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SpatialAE Demo

import spatialae as sa

(Running SpatialAE 0.1.0)
2024-03-08 18:31:39

Overview of the example dataset

example data download from https://github.com/labsyspharm/mcmicro/actions/runs/8041633029 exemplar-001.zip

import dask.array as da
import tifffile

imagePath = "/Users/rthuang/Documents/spatialaeExampleData/exemplar-001/registration/exemplar-001.ome.tif"

original_image = da.from_zarr(tifffile.imread(pathlib.Path(imagePath), aszarr=True, level=0))

original_image
Array Chunk
Bytes 180.35 MiB 2.00 MiB
Shape (12, 3138, 2511) (1, 1024, 1024)
Dask graph 144 chunks in 2 graph layers
Data type uint16 numpy.ndarray
2511 3138 12 
markerIndex = 0

zimage = da.from_zarr(tifffile.imread(pathlib.Path(imagePath), aszarr=True, level=0, key=markerIndex))

zimage
Array Chunk
Bytes 15.03 MiB 2.00 MiB
Shape (3138, 2511) (1024, 1024)
Dask graph 12 chunks in 2 graph layers
Data type uint16 numpy.ndarray
2511 3138 
markerIndex = 11

zimage = da.from_zarr(tifffile.imread(pathlib.Path(imagePath), aszarr=True, level=0, key=markerIndex))

zimage
Array Chunk
Bytes 15.03 MiB 2.00 MiB
Shape (3138, 2511) (1024, 1024)
Dask graph 12 chunks in 2 graph layers
Data type uint16 numpy.ndarray
2511 3138

Cropping image

default first channel

spatialTablePath= '/Users/rthuang/Documents/spatialaeExampleData/exemplar-001/quantification/exemplar-001--unmicst_cell.csv'
imagePath= '/Users/rthuang/Documents/spatialaeExampleData/exemplar-001/registration/exemplar-001.ome.tif'
projectDir = '/Users/rthuang/Documents/spatialaeExampleData/'

# if not specify any channel marker mapping, default crop the first channel as DNA1
sa.generateSinglePatch(spatialTablePath, imagePath, projectDir=projectDir, cell_indicator='CellID')

generateSinglePatch starting
Processing Marker: DNA1
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subimages have been generated, head over to "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch" to view results

Multiple specified channels

## specify markerChannelMapPath to crop multiple channels' images.
spatialTablePath= '/Users/rthuang/Documents/spatialaeExampleData/exemplar-001/quantification/exemplar-001--unmicst_cell.csv'
imagePath= '/Users/rthuang/Documents/spatialaeExampleData/exemplar-001/registration/exemplar-001.ome.tif'
projectDir = '/Users/rthuang/Documents/spatialaeExampleData/'

markerChannelMapPath = "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/data/markers.csv"
sa.generateSinglePatch(spatialTablePath, imagePath, markerChannelMapPath = markerChannelMapPath, markers = ["DNA_6", "ELANE", "CD57", 'CD45'], 
                        projectDir = projectDir, cell_indicator='CellID', image_fraction = 0.01)

generateSinglePatch starting
Processing Marker: DNA_6
Processing Marker: ELANE
Processing Marker: CD57
Processing Marker: CD45
subimages have been generated, head over to "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch" to view results

Modelling

single channel

input_dim = 256 # 16*16
encoding_dim = 64
max_epoch_num = 1
batch_size = 8
# dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatc/DNA1/"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/DNA_6/"
outModelPath = '/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_DNA_validate_1_model.pth'

sa.models.aeTrain(dataset_dir, outModelPath,  input_dim, encoding_dim,  max_epoch_num, batch_size)

GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name      | Type       | Params
-----------------------------------------
0 | criterion | MSELoss    | 0     
1 | encoder   | Sequential | 41.2 K
2 | decoder   | Sequential | 41.3 K
-----------------------------------------
82.5 K    Trainable params
0         Non-trainable params
82.5 K    Total params
0.330     Total estimated model params size (MB)


Train_data
Validate_data
Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:05<00:00,  2.03it/s, v_num=5](Running SpatialAE 0.1.0)
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Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  0.74it/s, v_num=5]

`Trainer.fit` stopped: `max_epochs=1` reached.


Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:16<00:00,  0.74it/s, v_num=5]
(Running SpatialAE 0.1.0)
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input_dim = 256 # 16*16
encoding_dim = 64
max_epoch_num = 2
batch_size = 8
# dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatc/DNA1/"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/DNA_6/"
outModelPath = '/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_DNA_validate_2_model.pth'

sa.models.aeTrain(dataset_dir, outModelPath,  input_dim, encoding_dim,  max_epoch_num, batch_size)

GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name      | Type       | Params
-----------------------------------------
0 | criterion | MSELoss    | 0     
1 | encoder   | Sequential | 41.2 K
2 | decoder   | Sequential | 41.3 K
-----------------------------------------
82.5 K    Trainable params
0         Non-trainable params
82.5 K    Total params
0.330     Total estimated model params size (MB)


Train_data
Validate_data


/Users/rthuang/miniconda3/envs/spatialAE/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (12) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.


Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.83it/s, v_num=1](Running SpatialAE 0.1.0)
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Epoch 1:   0%|                                                                                                                                                              | 0/12 [00:00<?, ?it/s, v_num=1](Running SpatialAE 0.1.0)
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Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:50<00:00,  0.24it/s, v_num=1](Running SpatialAE 0.1.0)
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Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [01:05<00:00,  0.18it/s, v_num=1]

`Trainer.fit` stopped: `max_epochs=2` reached.


Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [01:05<00:00,  0.18it/s, v_num=1]
(Running SpatialAE 0.1.0)
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extract embedding

input_dim = 256  #  (16x16)
encoding_dim = 64
batch_size = 32
embeddingSavePath="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/"
embeddingSaveName="latent_embedding_DNA.csv"

ModelPath = "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_DNA_validate_2_model.pth"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/DNA_6/"
multi = False

sa.datasets.extractEmbedding(ModelPath, dataset_dir,
                     embeddingSavePath,
                     embeddingSaveName,
                     input_dim = input_dim,
                     encoding_dim = encoding_dim,
                     batch_size = batch_size,
                     multi=multi)

Embeddings have been written to /Users/rthuang/Documents/spatialaeExampleData/SpatialAE/latent_embedding_DNA.csv

multiple channel

dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/"
outModelPath = '/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_multi_validate_2_model.pth'
channels = ["DNA_6", "ELANE", "CD57", 'CD45']
input_dim=256
encoding_dim=64
batch_size=32
max_epoch_num=2
num_workers = 10
prefetch_factor = 8

sa.aeTrainMulti(dataset_dir, outModelPath, channels, input_dim, encoding_dim, max_epoch_num, batch_size, num_workers, prefetch_factor)

GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name      | Type       | Params
-----------------------------------------
0 | criterion | MSELoss    | 0     
1 | encoder   | Sequential | 139 K 
2 | decoder   | Sequential | 140 K 
-----------------------------------------
279 K     Trainable params
0         Non-trainable params
279 K     Total params
1.119     Total estimated model params size (MB)


Warning: '/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/common_files.txt' not found. Falling back to finding common files.
Save files: '/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/common_files.txt' 
Train_data
Validate_data


/Users/rthuang/miniconda3/envs/spatialAE/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (3) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.


Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:07<00:00,  0.38it/s, v_num=2](Running SpatialAE 0.1.0)
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(Running SpatialAE 0.1.0)
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(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
(Running SpatialAE 0.1.0)
2024-03-08 16:38:12
Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:15<00:00,  0.20it/s, v_num=2](Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:21
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22
(Running SpatialAE 0.1.0)
2024-03-08 16:38:22

Validation: |                                                                                                                                                                         | 0/? [00:00<?, ?it/s]
Validation:   0%|                                                                                                                                                                     | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0:   0%|                                                                                                                                                        | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 154.59it/s]
Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:22<00:00,  0.14it/s, v_num=2]

`Trainer.fit` stopped: `max_epochs=2` reached.


Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:22<00:00,  0.14it/s, v_num=2]
(Running SpatialAE 0.1.0)
2024-03-08 16:38:28
(Running SpatialAE 0.1.0)
2024-03-08 16:38:28
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:28
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:29
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:43
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44
(Running SpatialAE 0.1.0)
2024-03-08 16:38:44

input_dim = 256  #  (16x16)
encoding_dim = 64
batch_size = 32
embeddingSavePath="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/"
embeddingSaveName="latent_embedding_multi.csv"

ModelPath = "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_multi_validate_2_model.pth"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/"
multi = True
channels = ["DNA_6", "ELANE", "CD57", 'CD45']


sa.datasets.extractEmbedding(ModelPath, dataset_dir,
                     embeddingSavePath,
                     embeddingSaveName,
                     input_dim = input_dim,
                     encoding_dim = encoding_dim,
                     batch_size = batch_size,
                     multi = multi,
                     channels = channels)

Embeddings have been written to /Users/rthuang/Documents/spatialaeExampleData/SpatialAE/latent_embedding_multi.csv

Visualization

# One channel visualization[DNA1]
multi = False
ModelPath = "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_DNA_validate_2_model.pth"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/DNA_6/"
batch_size = 8
# adata = sc.read(adataPath)
# select_int_list = adata.obs[adata.obs["leiden0.4"] == "4"]["cell"].values.tolist()
# select_str_list = [str(i) for i in select_int_list]

import pandas as pd
embedding  = pd.read_csv("/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/latent_embedding_DNA.csv", index_col=0)
select_int_list = embedding.index.values.tolist()
select_str_list = [str(i) for i in select_int_list]



iterNum = 1
sa.visualizeImages(ModelPath,
                    dataset_dir,
                    searchStrList = select_str_list,
                    input_dim = 256,
                    encoding_dim = 64,
                    batch_size = batch_size,
                    imageViewSize = 16,
                    iterNum = iterNum)

matching_imagefiles: 112

png

iterNum = 2
sa.visualizeImages(ModelPath,
                    dataset_dir,
                    searchStrList = select_str_list,
                    input_dim = 256,
                    encoding_dim = 64,
                    batch_size = batch_size,
                    imageViewSize = 16,
                    iterNum = iterNum)

matching_imagefiles: 112

png



# Multichannels  visualization
multi = True
ModelPath = "/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/models/ln_autoencoder_multi_validate_2_model.pth"
dataset_dir="/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/"
batch_size = 6
# adata = sc.read(adataPath)
# select_int_list = adata.obs[adata.obs["leiden0.4"] == "4"]["cell"].values.tolist()
# select_str_list = [str(i) for i in select_int_list]

import pandas as pd
cellidx  = pd.read_csv("/Users/rthuang/Documents/spatialaeExampleData/SpatialAE/SinglePatch/common_files.txt", header = None)
select_int_list = cellidx[0].str.split("_").str[2].str.split(".").str[0].tolist()
select_str_list = [str(i) for i in select_int_list]
channels = ["DNA_6", "ELANE", "CD57", 'CD45']

iterNum =1
sa.visualizeImages(ModelPath,
                    dataset_dir,
                    searchStrList = select_str_list,
                    input_dim = 256,
                    encoding_dim = 64,
                    batch_size = batch_size,
                    imageViewSize = 16,
                    iterNum = iterNum,
                    multi = multi,
                    channels = channels)

matching_imagefiles: 112

png

iterNum =2
sa.visualizeImages(ModelPath,
                    dataset_dir,
                    searchStrList = select_str_list,
                    input_dim = 256,
                    encoding_dim = 64,
                    batch_size = batch_size,
                    imageViewSize = 16,
                    iterNum = iterNum,
                    multi = multi,
                    channels = channels)

matching_imagefiles: 112

png