, train_loss: 0.0397, coef: 0.3434,            valid_loss: 0.0571, coef: 0.0618
9, train_loss: 0.0395, coef: 0.3477,            valid_loss: 0.0554, coef: 0.0641
--- completed ---

Execution Time: 523.19 seconds

1.3 Prediction: ST prediction for a TCGA slide using a single trained model¶

1.3.1 Feature extraction on the test slide¶

We need to extract the features on the TCGA slide (like we did in step 1.1)

Define the inputs

In [89]:
path2input_pred = f'{path2input}slides/TCGA-OL-A5S0-01Z-00-DX1.49A7AC9D-C186-406C-BA67-2D73DE82E13B.svs'
path2output_pred = f'{path2output}features_test/'
slide_name_pred = 'OL-A5S0-01Z-00-DX1'

Define the command

In [59]:
command = f"python ../scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction_TCGA.py {path2input_pred} {path2output_pred} {slide_name_pred}"
print(command)

python ../scripts/1.ST_prediction/1.1.Feature_extraction/1main_feature_extraction_TCGA.py ../input_data/slides/TCGA-OL-A5S0-01Z-00-DX1.49A7AC9D-C186-406C-BA67-2D73DE82E13B.svs ../output_data/features_test/ OL-A5S0-01Z-00-DX1

Execute the command

In [60]:
# Measure execution time
start_time = time.time()

try:
    result = subprocess.run(command, shell=True, check=True, capture_output=True, text=True, cwd=os.getcwd())
    print("Command Output:\n", result.stdout)
except subprocess.CalledProcessError as e:
    print("Error occurred:\n", e.stderr)

# Calculate elapsed time
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Execution Time: {elapsed_time:.2f} seconds")

Command Output:
 device: cuda
Device: cpu
[WARNING] Magnification not found, assuming: 40
Downsampling factor: 2
Slide dimensions (level 0): 11940 x 15472, Tile size: 320
Rows: 24, Columns: 18, Total tiles: 432
Processing row: 0/24
Processing row: 1/24
Processing row: 2/24
Processing row: 3/24
Processing row: 4/24
Processing row: 5/24
Processing row: 6/24
Processing row: 7/24
Processing row: 8/24
Processing row: 9/24
Processing row: 10/24
Processing row: 11/24
Processing row: 12/24
Processing row: 13/24
Processing row: 14/24
Processing row: 15/24
Processing row: 16/24
Processing row: 17/24
Processing row: 18/24
Processing row: 19/24
Processing row: 20/24
Processing row: 21/24
Processing row: 22/24
Processing row: 23/24
Figure(4000x2000)
Completed processing.
n_tiles: 313
tiles.shape: torch.Size([313, 3, 224, 224])
features.shape: (313, 768)
Features saved to ../output_data/features_test/OL-A5S0-01Z-00-DX1.pkl

Execution Time: 157.17 seconds

1.3.2 ST Prediction on the test slide¶

Example of Spatial Transcriptomics (ST) Prediction for a TCGA Slide Using a Model with One Inner and One Outer Fold

We start by importing the necessary modules and setting the path to the prediction script.

In [64]:
# Define the path to the prediction script and add it to the system path
script_path = Path("../scripts/1.ST_prediction/1.3.Prediction")
sys.path.append(str(script_path))

# Import the prediction function
from TCGA_Prediction import predict_st_from_trained_model
In [65]:
#Define the path to the test features file
path_to_test_features = "../output_data/features_test/OL-A5S0-01Z-00-DX1.pkl"
# Load the features from the pickle file
features_list = np.load(f"{path_to_test_features}", allow_pickle=True)
# Define the outer and inner cross-validation folds
ik_folds = [0]  # Example outer fold index
il_folds = [0]  # Example inner fold index

Run the Prediction Model¶

Now, we use the predict_st_from_trained_model function to generate predictions for the test slide.

In [66]:
tcga_st_pred = predict_st_from_trained_model(ik_folds, il_folds,features_list,
                                              path2models = path2output,
                                              genes = genes)

ik_fold: 0

View the Prediction Results

In [67]:
tcga_st_pred.head()
Out[67]:
gene AL627309.5 LINC01409 LINC01128 LINC00115 FAM41C NOC2L KLHL17 PLEKHN1 HES4 ISG15 ... MT-ATP6 MT-CO3 MT-ND3 MT-ND4L MT-ND4 MT-ND5 MT-ND6 MT-CYB AC011043.1 AC007325.4
OL-A5S0-01Z-00-DX1_5120_640 0.000000 0.063481 0.137235 0.0 0.000000 0.366984 0.094526 0.011786 0.242220 0.503742 ... 1.856757 2.162080 1.604967 0.537832 2.073462 0.943535 0.035123 1.772477 0.004086 0.128883
OL-A5S0-01Z-00-DX1_5760_640 0.000000 0.090824 0.129589 0.0 0.011592 0.437613 0.090697 0.027083 0.272966 0.533956 ... 1.955625 2.249601 1.662094 0.581792 2.147532 0.965505 0.051561 1.850879 0.021384 0.182696
OL-A5S0-01Z-00-DX1_6400_640 0.000000 0.059229 0.090626 0.0 0.009585 0.364503 0.079889 0.004146 0.299876 0.534686 ... 1.866759 2.171256 1.560895 0.574450 2.032656 0.956175 0.022169 1.752970 0.014386 0.134413
OL-A5S0-01Z-00-DX1_7040_640 0.004623 0.069930 0.084290 0.0 0.008578 0.404981 0.100633 0.024949 0.336416 0.536940 ... 1.960141 2.249665 1.589541 0.551702 2.068218 0.969224 0.040065 1.793535 0.035897 0.120231
OL-A5S0-01Z-00-DX1_7680_640 0.000000 0.035643 0.107543 0.0 0.000000 0.335269 0.064756 0.000000 0.224696 0.438432 ... 1.718369 2.042897 1.445177 0.495116 1.933079 0.838902 0.023798 1.642045 0.025675 0.110878

5 rows × 14068 columns

1.3.3. Smoothing the predictions¶

After obtaining thre predictiosn, we perform the smoothing step

In [68]:
#### Smoothing function

def smooth_genes_kdtree(slide_df, genes, radius=2, weights = 'uniform'):

    # Extract spatial coordinates and gene data
    coordinates = slide_df[['x', 'y']].values
    gene_data = slide_df[genes].values

    # Build a KDTree for fast radius neighbor searches
    tree = cKDTree(coordinates)

    # Query the tree for neighbors within the radius for each point
    indices = [tree.query_ball_point(point, r=radius) for point in coordinates]

    # Prepare an array to hold smoothed gene values
    smoothed_gene_data = np.zeros_like(gene_data)

    # Compute average values of all genes for neighbors within the radius
    for i, neighbors in enumerate(indices):
        if neighbors:
            # Calculate the mean across all selected genes for these indices
            smoothed_gene_data[i] = gene_data[neighbors].mean(axis=0)
            if weights != 'uniform':
                
                smoothed_gene_data[i] = (len(neighbors)*smoothed_gene_data[i] + (weights-1)*gene_data[i])/(len(neighbors) + weights -1)

        else:
            # Handle cases with no neighbors (could handle differently if needed)
            smoothed_gene_data[i] = gene_data[i]

    # Create a DataFrame for smoothed gene values
    smoothed_knn = pd.DataFrame(smoothed_gene_data, columns=genes, index=slide_df.index)

    # Optionally, combine original data with smoothed data
    result_df = slide_df.copy()
    result_df.update(smoothed_knn)

    return result_df

## extract x,y coordinates from spot names

def get_x_y_grid(df_pred, tile_size=640):
    slide_names=df_pred.index.str.split('_').str[0]
    
    # Split 'spot_id' to extract 'x' and 'y' coordinates    
    x = df_pred.index.str.split('_').str[-1]
    y = df_pred.index.str.split('_').str[-2]

    # Convert 'x' and 'y' from strings to floats
    x = (x.astype(float)-x.astype(float).min())//tile_size
    y = (y.astype(float)-y.astype(float).min())//tile_size
    
    out_df = pd.DataFrame({'slide_file_name':slide_names,'x':x, 'y':y}, index = df_pred.index)


    return out_df
In [69]:
grid_df = get_x_y_grid(tcga_st_pred)
tcga_st_pred = grid_df.join(tcga_st_pred)
tcga_st_pred = tcga_st_pred.sort_values(['slide_file_name', 'x', 'y'])
In [70]:
tcga_st_pred_s = smooth_genes_kdtree(tcga_st_pred, genes, radius=2, weights ='uniform')
tcga_st_pred_s.head(5)
Out[70]:
slide_file_name x y AL627309.5 LINC01409 LINC01128 LINC00115 FAM41C NOC2L KLHL17 ... MT-ATP6 MT-CO3 MT-ND3 MT-ND4L MT-ND4 MT-ND5 MT-ND6 MT-CYB AC011043.1 AC007325.4
OL-A5S0-01Z-00-DX1_5120_640 OL-A5S0-01Z-00-DX1 0.0 7.0 0.000000 0.079070 0.130132 0.0 0.023046 0.401769 0.081746 ... 1.902388 2.186774 1.623865 0.564500 2.086916 0.939488 0.041352 1.800760 0.027332 0.150518
OL-A5S0-01Z-00-DX1_5760_640 OL-A5S0-01Z-00-DX1 0.0 8.0 0.000578 0.076432 0.112029 0.0 0.014338 0.397474 0.078107 ... 1.893562 2.184277 1.594706 0.558414 2.065148 0.933467 0.034625 1.781440 0.029887 0.143750
OL-A5S0-01Z-00-DX1_6400_640 OL-A5S0-01Z-00-DX1 0.0 9.0 0.000514 0.070372 0.105360 0.0 0.008933 0.391541 0.082247 ... 1.888205 2.191742 1.582788 0.556548 2.052988 0.930027 0.031429 1.775921 0.029741 0.142698
OL-A5S0-01Z-00-DX1_7040_640 OL-A5S0-01Z-00-DX1 0.0 10.0 0.000514 0.062812 0.101876 0.0 0.011261 0.381107 0.080334 ... 1.872242 2.171229 1.563104 0.543288 2.026742 0.918676 0.024383 1.758730 0.029152 0.123052
OL-A5S0-01Z-00-DX1_7680_640 OL-A5S0-01Z-00-DX1 0.0 11.0 0.000578 0.057530 0.102105 0.0 0.009892 0.373124 0.075897 ... 1.855960 2.150274 1.543203 0.540031 2.011223 0.906584 0.019498 1.741664 0.033048 0.124812

5 rows × 14071 columns

Save the smoothed and non-smoothed predictions

In [71]:
tcga_st_pred_s.to_pickle(f'{path2output}smoothed_preds_tcga.pkl')
tcga_st_pred.to_pickle(f'{path2output}preds_tcga.pkl')

Python and packages versions¶

In [72]:
# Print Python version
print(f"Python version: {sys.version}")

Python version: 3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:23:14) [GCC 10.4.0]
In [73]:
# Print installed packages and their versions
installed_packages = {pkg.key: pkg.version for pkg in pkg_resources.working_set}
for package, version in installed_packages.items():
    print(f"{package}=={version}")

simpleitk==2.3.1
spagcn==1.2.7
absl-py==2.1.0
access==1.1.9
ace-tools==0.0
affine==2.4.0
autograd==1.6.2
autograd-gamma==0.5.0
brokenaxes==0.6.2
click-plugins==1.1.1
cligj==0.7.2
cytospace==1.1.0
datatable==1.1.0
deprecation==2.1.0
einops==0.7.0
esda==2.6.0
fiona==1.10.0
formulaic==1.0.1
geopandas==1.0.1
giddy==2.3.5
gseapy==1.1.3
huggingface-hub==0.20.3
igraph==0.11.6
immutabledict==4.2.0
inequality==1.0.1
interface-meta==1.3.0
lapjv==1.3.14
legacy-api-wrap==1.4
leidenalg==0.10.2
libpysal==4.12.0
lifelines==0.28.0
louvain==0.8.2
mapclassify==2.8.0
matplotlib-venn==0.11.10
mgwr==2.2.1
momepy==0.8.0
openslide-python==1.3.1
ortools==9.3.10497
pip==24.1.2
pointpats==2.5.0
protobuf==3.20.1
pykwalify==1.8.0
pyogrio==0.9.0
pypdf2==3.0.1
pyradiomics==3.0.1
pysal==24.7
python-igraph==0.11.6
quantecon==0.7.2
radiomics==0.1
rasterio==1.3.11
rasterstats==0.19.0
ripleyk==0.0.3
safetensors==0.4.2
scanpy==1.10.1
seaborn==0.13.2
segregation==2.5
session-info==1.0.0
snuggs==1.4.7
spaghetti==1.7.6
spams==2.6.5.4
spglm==1.1.0
spint==1.0.7
splot==1.1.6
spopt==0.6.1
spreg==1.6.1
spvcm==0.3.0
texttable==1.7.0
tobler==0.11.3
tokenizers==0.15.2
transformers==4.38.0
automat==22.10.0
babel==2.12.1
bottleneck==1.3.7
configargparse==1.5.5
cython==0.29.36
dendropy==4.6.1
deprecated==1.2.14
edflib-python==1.0.7
flask==2.3.2
flask-cors==4.0.0
gitpython==3.1.31
htseq==2.0.3
heapdict==1.0.1
jinja2==3.1.2
keras-preprocessing==1.1.2
levenshtein==0.21.1
mako==1.2.4
markdown==3.4.3
markupsafe==2.1.3
pillow==9.2.0
pulp==2.7.0
pyjwt==2.7.0
pynacl==1.5.0
pyopengl==3.1.6
pyqt5==5.15.7
pyqt5-sip==12.11.0
pyqtwebengine==5.15.4
pysocks==1.7.1
pyvcf3==1.0.3
pywavelets==1.4.1
pyyaml==6.0
pygments==2.15.1
qdarkstyle==3.2.3
qtawesome==1.2.3
qtpy==2.4.1
rtree==1.0.1
sqlalchemy==2.0.18
secretstorage==3.3.3
send2trash==1.8.2
theano==1.0.5
unidecode==1.3.6
werkzeug==2.3.6
xlsxwriter==3.1.2
aioeasywebdav==2.4.0
aiohttp==3.8.4
aioredis==2.0.1
aiosignal==1.3.1
alabaster==0.7.13
amply==0.1.6
anndata==0.9.1
anyio==3.7.1
appdirs==1.4.4
argcomplete==3.1.1
argh==0.27.2
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
arviz==0.15.1
asn1crypto==1.5.1
astor==0.8.1
astroid==2.15.5
astropy==5.3.1
asttokens==2.2.1
astunparse==1.6.3
async-generator==1.10
async-timeout==4.0.2
atomicwrites==1.4.1
attmap==0.13.2
attrs==23.1.0
autopep8==2.0.4
backcall==0.2.0
backports.functools-lru-cache==1.6.5
basemap==1.3.7
basemap-data==1.3.2
bcrypt==3.2.2
beautifulsoup4==4.12.2
bidict==0.22.1
binaryornot==0.4.4
biom-format==2.1.15
biopython==1.81
bitarray==2.7.6
black==23.3.0
bleach==6.0.0
blinker==1.6.2
bokeh==3.2.0
boltons==23.0.0
boto==2.49.0
boto3==1.28.1
botocore==1.31.1
brotlipy==0.7.0
bz2file==0.98
cached-property==1.5.2
cachetools==5.3.1
certifi==2024.6.2
cffi==1.15.1
cftime==1.6.2
chardet==5.1.0
charset-normalizer==3.2.0
click==8.1.4
clikit==0.6.2
cloudpickle==2.2.1
clyent==1.2.2
colorama==0.4.6
colorcet==3.0.1
coloredlogs==15.0.1
colorful==0.5.4
colormath==3.0.0
colorspacious==1.1.2
comm==0.1.3
commonmark==0.9.1
conda==23.3.1
conda-package-handling==2.0.2
conda-package-streaming==0.8.0
configparser==5.3.0
connection-pool==0.0.3
constantly==15.1.0
contextlib2==21.6.0
contourpy==1.1.0
cookiecutter==2.2.0
crashtest==0.4.1
crc32c==2.3.post0
crcmod==1.7
cryptography==39.0.0
cupy==12.1.0
cvxopt==0.0.0
cvxpy==1.3.2
cxxfilt==0.3.0
cycler==0.11.0
cytoolz==0.12.0
darkdetect==0.8.0
dask==2023.7.0
dask-jobqueue==0.8.2
dataclasses==0.8
datashape==0.5.4
datrie==0.8.2
debugpy==1.6.7
decorator==5.1.1
deepdiff==6.3.1
defusedxml==0.7.1
descartes==1.1.0
diff-match-patch==20230430
dill==0.3.6
dipy==1.7.0
distlib==0.3.6
distributed==2023.7.0
dm-tree==0.1.7
dnspython==2.3.0
docopt==0.6.2
docstring-to-markdown==0.12
docutils==0.20.1
dpath==2.1.6
dropbox==11.36.2
ecos==2.0.11
eeglabio==0.0.2.post4
entrypoints==0.4
et-xmlfile==1.1.0
exceptiongroup==1.1.2
executing==1.2.0
fabric==3.1.0
fastcache==1.1.0
fastjsonschema==2.17.1
fastrlock==0.8
feather-format==0.4.1
filechunkio==1.8
filelock==3.12.2
flake8==6.1.0
flatbuffers==23.5.26
flit-core==3.9.0
fonttools==4.40.0
freetype-py==2.3.0
frozenlist==1.3.3
fsspec==2023.6.0
ftputil==5.0.4
func-timeout==4.3.5
future==0.18.3
gast==0.4.0
gemmi==0.6.4
geneimpacts==0.3.7
gevent==22.10.2
gffutils==0.11.1
gitdb==4.0.10
gitdb2==4.0.2
glob2==0.7
globus-sdk==3.23.0
gmpy2==2.1.2
google-api-core==2.11.1
google-api-python-client==2.92.0
google-auth==2.21.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==0.4.6
google-cloud-core==2.3.3
google-cloud-storage==2.10.0
google-crc32c==1.1.2
google-pasta==0.2.0
google-resumable-media==2.5.0
googleapis-common-protos==1.59.1
greenlet==2.0.2
grpcio==1.51.1
gym==0.26.1
gym-notices==0.0.8
h5io==0.1.8
h5netcdf==1.2.0
h5py==3.8.0
hdf5storage==0.1.19
helpdev==0.7.1
hiredis==2.2.3
holoviews==1.16.2
html5lib==1.1
httplib2==0.22.0
httpstan==4.6.1
humanfriendly==10.0
hyperlink==21.0.0
hypothesis==6.80.1
idna==3.4
ihm==0.43
imagecodecs==2022.8.8
imageio==2.31.1
imageio-ffmpeg==0.4.8
imagesize==1.4.1
importlib-metadata==6.8.0
importlib-resources==6.0.0
incremental==22.10.0
inflection==0.5.1
iniconfig==2.0.0
intervaltree==3.1.0
invoke==2.1.3
ipycanvas==0.13.1
ipyevents==2.0.1
ipykernel==6.29.5
ipympl==0.9.3
ipyparallel==8.6.1
ipython==8.14.0
ipython-genutils==0.2.0
ipyvtklink==0.2.3
ipywidgets==7.7.5
isal==1.1.0
isort==5.12.0
itsdangerous==2.1.2
jaraco.classes==3.2.3
jax==0.3.25
jaxlib==0.3.25
jdcal==1.4.1
jedi==0.18.2
jeepney==0.8.0
jellyfish==1.0.0
jinja2-time==0.2.0
jmespath==1.0.1
joblib==1.3.0
json5==0.9.14
jsonpatch==1.32
jsonpickle==2.2.0
jsonpointer==2.0
jsonschema==4.18.0
jsonschema-specifications==2023.6.1
jupyter==1.0.0
jupyter-client==8.3.0
jupyter-console==6.6.3
jupyter-core==5.3.1
jupyter-events==0.6.3
jupyter-server==2.7.0
jupyter-server-terminals==0.4.4
jupyterlab-pygments==0.2.2
jupyterlab-widgets==1.1.4
keras==2.11.0
keyring==24.2.0
kiwisolver==1.4.4
lazy-loader==0.2
lazy-object-proxy==1.9.0
libmambapy==1.2.0
line-profiler==4.0.3
linkify-it-py==2.0.0
llvmlite==0.40.1
lmdb==1.4.1
locket==1.0.0
logmuse==0.2.6
loguru==0.7.0
lxml==4.9.2
lz4==4.3.2
mamba==1.2.0
markdown-it-py==3.0.0
marshmallow==3.19.0
matplotlib==3.7.2
matplotlib-inline==0.1.6
mccabe==0.7.0
mdit-py-plugins==0.4.0
mdurl==0.1.0
meshio==5.3.4
mffpy==0.8.0
mistune==3.0.0
mizani==0.9.2
mne==1.4.2
mne-qt-browser==0.5.1
mock==5.0.2
modelcif==0.9
more-itertools==9.1.0
mpmath==1.3.0
msgpack==1.0.5
multidict==6.0.4
multipledispatch==0.6.0
munkres==1.1.4
mypy-extensions==1.0.0
mysql-connector-python==8.0.31
natsort==8.4.0
nbclassic==1.0.0
nbclient==0.8.0
nbconvert==7.6.0
nbformat==5.9.0
nest-asyncio==1.5.6
netcdf4==1.6.2
networkx==3.1
nibabel==5.1.0
nilearn==0.10.1
nltk==3.8.1
nose==1.3.7
notebook==6.5.4
notebook-shim==0.2.3
npx==0.1.1
numba==0.57.1
numexpr==2.7.3
numpy==1.24.4
numpydoc==1.5.0
oauth2client==4.1.3
oauthlib==3.2.2
odo==0.5.1
olefile==0.46
opencensus==0.11.2
opencensus-context==0.1.3
opencv-python==4.6.0
openpyxl==3.1.2
opt-einsum==3.3.0
ordered-set==4.1.0
orjson==3.9.2
osqp==0.6.3
overrides==7.3.1
packaging==23.1
palettable==3.3.3
pandas==2.0.3
pandocfilters==1.5.0
panel==1.2.0
param==1.13.0
paramiko==3.2.0
parasail==1.3.4
parso==0.8.3
partd==1.4.0
pastel==0.2.1
path==16.7.1
pathlib2==2.3.7.post1
pathspec==0.11.1
pathtools==0.1.2
patsy==0.5.3
pbr==5.11.1
pep8==1.7.1
peppy==0.35.6
pexpect==4.8.0
pickleshare==0.7.5
pkgutil-resolve-name==1.3.10
plac==1.3.5
platformdirs==3.8.1
plotly==5.15.0
plotnine==0.12.1
pluggy==1.2.0
plumbum==1.8.2
ply==3.11
pooch==1.7.0
poyo==0.5.0
prettytable==3.7.0
prometheus-client==0.17.0
prompt-toolkit==3.0.39
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
pyopenssl==23.2.0
pyarrow==10.0.1
pyasn1==0.4.8
pyasn1-modules==0.2.7
pycairo==1.24.0
pycodestyle==2.11.1
pycosat==0.6.4
pycparser==2.21
pycrypto==2.6.1
pyct==0.4.6
pycurl==7.45.1
pydocstyle==6.3.0
pydot==1.4.2
pyerfa==2.0.0.3
pyfaidx==0.7.2.1
pyfasta==0.5.2
pyflakes==3.1.0
pyglet==1.5.27
pygpu==0.7.6
pygraphviz==1.10
pyhamcrest==2.0.4
pylev==1.4.0
pylint==2.17.4
pylint-venv==3.0.2
pyls-spyder==0.4.0
pymatreader==0.0.32
pymongo==4.4.0
pynndescent==0.5.10
pyodbc==4.0.39
pyopencl==2023.1.1
pyparsing==3.0.9
pyproj==3.4.1
pyqtgraph==0.13.3
pyrsistent==0.19.3
pysam==0.21.0
pysftp==0.2.9
pyshp==2.3.1
pysimdjson==5.0.2
pysmi==0.3.4
pytest==7.4.0
pytest-arraydiff==0.5.0
pytest-doctestplus==0.13.0
pytest-openfiles==0.5.0
pytest-remotedata==0.4.0
pytest-runner==6.0.0
python-levenshtein==0.21.1
python-dateutil==2.8.2
python-hostlist==1.21
python-irodsclient==1.1.8
python-json-logger==2.0.7
python-lsp-black==1.3.0
python-lsp-jsonrpc==1.1.2
python-lsp-server==1.9.0
python-picard==0.7
python-slugify==8.0.1
pytoolconfig==1.2.5
pytools==2023.1
pytz==2023.3
pytz-deprecation-shim==0.1.0.post0
pyu2f==0.1.5
pyvista==0.40.0
pyvistaqt==0.0.0
pyviz-comms==2.3.2
pyxdg==0.28
pyzmq==25.1.0
qdldl==0.1.5.post2
qstylizer==0.2.2
qtconsole==5.5.2
rapidfuzz==2.15.1
ray==2.2.0
redis==4.5.5
referencing==0.29.1
regex==2023.6.3
reportlab==4.0.4
requests==2.31.0
requests-oauthlib==1.3.1
reretry==0.11.8
retrying==1.3.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==13.4.2
rlpycairo==0.2.0
rope==1.9.0
rpds-py==0.8.8
rpy2==3.5.13
rsa==4.9
ruamel.yaml==0.17.32
ruamel.yaml.clib==0.2.7
s3transfer==0.6.1
scikit-image==0.21.0
scikit-learn==1.3.0
scipy==1.11.1
scooby==0.7.2
scs==3.2.3
service-identity==18.1.0
setuptools==68.0.0
setuptools-scm==7.1.0
sh==2.0.4
shapely==2.0.1
simplegeneric==0.8.1
simplejson==3.19.1
sinfo==0.3.1
singledispatch==0.0.0
sip==6.7.9
six==1.16.0
slacker==0.14.0
smart-open==6.3.0
smmap==3.0.5
snakemake==7.30.1
sniffio==1.3.0
snowballstemmer==2.2.0
sortedcollections==2.1.0
sortedcontainers==2.4.0
soupsieve==2.3.2.post1
sphinx==7.0.1
sphinxcontrib-applehelp==1.0.4
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
spyder==5.5.0
spyder-kernels==2.5.2
stack-data==0.6.2
statsmodels==0.14.0
stdlib-list==0.8.0
stone==3.3.1
stopit==1.1.2
svgutils==0.3.4
sympy==1.12
tables==3.7.0
tabulate==0.9.0
tblib==1.7.0
tenacity==8.2.2
tensorboard==2.11.2
tensorboardx==2.5
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.11.0
tensorflow-estimator==2.11.0
tensorflow-probability==0.19.0
termcolor==2.3.0
terminado==0.17.1
terminaltables==3.1.10
testpath==0.6.0
text-unidecode==1.3
textdistance==4.5.0
threadpoolctl==3.1.0
three-merge==0.1.1
throttler==1.2.1
tifffile==2022.10.10
tinycss2==1.2.1
toml==0.10.2
tomli==2.0.1
tomlkit==0.11.8
toolz==0.12.0
toposort==1.10
torch==1.12.1.post201
torchvision==0.13.0a0+8069656
tornado==6.3.2
tqdm==4.65.0
traitlets==5.9.0
twobitreader==3.1.7
typing-extensions==4.7.1
typing-utils==0.1.0
tzdata==2023.3
tzlocal==5.0.1
ubiquerg==0.6.2
uc-micro-py==1.0.1
ujson==5.7.0
umap-learn==0.5.3
unicodecsv==0.14.1
unicodedata2==15.0.0
uritemplate==4.1.1
urllib3==1.26.15
veracitools==0.1.3
virtualenv==20.23.1
vtk==9.2.5
watchdog==3.0.0
wcwidth==0.2.6
webargs==8.2.0
webencodings==0.5.1
websocket-client==1.6.1
whatthepatch==1.0.5
wheel==0.40.0
whichcraft==0.6.1
widgetsnbextension==3.6.4
wrapt==1.15.0
wslink==1.11.1
wurlitzer==3.0.3
xarray==2023.6.0
xarray-einstats==0.5.1
xgboost==1.7.4
xlrd==2.0.1
xlwt==1.3.0
xmlrunner==1.7.7
xmltodict==0.13.0
xopen==1.7.0
xyzservices==2023.5.0
yapf==0.33.0
yarl==1.9.2
yte==1.5.1
zict==3.0.0
zipp==3.15.0
zope.event==5.0
zope.interface==6.0
zstandard==0.19.0
In [ ]: