{ "cells": [ { "cell_type": "markdown", "id": "65da9095", "metadata": {}, "source": [ "# Tutorial 7: Morphology-Aware Analysis of Subcellular Protein Localization in Large Datasets (dCellAligner-OT)" ] }, { "cell_type": "markdown", "id": "1b6d513c", "metadata": {}, "source": "The Fused Gromov-Wasserstein mapping between two cells with 1,000 points takes around 3 s to compute on a standard desktop computer, whereas the optimal transport (OT) distance between the two mapped distributions takes around 18 ms. Although the number of Fused Gromov-Wasserstein mapping computations scales linearly with the number of cells and the number of OT distance computations between mapped distributions scales quadratically, this can result in long runtimes for datasets with hundreds of thousands of cells.\n\nFor these large datasets, CAJAL provides a deep learning framework, dCellAligner-OT, to reduce the required computation. This approach allows users to compute CellAligner mappings and OT distances for only a subset of cells, then train a deep learning model to predict the mappings and distances for the remaining cells. \n\nWe will demonstrate this approach on a dataset of 16,787 neurons with simulated subcellular protein distributions. For this analysis, we assume that the image data has already been processed into `CellAligner_Cell` objects (as described in [Tutorial 6](https://cajal.readthedocs.io/en/stable/notebooks/Example_6.html)), which can be downloaded from this [link](https://www.dropbox.com/scl/fi/mb1wx32lfqiqpu3mkhni9/sim_neuron_cell_objects.zip?rlkey=113rcvxp1qgpp0wbih63phu5t&dl=0)." }, { "cell_type": "code", "execution_count": null, "id": "4ca74919", "metadata": {}, "outputs": [], "source": [ "import os\n", "from cajal.subcellular import *\n", "from cajal.subcellular_dl import *\n", "\n", "data_path = '/workspaces/CellAligner/sim_neuron_cell_objects/cell_objects/' # Path to directory containing cell object files\n", "cell_object_paths = [os.path.join(data_path, fname) for fname in os.listdir(data_path)]\n", "anchor_ind = 658 # index of anchor cell (which other cells are mapped to)\n", "anchor_cell_obj_path = cell_object_paths[anchor_ind]\n", "with open('/workspaces/CellAligner/sim_neuron_cell_objects/anchor_658_mapped_distbs.pickle', 'rb') as file:\n", " mapped_distbs = pickle.load(file)" ] }, { "cell_type": "markdown", "id": "b6ce925d", "metadata": {}, "source": [ "First, we convert the `CellAligne_Cell` objects and their mapped subcellular protein distributions into cell-specific images that can be used to the dCellAligner-OT model. The `make_NN_training_data` function creates two directories, `cell_images` and `mapped_cell_images`, which store the original and mapped cell images, respectively. It also generates a configuration file, `cell_image_processing.json`, containing the image-processing parameters used for alignment, centering, resizing, and related steps. This file must be referenced when applying dCellAligner-OT to new `CellAligner_Cell` objects so that image processing remains consistent." ] }, { "cell_type": "code", "execution_count": null, "id": "6ddb8254", "metadata": {}, "outputs": [], "source": [ "cell_image_path = '/workspaces/CellAligner/sim_neuron_cell_objects/' # Path to directory where generated cell images will be saved\n", "make_NN_training_data(save_path=cell_image_path, \n", " cell_objects=cell_object_paths,\n", " reference_cell_object=anchor_cell_obj_path,\n", " mapped_channel_distributions=mapped_distbs[0], # using the mapped protein distribution\n", " channel='protein', # this should match the mapped distributions used\n", " center='nucleus', # center='cell' when using Fused GW mappings, center='nucleus' when using Unbalanced Fused GW mappings\n", " shape=(256,256), # shape of the output cell images\n", " rescale=False) # rescale=True when using Fused GW mappings, rescale=False when using Unbalanced Fused GW mappings" ] }, { "cell_type": "markdown", "id": "1b03bd80", "metadata": {}, "source": [ "To reduce overfitting, we split the data into training, validation, and test sets. The dCellAligner model does not need to be trained on all possible pairs of training cells, so we compute OT distances between mapped protein distributions for only a subset of cell pairs. In practice, we have observed good performance when training on approximately 10,000 cells and 30,000 cell pairs." ] }, { "cell_type": "code", "execution_count": 17, "id": "b18b026f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing pairwise OT distances:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 35000/35000 [07:26<00:00, 78.44it/s] " ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing pairwise OT distances:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "100%|██████████| 10000/10000 [03:16<00:00, 50.90it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing pairwise OT distances:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5000/5000 [01:43<00:00, 48.46it/s]\n" ] } ], "source": [ "# Generate train/val/test dataset cell pairs\n", "train_pairs, val_pairs, test_pairs = generate_dataset_split_pairs(indices=list(range(len(cell_object_paths))), \n", " n_pairs=[35000, 10000, 5000], # number of cell pairs in train/val/test sets\n", " proportions=[0.7, 0.2, 0.1]) # proportion of cells in train/val/test sets\n", "\n", "# Store unique indices in each set\n", "train_inds = np.unique(train_pairs)\n", "val_inds = np.unique(val_pairs)\n", "test_inds = np.unique(test_pairs)\n", "\n", "# Compute GW-mapped OT distances for all pairs in train/val/test sets\n", "train_ot_dists = gw_mapped_ot_pairwise_parallel(cell_object_paths[anchor_ind], mapped_distbs, \n", " num_processes=12, chunksize=20, index_pairs=train_pairs)[0]\n", "val_ot_dists = gw_mapped_ot_pairwise_parallel(cell_object_paths[anchor_ind], mapped_distbs, \n", " num_processes=12, chunksize=20, index_pairs=val_pairs)[0]\n", "test_ot_dists = gw_mapped_ot_pairwise_parallel(cell_object_paths[anchor_ind], mapped_distbs, \n", " num_processes=12, chunksize=20, index_pairs=test_pairs)[0]\n", "\n", "# Create PairedDataset objects for train/val/test sets for training dCellAligner-OT\n", "train_data = PairedDataset(\n", " image_dir = cell_image_path, \n", " mapped_image_dir = cell_image_path,\n", " distances = train_ot_dists.astype('float32'), \n", " image_pairs = train_pairs,\n", " transform = transforms.Compose([transforms.ToImage(),\n", " transforms.ToDtype(torch.float32)]),\n", ")\n", "\n", "val_data = PairedDataset(\n", " image_dir = cell_image_path, \n", " mapped_image_dir = cell_image_path,\n", " distances = val_ot_dists.astype('float32'), \n", " image_pairs = val_pairs,\n", " transform = transforms.Compose([transforms.ToImage(),\n", " transforms.ToDtype(torch.float32)]),\n", ")\n", "\n", "test_data = PairedDataset(\n", " image_dir = cell_image_path, \n", " mapped_image_dir = cell_image_path,\n", " distances = test_ot_dists.astype('float32'), \n", " image_pairs = test_pairs,\n", " transform = transforms.Compose([transforms.ToImage(),\n", " transforms.ToDtype(torch.float32)]),\n", ")" ] }, { "cell_type": "markdown", "id": "112f9ac4", "metadata": {}, "source": [ "We initialize the dCellAligner-OT model and begin the two-stage training process. During the first stage, or pretraining, the model learns to approximate the mapping operation. More specifically, for each cell, the model learns to predict the subcellular protein distribution after mapping to the anchor cell.\n", "\n", "In our benchmark, dCellAligner-OT pretraining took approximately 24 hours on an NVIDIA RTX 4500 Ada GPU." ] }, { "cell_type": "code", "execution_count": null, "id": "e95eaaf0", "metadata": {}, "outputs": [], "source": [ "model = dCellAlignerNetwork(embedding_size=50, image_size=image_shape[0])\n", "model = pretrain_model(train_data, model, dataset_name='sim_neuron', batch_size=8, epochs=50, lr=1e-3, \n", " device='cuda', save_path='/workspaces/CellAligner/sim_neuron_cell_objects/pretrained_model/', return_model=True) # Replace with path to save the pretrained model" ] }, { "cell_type": "markdown", "id": "bd50f6b6", "metadata": {}, "source": [ "Next, during training, the model learns to extract features that preserve CellAligner-OT distances between cells in the latent feature space, while continuing to predict the mapped protein distributions.\n", "\n", "The model is optimized with respect to two main loss components. The distance loss measures how well CellAligner-OT distances are preserved in the model’s latent feature space, whereas the reconstruction loss measures how accurately the model predicts the mapped protein distributions. The `dist_weight` parameter controls the relative weighting of these loss components during training. Ideally, the contributions of the two losses, which can be inspected by setting `show_loss_components = True`, should be of similar order of magnitude.\n", "\n", "To reduce overfitting, we apply L1 regularization, controlled by `weight_decay`, and L2 regularization, controlled by `sparsity_weight` and `sparsity_target`. If the dCellAligner-OT model is overfitting, users can try increasing `weight_decay` or `sparsity_weight`, or decreasing `sparsity_target`, to regularize the model more strongly.\n", "\n", "In our benchmark, dCellAligner-OT training took approximately 48 hours on an NVIDIA RTX 4500 Ada GPU." ] }, { "cell_type": "code", "execution_count": null, "id": "6704bb14", "metadata": {}, "outputs": [], "source": [ "# Train the dCellAligner-OT with the prepared datasets\n", "models, train_losses, val_losses = train_dCellAligner(\n", " train_data, val_data, test_data,\n", " save_path='/workspaces/CellAligner/sim_neuron_cell_objects/fully_trained_model/', # Replace with path to save the fully trained model\n", " dataset_name='sim_neuron',\n", " embedding_size=50, # 50-dimensional embeddings\n", " image_shape=(256, 256), # Input image shape\n", " device='cuda', # Use GPU if available\n", " batch_size=8, # Batch size for training\n", " epochs=25, # Number of epochs\n", " learning_rate=0.001, # Adam learning rate\n", " dist_weight=0.1, # Distance weight (vs reconstruction loss)\n", " early_stopping=False, # Disable early stopping\n", " weight_decay=1e-4, # L2 regularization weight\n", " lr_gamma=0.95, # Learning rate decay factor\n", " sparsity_weight=1e-3, # Sparsity weight for the embedding loss\n", " sparsity_target=0.1, # Target sparsity for the embedding loss\n", " pretrained_path=\"/workspaces/CellAligner/sim_neuron_cell_objects/pretrained_model/sim_neuron_pretrained_best.pth\"\n", ")" ] }, { "cell_type": "markdown", "id": "5a5a11d0", "metadata": {}, "source": [ "The train_dCellAligner function saves two model checkpoints: `_best.pth`, corresponding to the best validation-set performance, and `_final.pth`, corresponding to the model after the final training epoch. Here, we load the best-performing model based on validation loss." ] }, { "cell_type": "code", "execution_count": 18, "id": "cff2c062", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.12/site-packages/cajal/subcellular_dl.py:1276: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " checkpoint = torch.load(checkpoint_path, map_location=device)\n", "/opt/conda/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/opt/conda/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_B4_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B4_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n", "Downloading: \"https://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pth\" to /home/jovyan/.cache/torch/hub/checkpoints/efficientnet_b4_rwightman-23ab8bcd.pth\n", "100%|██████████| 74.5M/74.5M [00:09<00:00, 8.55MB/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Deep CellAligner model from /workspaces/CellAligner/sim_neuron_cell_objects/fully_trained_model/sim_neuron_best.pth\n", "Config: {'input_channels': 3, 'embedding_size': 50, 'image_size': 256}\n" ] } ], "source": [ "# load best model\n", "model = load_dCellAligner_model('/workspaces/CellAligner/sim_neuron_cell_objects/fully_trained_model/sim_neuron_best.pth')" ] }, { "cell_type": "markdown", "id": "fefc2421", "metadata": {}, "source": [ "To evaluate model performance, we consider two criteria: how well the dCellAligner-OT feature space preserves the true CellAligner-OT distances, and how accurately the model predicts the mapped protein distributions." ] }, { "cell_type": "code", "execution_count": 20, "id": "f7cf85e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting embeddings for 1676 unique images...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Extracting embeddings: 100%|██████████| 27/27 [15:59<00:00, 35.52s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing distances for 5000 pairs...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Computing pairwise distances: 100%|██████████| 5000/5000 [00:00<00:00, 101063.18it/s]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import umap as umap\n", "import plotly.express\n", "\n", "# load cell metadata\n", "cell_metadata = pd.read_csv('/workspaces/CellAligner/sim_neuron_cell_objects/cell_metadata.csv', index_col=0)\n", "\n", "embeddings = extract_embeddings(model, test_data)\n", "\n", "# Compute UMAP representation\n", "reducer = umap.UMAP(random_state=1)\n", "umap_coords = reducer.fit_transform(embeddings)\n", "\n", "plotly.express.scatter(x=umap_coords[:,0],\n", " y=umap_coords[:,1],\n", " template=\"simple_white\",\n", " hover_name=cell_metadata.iloc[np.unique(test_pairs)]['hpa_crop_id'],\n", " color=cell_metadata.iloc[np.unique(test_pairs)]['hpa_locations'])" ] }, { "cell_type": "markdown", "id": "8bd79ee7", "metadata": {}, "source": [ "The model evaluations above used cell images for which anchor-cell mappings and OT distances had already been computed. After training, however, dCellAligner-OT can be applied directly to new `CellAligner_Cell` objects to infer anchor-cell mappings and estimate CellAligner-OT distances much more efficiently." ] }, { "cell_type": "code", "execution_count": 26, "id": "409c8591", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Mapping cells: 100%|██████████| 1/1 [00:04<00:00, 4.09s/it]\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Select random cells to visualize\n", "np.random.seed(0)\n", "cells_to_plot = np.random.choice(len(cell_object_paths), size=3, replace=False)\n", "new_cell_object_paths = [cell_object_paths[i] for i in cells_to_plot]\n", "\n", "# Use dCellAligner model to map cells to anchor cell\n", "mapped_images = deep_map_to_anchor_cell(model, new_cell_object_paths, process_info_path=os.path.join(cell_image_path, 'cell_image_processing.json'), channel='protein')\n", "\n", "fig, axes = plt.subplots(1, len(new_cell_object_paths), figsize=(15, 5))\n", "for ax, i in zip(axes, range(len(new_cell_object_paths))):\n", " ax.imshow(mapped_images[i])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "id": "ad6612fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting embeddings for 3 images...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Extracting embeddings: 100%|██████████| 1/1 [00:03<00:00, 3.41s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing distance matrix for 3 cells:\n", "[[ 0. 14.308745 4.5295916]\n", " [14.308745 0. 13.887374 ]\n", " [ 4.5295916 13.887374 0. ]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Compute pairwise distances for CellAligner_Cell objects\n", "distances = predict_distances(model, new_cell_object_paths, channel='protein', process_info_path=os.path.join(cell_image_path, 'cell_image_processing.json'))\n", "print(distances)" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 5 }