{ "cells": [ { "cell_type": "markdown", "id": "939102b0-2779-4dbd-adfd-64eb06c39a13", "metadata": {}, "source": [ "# ST-Gears\n", "\n", "ST-Gears is a strong 3D reconstruction tool for Spatial Transcriptomics, with accurate position alignment plus distortion correction [[Xia23](https://www.biorxiv.org/content/10.1101/2023.12.09.570320v1)].\n", "\n", "ST-Gears consists of methods to compute anchors, to align rigidly and to registrate sections elastically:\n", "\n", "1. `st_gears` the main method which computes mappings between adjacent sections in serial, using Fused-Gromov Wasserstein Optimal Transport with our innovatie Distributive Constraints.\n", " \n", "2. `stack_slices_pairwise_rigid` rigidly aligns sections using Procrustes Analysis.\n", " \n", "3. `stack_slices_pairwise_elas_field` eliminates distorsions through Gaussian Smoothed Elastic Fields. Validity is proved mathematically.\n", "\n", "This function can be ran on GPU, if you want to use GPU, you need to create an environment according to the guide [Clustering_by_GPU](../Tutorials/Clustering_by_GPU.html).\n", "\n", "Before anlysing, you also need to install a necessary package: **torch**\n", "\n", " CPU: pip install torch==1.10.0\n", "\n", " GPU: pip install torch==1.10.0+cu113 --extra-index-url https://download.pytorch.org/whl/" ] }, { "cell_type": "code", "execution_count": 1, "id": "4bf7a964-4f0b-4ac1-ad62-51a8349bf4ee", "metadata": { "execution": { "iopub.execute_input": "2024-05-21T09:22:22.371057Z", "iopub.status.busy": "2024-05-21T09:22:22.370856Z", "iopub.status.idle": "2024-05-21T09:22:27.150127Z", "shell.execute_reply": "2024-05-21T09:22:27.149378Z", "shell.execute_reply.started": "2024-05-21T09:22:22.371041Z" } }, "outputs": [ { "data": { "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof 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"application/vnd.holoviews_load.v0+json": "" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import stereo as st\n", "from stereo.core.ms_data import MSData\n", "from natsort import natsorted\n", "from glob import glob" ] }, { "cell_type": "markdown", "id": "31607926-214c-4c65-ab8f-0628ee8cea4c", "metadata": {}, "source": [ "## Reading Data\n", "\n", "Download our [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f)" ] }, { "cell_type": "code", "execution_count": 2, "id": "9f5d9b71-0e33-4b77-82ea-8c268e8edadd", "metadata": { "execution": { "iopub.execute_input": "2024-05-21T09:22:27.151345Z", "iopub.status.busy": "2024-05-21T09:22:27.150974Z", "iopub.status.idle": "2024-05-21T09:22:28.542049Z", "shell.execute_reply": "2024-05-21T09:22:28.541521Z", "shell.execute_reply.started": "2024-05-21T09:22:27.151328Z" } }, "outputs": [ { "data": { "text/plain": [ "ms_data: {'0': (805, 114), '1': (1051, 114), '2': (1188, 114), '3': (1206, 114), '4': (1448, 114), '5': (1487, 114), '6': (1584, 114), '7': (1652, 114), '8': (1748, 114), '9': (1766, 114), '10': (1649, 114), '11': (1667, 114), '12': (1783, 114), '13': (1730, 114), '14': (1810, 114), '15': (1812, 114), '16': (1680, 114), '17': (1692, 114), '18': (1586, 114), '19': (1634, 114), '20': (1604, 114), '21': (1531, 114), '22': (1527, 114), '23': (1676, 114), '24': (1399, 114), '25': (1388, 114), '26': (1221, 114), '27': (1149, 114), '28': (1163, 114), '29': (1172, 114), '30': (1023, 114), '31': (943, 114), '32': (847, 114), '33': (754, 114), '34': (751, 114), '35': (620, 114), '36': (629, 114), '37': (594, 114), '38': (509, 114), '39': (441, 114)}\n", "num_slice: 40\n", "names: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39']\n", "obs: []\n", "var: []\n", "relationship: other\n", "var_type: intersect to 0\n", "mss: []" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_dir = '../data/mouse_brain/'\n", "files_list = natsorted(glob(f'{data_dir}/*.h5ad'))\n", "data_list = [st.io.read_h5ad(path) for path in files_list]\n", "ms_data = MSData(_data_list=data_list)\n", "ms_data" ] }, { "cell_type": "markdown", "id": "7f1458ba-a19f-4154-b58f-b661409c284c", "metadata": { "execution": { "iopub.execute_input": "2024-05-21T08:53:25.991469Z", "iopub.status.busy": "2024-05-21T08:53:25.990924Z", "iopub.status.idle": "2024-05-21T08:53:26.058224Z", "shell.execute_reply": "2024-05-21T08:53:26.057020Z", "shell.execute_reply.started": "2024-05-21T08:53:25.991423Z" } }, "source": [ "## Showing the spatial distribution of cells type.\n", "\n", "Here we take sample 8 to sample 11 as example.\n", "\n", "We can see the last two samples are not aligned with the first two." ] }, { "cell_type": "code", "execution_count": 3, "id": "1b66acea-10b7-46e9-bdb4-d1cf35359728", "metadata": { "execution": { "iopub.execute_input": "2024-05-21T09:22:28.543467Z", "iopub.status.busy": "2024-05-21T09:22:28.543071Z", "iopub.status.idle": "2024-05-21T09:22:29.377145Z", "shell.execute_reply": "2024-05-21T09:22:29.375929Z", "shell.execute_reply.started": "2024-05-21T09:22:28.543449Z" } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "