Dealing with different ST technologies

Logically, STASCAN can be employed in all ST technologies that utilize microarrays, microfluidics, or similar designs, to annotate cell types for unseen spots. It can also be used in other NGS-based ST technologies to annotate cell types for subdivided spots and unseen sections, only if the technology provides both H&E staining images and transcriptional data.

In this tutorial, we applied STASCAN on ST datasets generated by the 10x Visium and MISAR-seq technologies (motivated by DBiT-seq), which respectively stand for microarray-based and microfluidic-based technologies. The documents demonstrating how to use STASCAN on the 10x Visium data can be found in the “Getting quickly started” and “Advanced analysis” sentions in this tutorial. Here, we present a demonstration on how to deal with microfluidic-based ST data.

We employed STASCAN on an embryonic mouse brain dataset from MISAR-seq(Jiang et al., Nat Methods, 2023), a microfluidic indexing-based spatial technology motivated by DBiT-seq4 with both high-quality image and sequencing data. The data(Slide_E15_5) are available at https://github.com/AbbyWY/STASCAN/tree/master/Vignettes/Jiang_mousebrain_E15_5.

For reference, we employed STASCAN on this dataset with downstream cell-type niche analysis in 25 minutes. The experiment was conducted on the GPU platform (NVIDIA Tesla V100s with 32 GB memory).

Preparation

import sys
import os
import shutil
import glob

import STASCAN

vignettes_path = "./Jiang_mousebrain_E15_5/" # the path of downloaded Slide_E15_5 data.
output_path = "./"

image = vignettes_path + "./E15_5-S1-HE.jpg"
crop_size = 8

origin_set = [10, 10]
gap_length = 29.5

Dir = ["Corticalorhippocampalglutamatergic", "ForebrainGABAergic", "Forebrainglutamatergic",
       "HindbrainGABAergic", "Hindbrainglutamatergic", "Hindbrain",
       "Hypothalamus", "MidbrainGABAergic", "Midbrainglutamatergic",
       "Midbrain", "Mixedregionandneurotransmitter", "MixedregionGABAergic",
       "Mixedregionglutamatergic", "Mixedregion", "Undefined"]

color_map = {'CerebellumGABAergic': ['#FFC0CB', 'pink'],
             'Corticalorhippocampalglutamatergic': ['#FF1493', 'deeppink'],
             'Forebrain': ['#FF00FF', 'magenta'],
             'ForebrainGABAergic': ['#800080', 'purple'],
             'Forebrainglutamatergic': ['#8A2BE2', 'blueviolet'],
             'Hindbrain': ['#0000FF', 'blue'],
             'HindbrainGABAergic': ['#00BFFF', 'deepskyblue'],
             'Hindbrainglutamatergic': ['#6495ED', 'cornflowerblue'],
             'Hindbrainglycinergic': ['#00FFFF', 'cyan'],
             'Hypothalamus': ['#00CED1', 'darkturquoise'],
             'Midbrain':['#2F4F4F', 'darkslategray'],
             'MidbrainGABAergic': ['#808000', 'olive'],
             'Midbrainglutamatergic': ['#F5FFFA', 'mediumspringgreen'],
             'Mixedregion': ['#2E8B57', 'seagreen'],
             'MixedregionGABAergic': ['#90EE90', 'lightgreen'],
             'Mixedregionandneurotransmitter': ['#FFFF00', 'yellow'],
             'Mixedregionglutamatergic': ['#FFA500', 'orange'],
             'Pia3': ['#FF0000', 'red'],
             'Undefined': ['#A0522D', 'sienna']}

epochs = 50
position = vignettes_path + "/xy.txt"
prelabel_path = vignettes_path + "/RCTD_prior.txt"
allprelabel_file = vignettes_path + "/RCTD_prelabelling_all.txt"
downsample_number = 300
min_num = 12

Model running

run = STASCAN.run_STASCAN_dbit.Module()
run.UnseenSpot(output_path, position, image, crop_size, origin_set, gap_length, prelabel_path, allprelabel_file, Dir, downsample_number=downsample_number, min_num=min_num, epochs=epochs)

Running reports

The number of prior spots :  1697
The number of imputed spots :  5847.0
The downsampling dirs are :
['MixedregionGABAergic', 'Undefined']
The rare cell dirs are :
['HindbrainGABAergic', 'Hindbrainglutamatergic', 'Hindbrain', 'Hypothalamus', 'MidbrainGABAergic', 'Midbrain', 'Mixedregion']
Found 858 images belonging to 8 classes.
Found 210 images belonging to 8 classes.
{'Corticalorhippocampalglutamatergic': 0, 'ForebrainGABAergic': 1, 'Forebrainglutamatergic': 2, 'Midbrainglutamatergic': 3, 'MixedregionGABAergic': 4, 'Mixedregionandneurotransmitter': 5, 'Mixedregionglutamatergic': 6, 'Undefined': 7}
{'Corticalorhippocampalglutamatergic': 0, 'ForebrainGABAergic': 1, 'Forebrainglutamatergic': 2, 'Midbrainglutamatergic': 3, 'MixedregionGABAergic': 4, 'Mixedregionandneurotransmitter': 5, 'Mixedregionglutamatergic': 6, 'Undefined': 7}

 Epoch 1/50
 27/27 [==============================] - 24s 848ms/step - loss: 1.8400 - accuracy: 0.2821 - val_loss: 1.6299 - val_accuracy: 0.2952
 Epoch 2/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.7268 - accuracy: 0.2576 - val_loss: 1.6468 - val_accuracy: 0.2857
 Epoch 3/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.6992 - accuracy: 0.2657 - val_loss: 1.6132 - val_accuracy: 0.2857
 Epoch 4/50
 27/27 [==============================] - 12s 421ms/step - loss: 1.6743 - accuracy: 0.2809 - val_loss: 1.6564 - val_accuracy: 0.2857
 Epoch 5/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.6339 - accuracy: 0.2716 - val_loss: 1.5382 - val_accuracy: 0.2857
 Epoch 6/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.5546 - accuracy: 0.2855 - val_loss: 1.5131 - val_accuracy: 0.3190
 Epoch 7/50
 27/27 [==============================] - 12s 426ms/step - loss: 1.6252 - accuracy: 0.2541 - val_loss: 1.5353 - val_accuracy: 0.2524
 Epoch 8/50
 27/27 [==============================] - 12s 430ms/step - loss: 1.5688 - accuracy: 0.2867 - val_loss: 1.5190 - val_accuracy: 0.3238
 Epoch 9/50
 27/27 [==============================] - 12s 431ms/step - loss: 1.5525 - accuracy: 0.2762 - val_loss: 1.5043 - val_accuracy: 0.3238
 Epoch 10/50
 27/27 [==============================] - 12s 428ms/step - loss: 1.5179 - accuracy: 0.3357 - val_loss: 1.5408 - val_accuracy: 0.2714
 Epoch 11/50
 27/27 [==============================] - 12s 435ms/step - loss: 1.5273 - accuracy: 0.3357 - val_loss: 1.5422 - val_accuracy: 0.3286
 Epoch 12/50
 27/27 [==============================] - 12s 428ms/step - loss: 1.5452 - accuracy: 0.3450 - val_loss: 1.5103 - val_accuracy: 0.4095
 Epoch 13/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.5086 - accuracy: 0.3601 - val_loss: 1.4618 - val_accuracy: 0.4095
 Epoch 14/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.4724 - accuracy: 0.3695 - val_loss: 1.4585 - val_accuracy: 0.4238
 Epoch 15/50
 27/27 [==============================] - 12s 424ms/step - loss: 1.5146 - accuracy: 0.3100 - val_loss: 1.4791 - val_accuracy: 0.4429
 Epoch 16/50
 27/27 [==============================] - 12s 431ms/step - loss: 1.4679 - accuracy: 0.3788 - val_loss: 1.4566 - val_accuracy: 0.4190
 Epoch 17/50
 27/27 [==============================] - 12s 421ms/step - loss: 1.4833 - accuracy: 0.4138 - val_loss: 1.4706 - val_accuracy: 0.4238
 Epoch 18/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.4645 - accuracy: 0.3963 - val_loss: 1.4824 - val_accuracy: 0.4238
 Epoch 19/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.4511 - accuracy: 0.4161 - val_loss: 1.4382 - val_accuracy: 0.4476
 Epoch 20/50
 27/27 [==============================] - 11s 415ms/step - loss: 1.4871 - accuracy: 0.3916 - val_loss: 1.4742 - val_accuracy: 0.4000
 Epoch 21/50
 27/27 [==============================] - 11s 410ms/step - loss: 1.4507 - accuracy: 0.3939 - val_loss: 1.4764 - val_accuracy: 0.4143
 Epoch 22/50
 27/27 [==============================] - 11s 409ms/step - loss: 1.4393 - accuracy: 0.4103 - val_loss: 1.4606 - val_accuracy: 0.4000
 Epoch 23/50
 27/27 [==============================] - 12s 425ms/step - loss: 1.4887 - accuracy: 0.3951 - val_loss: 1.4535 - val_accuracy: 0.4429
 Epoch 24/50
 27/27 [==============================] - 11s 417ms/step - loss: 1.4632 - accuracy: 0.4207 - val_loss: 1.4483 - val_accuracy: 0.4190
 Epoch 25/50
 27/27 [==============================] - 12s 429ms/step - loss: 1.4159 - accuracy: 0.4207 - val_loss: 1.4483 - val_accuracy: 0.4333
 Epoch 26/50
 27/27 [==============================] - 12s 425ms/step - loss: 1.4709 - accuracy: 0.4056 - val_loss: 1.5118 - val_accuracy: 0.3714
 Epoch 27/50
 27/27 [==============================] - 12s 424ms/step - loss: 1.4642 - accuracy: 0.3846 - val_loss: 1.4736 - val_accuracy: 0.3762
 Epoch 28/50
 27/27 [==============================] - 12s 428ms/step - loss: 1.4283 - accuracy: 0.4103 - val_loss: 1.4255 - val_accuracy: 0.4524
 Epoch 29/50
 27/27 [==============================] - 12s 429ms/step - loss: 1.4308 - accuracy: 0.4417 - val_loss: 1.4299 - val_accuracy: 0.4476
 Epoch 30/50
 27/27 [==============================] - 12s 425ms/step - loss: 1.4305 - accuracy: 0.4103 - val_loss: 1.4511 - val_accuracy: 0.4571
 Epoch 31/50
 27/27 [==============================] - 12s 433ms/step - loss: 1.4098 - accuracy: 0.4371 - val_loss: 1.4376 - val_accuracy: 0.4286
 Epoch 32/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.4145 - accuracy: 0.4242 - val_loss: 1.4214 - val_accuracy: 0.4429
 Epoch 33/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.3980 - accuracy: 0.4464 - val_loss: 1.4801 - val_accuracy: 0.3810
 Epoch 34/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.4294 - accuracy: 0.4126 - val_loss: 1.4266 - val_accuracy: 0.4571
 Epoch 35/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.3893 - accuracy: 0.4429 - val_loss: 1.4149 - val_accuracy: 0.4619
 Epoch 36/50
 27/27 [==============================] - 12s 430ms/step - loss: 1.4031 - accuracy: 0.4277 - val_loss: 1.4429 - val_accuracy: 0.4429
 Epoch 37/50
 27/27 [==============================] - 11s 421ms/step - loss: 1.3951 - accuracy: 0.4417 - val_loss: 1.4126 - val_accuracy: 0.4524
 Epoch 38/50
 27/27 [==============================] - 11s 415ms/step - loss: 1.3698 - accuracy: 0.4767 - val_loss: 1.4012 - val_accuracy: 0.4476
 Epoch 39/50
 27/27 [==============================] - 12s 423ms/step - loss: 1.3657 - accuracy: 0.4569 - val_loss: 1.4202 - val_accuracy: 0.4524
 Epoch 40/50
 27/27 [==============================] - 12s 429ms/step - loss: 1.3524 - accuracy: 0.4627 - val_loss: 1.3827 - val_accuracy: 0.4619
 Epoch 41/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.3991 - accuracy: 0.4266 - val_loss: 1.3943 - val_accuracy: 0.4667
 Epoch 42/50
 27/27 [==============================] - 12s 431ms/step - loss: 1.3587 - accuracy: 0.4744 - val_loss: 1.4160 - val_accuracy: 0.4000
 Epoch 43/50
 27/27 [==============================] - 12s 434ms/step - loss: 1.3615 - accuracy: 0.4557 - val_loss: 1.4127 - val_accuracy: 0.4476
 Epoch 44/50
 27/27 [==============================] - 12s 423ms/step - loss: 1.3998 - accuracy: 0.4417 - val_loss: 1.4048 - val_accuracy: 0.4190
 Epoch 45/50
 27/27 [==============================] - 12s 427ms/step - loss: 1.3495 - accuracy: 0.4615 - val_loss: 1.4089 - val_accuracy: 0.4571
 Epoch 46/50
 27/27 [==============================] - 12s 431ms/step - loss: 1.4328 - accuracy: 0.4254 - val_loss: 1.4311 - val_accuracy: 0.3762
 Epoch 47/50
 27/27 [==============================] - 12s 431ms/step - loss: 1.3393 - accuracy: 0.4895 - val_loss: 1.3849 - val_accuracy: 0.4714
 Epoch 48/50
 27/27 [==============================] - 12s 426ms/step - loss: 1.3618 - accuracy: 0.4650 - val_loss: 1.3546 - val_accuracy: 0.4952
 Epoch 49/50
 27/27 [==============================] - 12s 432ms/step - loss: 1.3282 - accuracy: 0.4545 - val_loss: 1.3697 - val_accuracy: 0.4857
 Epoch 50/50
 27/27 [==============================] - 12s 434ms/step - loss: 1.3409 - accuracy: 0.4685 - val_loss: 1.3519 - val_accuracy: 0.4810
 {0: 'Corticalorhippocampalglutamatergic', 1: 'ForebrainGABAergic', 2: 'Forebrainglutamatergic', 3: 'Midbrainglutamatergic', 4: 'MixedregionGABAergic', 5: 'Mixedregionandneurotransmitter', 6: 'Mixedregionglutamatergic', 7: 'Undefined'}
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 {0: 'Corticalorhippocampalglutamatergic', 1: 'ForebrainGABAergic', 2: 'Forebrainglutamatergic', 3: 'Midbrainglutamatergic', 4: 'MixedregionGABAergic', 5: 'Mixedregionandneurotransmitter', 6: 'Mixedregionglutamatergic', 7: 'Undefined'}
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    </pre>
</div>

Model evaluation

Visualization

fig = STASCAN.run_STASCAN_dbit.Visualization()
fig.EnhancedPlot(origin_set, gap_length, position, output_path + "/Predict/Raw_predict.txt", prelabel_path, output_path + "/Predict/Imputed_predict.txt", output_path + "/fill_full_list.txt", color_map, output_path)

Downstream analysis

Cell-type niche analysis

import STASCAN.downstream as downstream
n_kmeans = 10
cmap = "tab10"
raw_pixel_array = vignettes_path + "/MISAR-seq_barcode.csv"
colormap = {"0": "#1F77B4", "1": "#FF7F0E", "2": "#2CA02C", "3": "#D62728",
            "4": "#9467BD", "5": "#8C564B", "6": "#E377C2", "7": "#7F7F7F",
            "8": "#BCBD22", "9": "#17BECF"}

niche = downstream.FindNiche(output_path)
niche.Kmeans(output_path + "/Predict/Rawpredict_detail.txt", output_path + "/Predict/Imputedpredict_detail.txt", n_kmeans)
niche.TSNE_Plot(output_path + "/Predict/", output_path + "/Downstream/kmeans_label.txt", n_kmeans, cmap)
niche.process_xy_dbit(origin_set, gap_length, raw_pixel_array, output_path + "/fill_full_list.txt")
niche.SpatialNiche_dbit(output_path + "/Downstream/kmeans_label.txt", colormap)