Getting quickly started¶
Here we present a quickly re-analysis of slide6 on the human intestinal dataset (Fawkner-Corbett et al., Cell, 2021), which including 346 raw ST spots.
This tutorial demonstrates how to enhanced the cellular resolution on 10x Visium data using STASCAN quickly.
The slide6 data are available at https://github.com/AbbyWY/STASCAN/tree/master/Vignettes/Fawkner-Corbett_Intestinal_Slide6.
For reference, we employed STASCAN on this dataset with three modules in 14 minutes. The experiment was conducted on the GPU platform (NVIDIA Tesla V100s with 32 GB memory).
Preparation¶
import sys
import os
vignettes_path = "./Vignettes/Fawkner-Corbett_Intestinal_Slide6/" # the path of downloaded slide6 data.
output_path = "./"
# You can optionally set the parameter 'epochs=2' to quickly test the pipeline, but you should set the parameter 'epochs=50' for the official run in this tutorial.
#epochs = 2
epochs = 50
import STASCAN
label_list = ['Epithelium', 'Fibroblasts', 'Muscularis', 'Neural'] # The list of cell types.
color_list = ["purple", "orange", "red", "gold"]
dict_label = dict(zip(label_list, color_list))
crop_size = 40 # Pixel radius of spot images.
raw_image = vignettes_path + "/A6.jpg" # The original H&E staining image, used for Space Ranger previously.
adjacent_image = vignettes_path+"/Simulated_A7.jpg"
Module 1: Cell annotation for unseen spots¶
Model running¶
if not os.path.exists(output_path + "/Module1/"):
os.makedirs(output_path + "/Module1/")
Module1_output = output_path + "/Module1/"
# The results will be automatically saved in the default Module1_output path.
run = STASCAN.run_STASCAN.Module()
run.UnseenSpot(Module1_output, vignettes_path + "/tissue_positions_list.csv", raw_image, crop_size, vignettes_path, epochs=epochs)
Running reports¶
The number of raw spots : 346
The strategy of pre-labelling is: joint
The number of prior spots : 288
The number of imputed spots : 614
Found 231 images belonging to 4 classes.
Found 57 images belonging to 4 classes.
{'Epithelium': 0, 'Fibroblasts': 1, 'Muscularis': 2, 'Neural': 3}
{'Epithelium': 0, 'Fibroblasts': 1, 'Muscularis': 2, 'Neural': 3}
Epoch 1/50 8/8 [==============================] - 10s 589ms/step - loss: 1.2529 - accuracy: 0.5541 - val_loss: 0.9509 - val_accuracy: 0.6316 Epoch 2/50 8/8 [==============================] - 3s 385ms/step - loss: 1.0719 - accuracy: 0.5455 - val_loss: 1.0987 - val_accuracy: 0.6316 Epoch 3/50 8/8 [==============================] - 3s 373ms/step - loss: 1.0329 - accuracy: 0.6061 - val_loss: 0.8360 - val_accuracy: 0.7018 Epoch 4/50 8/8 [==============================] - 3s 439ms/step - loss: 0.9308 - accuracy: 0.6277 - val_loss: 0.7543 - val_accuracy: 0.6667 Epoch 5/50 8/8 [==============================] - 3s 386ms/step - loss: 0.8571 - accuracy: 0.6883 - val_loss: 0.7092 - val_accuracy: 0.7368 Epoch 6/50 8/8 [==============================] - 3s 371ms/step - loss: 0.8062 - accuracy: 0.7143 - val_loss: 0.7969 - val_accuracy: 0.6316 Epoch 7/50 8/8 [==============================] - 3s 388ms/step - loss: 0.7512 - accuracy: 0.7143 - val_loss: 0.7314 - val_accuracy: 0.6667 Epoch 8/50 8/8 [==============================] - 3s 390ms/step - loss: 0.8180 - accuracy: 0.6797 - val_loss: 0.9108 - val_accuracy: 0.6316 Epoch 9/50 8/8 [==============================] - 3s 387ms/step - loss: 0.9679 - accuracy: 0.6277 - val_loss: 0.7732 - val_accuracy: 0.6316 Epoch 10/50 8/8 [==============================] - 3s 372ms/step - loss: 0.8724 - accuracy: 0.6883 - val_loss: 0.6475 - val_accuracy: 0.7719 Epoch 11/50 8/8 [==============================] - 3s 388ms/step - loss: 0.7356 - accuracy: 0.7359 - val_loss: 0.7226 - val_accuracy: 0.7018 Epoch 12/50 8/8 [==============================] - 3s 383ms/step - loss: 0.7953 - accuracy: 0.7056 - val_loss: 0.6358 - val_accuracy: 0.7895 Epoch 13/50 8/8 [==============================] - 3s 376ms/step - loss: 0.6318 - accuracy: 0.7792 - val_loss: 0.6780 - val_accuracy: 0.7368 Epoch 14/50 8/8 [==============================] - 3s 396ms/step - loss: 0.7530 - accuracy: 0.7489 - val_loss: 0.5250 - val_accuracy: 0.8246 Epoch 15/50 8/8 [==============================] - 3s 386ms/step - loss: 0.6392 - accuracy: 0.7835 - val_loss: 0.4861 - val_accuracy: 0.8421 Epoch 16/50 8/8 [==============================] - 3s 388ms/step - loss: 0.6003 - accuracy: 0.7922 - val_loss: 0.6001 - val_accuracy: 0.7544 Epoch 17/50 8/8 [==============================] - 3s 374ms/step - loss: 0.5739 - accuracy: 0.7879 - val_loss: 0.7940 - val_accuracy: 0.7018 Epoch 18/50 8/8 [==============================] - 3s 391ms/step - loss: 0.6568 - accuracy: 0.7922 - val_loss: 0.6823 - val_accuracy: 0.7193 Epoch 19/50 8/8 [==============================] - 3s 388ms/step - loss: 0.6321 - accuracy: 0.7532 - val_loss: 0.5100 - val_accuracy: 0.8421 Epoch 20/50 8/8 [==============================] - 3s 387ms/step - loss: 0.6485 - accuracy: 0.7879 - val_loss: 0.6803 - val_accuracy: 0.7544 Epoch 21/50 8/8 [==============================] - 3s 386ms/step - loss: 0.5430 - accuracy: 0.8225 - val_loss: 0.4647 - val_accuracy: 0.8070 Epoch 22/50 8/8 [==============================] - 3s 443ms/step - loss: 0.5112 - accuracy: 0.8139 - val_loss: 0.4715 - val_accuracy: 0.8246 Epoch 23/50 8/8 [==============================] - 3s 394ms/step - loss: 0.5262 - accuracy: 0.8139 - val_loss: 0.4027 - val_accuracy: 0.8421 Epoch 24/50 8/8 [==============================] - 3s 385ms/step - loss: 0.4891 - accuracy: 0.8225 - val_loss: 0.3832 - val_accuracy: 0.8421 Epoch 25/50 8/8 [==============================] - 3s 384ms/step - loss: 0.5453 - accuracy: 0.8009 - val_loss: 0.5432 - val_accuracy: 0.8070 Epoch 26/50 8/8 [==============================] - 3s 388ms/step - loss: 0.5616 - accuracy: 0.8052 - val_loss: 0.6269 - val_accuracy: 0.7895 Epoch 27/50 8/8 [==============================] - 3s 387ms/step - loss: 0.7241 - accuracy: 0.7229 - val_loss: 0.7899 - val_accuracy: 0.6316 Epoch 28/50 8/8 [==============================] - 3s 390ms/step - loss: 0.7617 - accuracy: 0.6364 - val_loss: 0.5832 - val_accuracy: 0.8246 Epoch 29/50 8/8 [==============================] - 3s 373ms/step - loss: 0.5885 - accuracy: 0.7835 - val_loss: 0.4726 - val_accuracy: 0.8246 Epoch 30/50 8/8 [==============================] - 3s 394ms/step - loss: 0.5091 - accuracy: 0.8225 - val_loss: 0.3974 - val_accuracy: 0.8596 Epoch 31/50 8/8 [==============================] - 3s 384ms/step - loss: 0.4623 - accuracy: 0.8355 - val_loss: 0.4177 - val_accuracy: 0.8772 Epoch 32/50 8/8 [==============================] - 3s 384ms/step - loss: 0.4702 - accuracy: 0.8268 - val_loss: 0.3325 - val_accuracy: 0.8596 Epoch 33/50 8/8 [==============================] - 3s 386ms/step - loss: 0.4183 - accuracy: 0.8268 - val_loss: 0.3291 - val_accuracy: 0.8596 Epoch 34/50 8/8 [==============================] - 3s 368ms/step - loss: 0.3732 - accuracy: 0.8571 - val_loss: 0.3183 - val_accuracy: 0.8772 Epoch 35/50 8/8 [==============================] - 3s 382ms/step - loss: 0.3735 - accuracy: 0.8615 - val_loss: 0.3958 - val_accuracy: 0.8421 Epoch 36/50 8/8 [==============================] - 3s 386ms/step - loss: 0.4011 - accuracy: 0.8528 - val_loss: 0.3298 - val_accuracy: 0.8421 Epoch 37/50 8/8 [==============================] - 3s 375ms/step - loss: 0.3407 - accuracy: 0.8615 - val_loss: 0.3932 - val_accuracy: 0.8596 Epoch 38/50 8/8 [==============================] - 3s 391ms/step - loss: 0.4359 - accuracy: 0.8398 - val_loss: 0.3584 - val_accuracy: 0.8772 Epoch 39/50 8/8 [==============================] - 3s 384ms/step - loss: 0.6582 - accuracy: 0.7835 - val_loss: 0.5134 - val_accuracy: 0.7895 Epoch 40/50 8/8 [==============================] - 3s 388ms/step - loss: 0.5390 - accuracy: 0.7749 - val_loss: 0.3622 - val_accuracy: 0.8421 Epoch 41/50 8/8 [==============================] - 3s 373ms/step - loss: 0.3892 - accuracy: 0.8571 - val_loss: 0.3431 - val_accuracy: 0.8772 Epoch 42/50 8/8 [==============================] - 3s 371ms/step - loss: 0.3348 - accuracy: 0.8701 - val_loss: 0.3722 - val_accuracy: 0.8772 Epoch 43/50 8/8 [==============================] - 3s 392ms/step - loss: 0.5298 - accuracy: 0.8139 - val_loss: 0.6732 - val_accuracy: 0.7368 Epoch 44/50 8/8 [==============================] - 3s 396ms/step - loss: 0.6072 - accuracy: 0.7835 - val_loss: 0.5309 - val_accuracy: 0.7895 Epoch 45/50 8/8 [==============================] - 3s 387ms/step - loss: 0.4821 - accuracy: 0.8139 - val_loss: 0.4598 - val_accuracy: 0.8421 Epoch 46/50 8/8 [==============================] - 3s 387ms/step - loss: 0.3751 - accuracy: 0.8355 - val_loss: 0.5681 - val_accuracy: 0.8246 Epoch 47/50 8/8 [==============================] - 3s 434ms/step - loss: 0.3721 - accuracy: 0.8485 - val_loss: 0.3583 - val_accuracy: 0.8421 Epoch 48/50 8/8 [==============================] - 3s 393ms/step - loss: 0.3207 - accuracy: 0.8701 - val_loss: 0.3952 - val_accuracy: 0.8246 Epoch 49/50 8/8 [==============================] - 3s 434ms/step - loss: 0.3372 - accuracy: 0.8571 - val_loss: 0.3400 - val_accuracy: 0.8772 Epoch 50/50 8/8 [==============================] - 3s 382ms/step - loss: 0.2969 - accuracy: 0.8874 - val_loss: 0.3144 - val_accuracy: 0.8772 {0: 'Epithelium', 1: 'Fibroblasts', 2: 'Muscularis', 3: 'Neural'} 1/1 [==============================] - 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0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 32ms/step </pre> </div>Model evaluation¶
# Training metrics fig = STASCAN.StatPlot.Metric() fig.ROC_Curve(Module1_output + "/PriorSpot/test/", Module1_output + "/Predict/Rawpredict_detail.txt", label_list, color_list, Module1_output + "/Models/ROC.pdf") fig.Loss_Accuracy_Curve(Module1_output + "/Models/Log_BaseModel.txt", Module1_output + "/Models/")
# Check fig = STASCAN.StatPlot.Check() fig.Check_PriorSpot(Module1_output + "/ImputedSpot/adjust_raw_spot.txt", Module1_output + "/PriorSpot/prior_spot.txt", raw_image, dict_label, Module1_output) # Check the selection of prior spots fig.Check_ImputedSpot(Module1_output + "/ImputedSpot/adjust_raw_spot.txt", Module1_output + "ImputedSpot/imputed_spot.txt", raw_image, Module1_output) # Check the imputed location of unseen spots
Visualization¶
# Visualization fig = STASCAN.run_STASCAN.Visualization() fig.EnhancedPlot(Module1_output + "/Predict/Raw_predict.txt", Module1_output + "/PriorSpot/prior_spot.txt", Module1_output + "/Predict/Imputed_predict.txt", raw_image, dict_label, Module1_output)
Module 2: Cell annotation for subdivided spots¶
Model running¶
if not os.path.exists(output_path + "/Module2/"): os.makedirs(output_path + "/Module2/") Module2_output = output_path + "/Module2/" # The results will be automatically saved in the default Module2_output path. run.SubdividedSpot(Module2_output, vignettes_path + "/tissue_positions_list.csv", raw_image, crop_size, vignettes_path, threshold_proportion=0.5, epochs=epochs)Running reports¶
The number of raw spots : 346 The strategy of pre-labelling is: single_proportion The threshold of cell proportion is: 0.5 The number of prior spots : 156 Found 502 images belonging to 4 classes. Found 122 images belonging to 4 classes. {'Epithelium': 0, 'Fibroblasts': 1, 'Muscularis': 2, 'Neural': 3} {'Epithelium': 0, 'Fibroblasts': 1, 'Muscularis': 2, 'Neural': 3}Epoch 1/50 16/16 [==============================] - 11s 635ms/step - loss: 1.1288 - accuracy: 0.5896 - val_loss: 0.7579 - val_accuracy: 0.7295 Epoch 2/50 16/16 [==============================] - 7s 408ms/step - loss: 1.0111 - accuracy: 0.7052 - val_loss: 0.8268 - val_accuracy: 0.7295 Epoch 3/50 16/16 [==============================] - 6s 395ms/step - loss: 0.8286 - accuracy: 0.7151 - val_loss: 0.6827 - val_accuracy: 0.7295 Epoch 4/50 16/16 [==============================] - 7s 413ms/step - loss: 0.7143 - accuracy: 0.7211 - val_loss: 0.6401 - val_accuracy: 0.7295 Epoch 5/50 16/16 [==============================] - 7s 403ms/step - loss: 0.6399 - accuracy: 0.7311 - val_loss: 0.6236 - val_accuracy: 0.7787 Epoch 6/50 16/16 [==============================] - 7s 416ms/step - loss: 0.6456 - accuracy: 0.6992 - val_loss: 0.6339 - val_accuracy: 0.7295 Epoch 7/50 16/16 [==============================] - 7s 407ms/step - loss: 0.6252 - accuracy: 0.7251 - val_loss: 0.6347 - val_accuracy: 0.7869 Epoch 8/50 16/16 [==============================] - 7s 405ms/step - loss: 0.7326 - accuracy: 0.6793 - val_loss: 0.6503 - val_accuracy: 0.7295 Epoch 9/50 16/16 [==============================] - 7s 404ms/step - loss: 0.6399 - accuracy: 0.7072 - val_loss: 0.6068 - val_accuracy: 0.7295 Epoch 10/50 16/16 [==============================] - 7s 424ms/step - loss: 0.6037 - accuracy: 0.7191 - val_loss: 0.5947 - val_accuracy: 0.7705 Epoch 11/50 16/16 [==============================] - 7s 403ms/step - loss: 0.5975 - accuracy: 0.7470 - val_loss: 0.6114 - val_accuracy: 0.7787 Epoch 12/50 16/16 [==============================] - 7s 404ms/step - loss: 0.5946 - accuracy: 0.7271 - val_loss: 0.5771 - val_accuracy: 0.7705 Epoch 13/50 16/16 [==============================] - 7s 399ms/step - loss: 0.5916 - accuracy: 0.7291 - val_loss: 0.6157 - val_accuracy: 0.8033 Epoch 14/50 16/16 [==============================] - 7s 401ms/step - loss: 0.5884 - accuracy: 0.7470 - val_loss: 0.5664 - val_accuracy: 0.8115 Epoch 15/50 16/16 [==============================] - 7s 411ms/step - loss: 0.5718 - accuracy: 0.7450 - val_loss: 0.6638 - val_accuracy: 0.7459 Epoch 16/50 16/16 [==============================] - 7s 405ms/step - loss: 0.6152 - accuracy: 0.7470 - val_loss: 0.5709 - val_accuracy: 0.8115 Epoch 17/50 16/16 [==============================] - 7s 403ms/step - loss: 0.5673 - accuracy: 0.7749 - val_loss: 0.5432 - val_accuracy: 0.8197 Epoch 18/50 16/16 [==============================] - 7s 409ms/step - loss: 0.5717 - accuracy: 0.7649 - val_loss: 0.5564 - val_accuracy: 0.8033 Epoch 19/50 16/16 [==============================] - 7s 404ms/step - loss: 0.5179 - accuracy: 0.8088 - val_loss: 0.5605 - val_accuracy: 0.7951 Epoch 20/50 16/16 [==============================] - 7s 406ms/step - loss: 0.6320 - accuracy: 0.7231 - val_loss: 0.6627 - val_accuracy: 0.6721 Epoch 21/50 16/16 [==============================] - 7s 407ms/step - loss: 0.6510 - accuracy: 0.6853 - val_loss: 0.6173 - val_accuracy: 0.7623 Epoch 22/50 16/16 [==============================] - 7s 406ms/step - loss: 0.6092 - accuracy: 0.7649 - val_loss: 0.5421 - val_accuracy: 0.7869 Epoch 23/50 16/16 [==============================] - 7s 403ms/step - loss: 0.5557 - accuracy: 0.7769 - val_loss: 0.5496 - val_accuracy: 0.7869 Epoch 24/50 16/16 [==============================] - 7s 401ms/step - loss: 0.5528 - accuracy: 0.7829 - val_loss: 0.5130 - val_accuracy: 0.8197 Epoch 25/50 16/16 [==============================] - 7s 402ms/step - loss: 0.5176 - accuracy: 0.7908 - val_loss: 0.5661 - val_accuracy: 0.7869 Epoch 26/50 16/16 [==============================] - 7s 408ms/step - loss: 0.4521 - accuracy: 0.8386 - val_loss: 0.4750 - val_accuracy: 0.8361 Epoch 27/50 16/16 [==============================] - 7s 402ms/step - loss: 0.4994 - accuracy: 0.8207 - val_loss: 0.5807 - val_accuracy: 0.7541 Epoch 28/50 16/16 [==============================] - 7s 404ms/step - loss: 0.5707 - accuracy: 0.7610 - val_loss: 0.6020 - val_accuracy: 0.7295 Epoch 29/50 16/16 [==============================] - 7s 401ms/step - loss: 0.6282 - accuracy: 0.7351 - val_loss: 0.5437 - val_accuracy: 0.8361 Epoch 30/50 16/16 [==============================] - 7s 403ms/step - loss: 0.5640 - accuracy: 0.7371 - val_loss: 0.5522 - val_accuracy: 0.7951 Epoch 31/50 16/16 [==============================] - 7s 403ms/step - loss: 0.5402 - accuracy: 0.7669 - val_loss: 0.5312 - val_accuracy: 0.8361 Epoch 32/50 16/16 [==============================] - 7s 405ms/step - loss: 0.5443 - accuracy: 0.7948 - val_loss: 0.5404 - val_accuracy: 0.7869 Epoch 33/50 16/16 [==============================] - 7s 404ms/step - loss: 0.4684 - accuracy: 0.8167 - val_loss: 0.4673 - val_accuracy: 0.8443 Epoch 34/50 16/16 [==============================] - 7s 412ms/step - loss: 0.4452 - accuracy: 0.8287 - val_loss: 0.5045 - val_accuracy: 0.8197 Epoch 35/50 16/16 [==============================] - 7s 402ms/step - loss: 0.4562 - accuracy: 0.8247 - val_loss: 0.6192 - val_accuracy: 0.7377 Epoch 36/50 16/16 [==============================] - 7s 404ms/step - loss: 0.4727 - accuracy: 0.8267 - val_loss: 0.5430 - val_accuracy: 0.8279 Epoch 37/50 16/16 [==============================] - 7s 403ms/step - loss: 0.4811 - accuracy: 0.8048 - val_loss: 0.4300 - val_accuracy: 0.8607 Epoch 38/50 16/16 [==============================] - 7s 404ms/step - loss: 0.5053 - accuracy: 0.8167 - val_loss: 0.4978 - val_accuracy: 0.8197 Epoch 39/50 16/16 [==============================] - 7s 416ms/step - loss: 0.5355 - accuracy: 0.8187 - val_loss: 0.4676 - val_accuracy: 0.8197 Epoch 40/50 16/16 [==============================] - 7s 402ms/step - loss: 0.4721 - accuracy: 0.8147 - val_loss: 0.4551 - val_accuracy: 0.8525 Epoch 41/50 16/16 [==============================] - 7s 410ms/step - loss: 0.5267 - accuracy: 0.7888 - val_loss: 0.4732 - val_accuracy: 0.8607 Epoch 42/50 16/16 [==============================] - 7s 404ms/step - loss: 0.4355 - accuracy: 0.8267 - val_loss: 0.5184 - val_accuracy: 0.7869 Epoch 43/50 16/16 [==============================] - 7s 403ms/step - loss: 0.4019 - accuracy: 0.8606 - val_loss: 0.3984 - val_accuracy: 0.8770 Epoch 44/50 16/16 [==============================] - 7s 403ms/step - loss: 0.4024 - accuracy: 0.8486 - val_loss: 0.6094 - val_accuracy: 0.7295 Epoch 45/50 16/16 [==============================] - 7s 408ms/step - loss: 0.3645 - accuracy: 0.8586 - val_loss: 0.3998 - val_accuracy: 0.8770 Epoch 46/50 16/16 [==============================] - 7s 400ms/step - loss: 0.3904 - accuracy: 0.8546 - val_loss: 0.4001 - val_accuracy: 0.8689 Epoch 47/50 16/16 [==============================] - 6s 395ms/step - loss: 0.4341 - accuracy: 0.8267 - val_loss: 0.4238 - val_accuracy: 0.8443 Epoch 48/50 16/16 [==============================] - 7s 399ms/step - loss: 0.3886 - accuracy: 0.8625 - val_loss: 0.3884 - val_accuracy: 0.8770 Epoch 49/50 16/16 [==============================] - 6s 398ms/step - loss: 0.3712 - accuracy: 0.8645 - val_loss: 0.4753 - val_accuracy: 0.8525 Epoch 50/50 16/16 [==============================] - 7s 399ms/step - loss: 0.3370 - accuracy: 0.8765 - val_loss: 0.4061 - val_accuracy: 0.8852 {0: 'Epithelium', 1: 'Fibroblasts', 2: 'Muscularis', 3: 'Neural'} 1/1 [==============================] - 0s 194ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 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0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 37ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 35ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 38ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 34ms/step 1/1 [==============================] - 0s 32ms/step </pre> </div>Model evaluation¶
# Metrics fig = STASCAN.StatPlot.Metric() fig.ROC_Curve(Module2_output + "/SubSpot/prior_divided/test/", Module2_output + "/Predict/Subdividedpredict_detail.txt", label_list, color_list, Module2_output + "/Models/ROC.pdf") fig.Loss_Accuracy_Curve(Module2_output + "/Models/Log_BaseModel.txt", Module2_output + "/Models/")
Visualization¶
# Visualization fig = STASCAN.run_STASCAN.Visualization() fig.SubResolutionPlot(Module1_output+"/Predict/Raw_predict.txt", Module2_output+"/Predict/Subdivided_predict.txt", raw_image, dict_label, Module2_output)
Module 3: Cell annotation for unseen sections¶
Model running¶
if not os.path.exists(output_path + "/Module3/"): os.makedirs(output_path + "/Module3/") Module3_output = output_path + "/Module3/" spot_label = [[x] for x in label_list] run.UnseenSection(Module3_output, vignettes_path+"/Simulated_tissue_positions_list.csv", adjacent_image, crop_size, Module1_output+"/Models/base_model.h5", spot_label)Running reports¶
The number of raw spots : 344 The number of imputed spots : 594 {0: 'Epithelium', 1: 'Fibroblasts', 2: 'Muscularis', 3: 'Neural'} 1/1 [==============================] - 0s 204ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 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0s 32ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 31ms/step 1/1 [==============================] - 0s 33ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 30ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 32ms/step 1/1 [==============================] - 0s 32ms/step </pre> </div>Visualization¶
# Visualization fig = STASCAN.run_STASCAN.Visualization() fig.AdjacentSectionPlot(Module3_output+"/Predict/Adjacent_raw_predict.txt", Module3_output+"/Predict/Adjacent_imputed_predict.txt", adjacent_image, dict_label, Module3_output)