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  {
   "cell_type": "markdown",
   "id": "c57e9685-eb1d-46bb-ab38-12ff55cefb1a",
   "metadata": {},
   "source": [
    "# Spatial Alignment\n",
    "\n",
    "Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose **Spatial Alignment**, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics\n",
    "\n",
    "After alignment, we will get a new expression matrix and a reduced dimensional matrix, we can use them to do others analysis such as Neighbors, UMAP, Cluster,  etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cceaa02",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Preparation**\n",
    "\n",
    "In order to use this function, you need to install **stereopy** with some necessary dependencies:\n",
    "\n",
    "    CPU: pip install stereopy[spatialign] --extra-index-url https://download.pytorch.org/whl  \n",
    "\n",
    "    GPU(CUDA11): pip install stereopy[spatialign-cu11] --extra-index-url https://download.pytorch.org/whl  \n",
    "    \n",
    "    GPU(CUDA12): pip install stereopy[spatialign-cu12] --extra-index-url https://download.pytorch.org/whl  \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1288b65",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d6873d7-7f55-4295-b992-7b26af1bb4aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:31:54.481583Z",
     "iopub.status.busy": "2025-02-20T09:31:54.480979Z",
     "iopub.status.idle": "2025-02-20T09:32:01.108874Z",
     "shell.execute_reply": "2025-02-20T09:32:01.108358Z",
     "shell.execute_reply.started": "2025-02-20T09:31:54.481555Z"
    },
    "tags": []
   },
   "outputs": [
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      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import stereo as st\n",
    "from stereo.core.ms_pipeline import slice_generator\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fb52df",
   "metadata": {},
   "source": [
    "Please download our [example data](http://upload.dcs.cloud:8090/share/bb6fab82-2c16-46b2-a95e-6931338f31bf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e6b4ebd-e706-407e-878d-4e8acdd52162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:01.110154Z",
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     "shell.execute_reply": "2025-02-20T09:32:01.528027Z",
     "shell.execute_reply.started": "2025-02-20T09:32:01.110137Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ms_data: {'0': (1123, 27825), '1': (1184, 32285)}\n",
       "num_slice: 2\n",
       "names: ['0', '1']\n",
       "merged_data: None\n",
       "obs: []\n",
       "var: []\n",
       "relationship: other\n",
       "var_type: intersect to 0\n",
       "current_mode: integrate\n",
       "current_scope: scope_[0,1]\n",
       "scopes_data: []\n",
       "mss: []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = st.io.read_h5ad('../../data/BatchEval_spatiAlign_stereoAlign_demo_datasets/stereo_olfactory_bulb_ann.h5ad')\n",
    "data2 = st.io.read_h5ad('../../data/BatchEval_spatiAlign_stereoAlign_demo_datasets/visium_olfactory_bulb_ann.h5ad')\n",
    "\n",
    "ms_data = data1 + data2\n",
    "ms_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861b824c-ee22-4c3b-9742-034edbc09c63",
   "metadata": {},
   "source": [
    "## Merging Data\n",
    "\n",
    "If there are no z-coordinate in the input data, you can specify the distance between each sample by parameter `space_between` to calculate the z-coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbafb491-5864-4e88-811c-3c7a3c3596d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:01.529156Z",
     "iopub.status.busy": "2025-02-20T09:32:01.529034Z",
     "iopub.status.idle": "2025-02-20T09:32:01.675416Z",
     "shell.execute_reply": "2025-02-20T09:32:01.674955Z",
     "shell.execute_reply.started": "2025-02-20T09:32:01.529145Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnBasedStereoExpData object with n_cells X n_genes = 2307 X 26760\n",
       "adata: id(140717206027232)\n",
       "bin_type: bins\n",
       "bin_size: 1\n",
       "offset_x = None\n",
       "offset_y = None\n",
       "cells: ['cell_name', 'batch']\n",
       "genes: ['gene_name']\n",
       "cells_matrix = ['spatial']\n",
       "Layers with keys: \n",
       "tl.result: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_data.integrate(space_between='1um')\n",
    "ms_data.merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2edaec35-a856-4d24-a2e7-1e37cde70b23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:01.676449Z",
     "iopub.status.busy": "2025-02-20T09:32:01.676194Z",
     "iopub.status.idle": "2025-02-20T09:32:01.678673Z",
     "shell.execute_reply": "2025-02-20T09:32:01.678240Z",
     "shell.execute_reply.started": "2025-02-20T09:32:01.676438Z"
    }
   },
   "outputs": [],
   "source": [
    "ms_data.tl.set_scope_and_mode(scope=slice_generator[:], mode='integrate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a834b3c-6649-4e66-a668-23b3c9eecaf1",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50ecef8d-c400-4ce9-82fa-dde59cd170f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:01.679450Z",
     "iopub.status.busy": "2025-02-20T09:32:01.679329Z",
     "iopub.status.idle": "2025-02-20T09:32:01.817930Z",
     "shell.execute_reply": "2025-02-20T09:32:01.817387Z",
     "shell.execute_reply.started": "2025-02-20T09:32:01.679440Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:01][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run cal_qc\n",
      "[2025-02-20 17:32:01][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run cal_qc...\n",
      "[2025-02-20 17:32:01][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: cal_qc end, consume time 0.1102s.\n",
      "[2025-02-20 17:32:01][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run raw_checkpoint\n"
     ]
    }
   ],
   "source": [
    "ms_data.tl.cal_qc()\n",
    "ms_data.tl.raw_checkpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "104322e9-496d-4e51-af48-e7c2cbcc8cc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:01.818662Z",
     "iopub.status.busy": "2025-02-20T09:32:01.818517Z",
     "iopub.status.idle": "2025-02-20T09:32:02.362854Z",
     "shell.execute_reply": "2025-02-20T09:32:02.361969Z",
     "shell.execute_reply.started": "2025-02-20T09:32:01.818651Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:01][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run spatial_scatter\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x312.101 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_data.plt.spatial_scatter(show_plotting_scale=False, reorganize_coordinate=3, horizontal_offset_additional=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bf6ce6a-78eb-4486-8c6e-e18aff7e4990",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:02.363857Z",
     "iopub.status.busy": "2025-02-20T09:32:02.363654Z",
     "iopub.status.idle": "2025-02-20T09:32:02.929010Z",
     "shell.execute_reply": "2025-02-20T09:32:02.928503Z",
     "shell.execute_reply.started": "2025-02-20T09:32:02.363842Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run normalize_total\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run normalize_total...\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: normalize_total end, consume time 0.0656s.\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run log1p\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run log1p...\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: log1p end, consume time 0.4918s.\n"
     ]
    }
   ],
   "source": [
    "# ms_data.tl.filter_cells(min_genes=20)\n",
    "# ms_data.tl.filter_genes(min_cells=20)\n",
    "ms_data.tl.normalize_total()\n",
    "ms_data.tl.log1p()\n",
    "# ms_data.tl.scale(zero_center=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3649a3-199a-4222-bc2c-0a3337f969a9",
   "metadata": {},
   "source": [
    "## UMAP without alignment\n",
    "\n",
    "We can use UMAP to inspect the effect of Spatial Alignment Integration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ae1101e-0540-46aa-8ce6-84613710f553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:02.929635Z",
     "iopub.status.busy": "2025-02-20T09:32:02.929502Z",
     "iopub.status.idle": "2025-02-20T09:32:38.679766Z",
     "shell.execute_reply": "2025-02-20T09:32:38.679181Z",
     "shell.execute_reply.started": "2025-02-20T09:32:02.929623Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run pca\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run pca...\n",
      "[2025-02-20 17:32:02][Stereo][3375092][MainThread][140719273752384][dim_reduce][78][WARNING]: svd_solver: auto can not be used with sparse input.\n",
      "Use \"arpack\" (the default) instead.\n",
      "[2025-02-20 17:32:29][Stereo][3375092][MainThread][140719273752384][dim_reduce][99][INFO]: x_pca dType is changed from float64 to float32\n",
      "[2025-02-20 17:32:29][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: pca end, consume time 26.7350s.\n",
      "[2025-02-20 17:32:29][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run neighbors\n",
      "[2025-02-20 17:32:29][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run neighbors...\n",
      "2025-02-20 17:32:32.533106: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-02-20 17:32:32.709442: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-02-20 17:32:32.711024: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-02-20 17:32:33.582253: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "[2025-02-20 17:32:36][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: neighbors end, consume time 6.5504s.\n",
      "[2025-02-20 17:32:36][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run umap\n",
      "[2025-02-20 17:32:36][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run umap...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tcompleted  0  /  500 epochs\n",
      "\tcompleted  50  /  500 epochs\n",
      "\tcompleted  100  /  500 epochs\n",
      "\tcompleted  150  /  500 epochs\n",
      "\tcompleted  200  /  500 epochs\n",
      "\tcompleted  250  /  500 epochs\n",
      "\tcompleted  300  /  500 epochs\n",
      "\tcompleted  350  /  500 epochs\n",
      "\tcompleted  400  /  500 epochs\n",
      "\tcompleted  450  /  500 epochs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:38][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: umap end, consume time 2.4514s.\n"
     ]
    }
   ],
   "source": [
    "ms_data.tl.pca(use_highly_genes=False, n_pcs=50, res_key='pca')\n",
    "ms_data.tl.neighbors(pca_res_key='pca', res_key='neighbors')\n",
    "ms_data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78313e52-616b-49f8-b56d-2ffda8837e0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:38.681010Z",
     "iopub.status.busy": "2025-02-20T09:32:38.680413Z",
     "iopub.status.idle": "2025-02-20T09:32:38.944777Z",
     "shell.execute_reply": "2025-02-20T09:32:38.943527Z",
     "shell.execute_reply.started": "2025-02-20T09:32:38.680994Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:38][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run batches_umap\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[2307]},\"y\":{\"__ndarray__\":\"GhUEQS3yFEFzWx5BJXMbQaUTGkHF8AtBb8HeQGUlvkBiGuNAy9TaQDJX50AupjFBheUlQXEjPkHtLEdBv2JPQfDdUkE7x0RBC5wyQRpBGkGECRJBL4ndQJqaYEBB2xpAr0tSvwes7r91lEPALgOJwCXt2L9I8ylAfV/RQDoGGkHLVyJBZFpFQRYsSkGkUUNBXgQyQV12K0HX9DBBcW8iQRmYIEFPVTVBQy40Qeg4SUFJtVlBZ6JhQeRGZ0FlRGBB72ZZQewgT0ERCjtBNJYbQeWj/UDXJ9VASNtbQG9RxD+hM6G/2QIHwIySRcBDjuO/VWguQOAWlUDn3/xAGq8SQYSaO0HUckFBzjVaQelic0FVnHBBR1ViQT4xWEFHVzlBlsVEQdZzUUEObXJBdtF2QdnWeEGaEXxBrz17QZnWd0E0yWhBnHhWQepLQ0HeqSdBinETQS5z9EAdAX5AmlJ2P0/Jh79SeyvANaKkv5qUPsB9FCW/7jnuP4btnEDuyPFAit8OQX6zH0EiQT9BLCJLQWSkVUENCnJBfy93QTdeeEERq3BBizl1QYW2dEEFAXRB7slrQfQobkGRWHBBL656QXxMe0HEzXdB0bpoQU70WUGrijpBHuUaQRmn+0C0Xa5AFjQBQGa3fr/1pSHAX05ovyPaa8DTAhLAB5joPwOIQED2R9ZAxarbQOUKEUHDQShBtlgqQTINOUHtiEtBm+JUQbfoVkEAklJBXs9QQeaCQUEsej5BaWs/QfYmNEF9Ok1BX+1RQRfWYUEWn2lBelJiQWL+YEEbFEdBNFEeQdxKE0E+iM1A90ndP5KNjL/TojPA6HIRwMSqYsBWGfS/s9CHvssVNkB/a6RAjzdwQClHr0DJZ9VAxeABQZYiG0FAVDtBayQoQaSfHUFBqiBBVgMtQejEIUFSshdBVl4RQVFCHUFfrCpB5yArQR8JPkFNVk9B+RReQbmrSEGooUhBOTcyQWCAFUE3uMVAnkhiPysIhr/1u07AIe4awG2Bc78ptyw/nZISQN8utT86VzZAqfiDQFmam0DJE8FA27O3QGOgwkBaXORA7hjVQNGS2EBrycRABzjZQP6KyUDEsN1AUDDnQF+R8UBf4hZBoTEqQZgnPUGcOzJBrS8uQQTwIkF0cfZAyE6EQA6vOz/+hWK/3ItAwLljMMAjAg3A1LKkvfpxn76B5ZE/jNedP0n47z9apBFABbDrPzFADkAJTxJAoYkvQFy25D9K6kJAlvMZQM3ZPkCHS1xAmBWIQE3uvEDMzsJA3E/YQJwmGUFKvxtBBHwZQQ4fD0H86t9AAXIlQPsW2j78wxLAcYwowEB0ncDlHY3AN58NwL2EdMDAmynAMvz7v/3g8T7pNRk/kt+wP/SNeT8mIJs/2Su7P3fDCEB35PU/cnP/P6+HE0CvSypA+GQgQHtwQEDczGFA2kutQDTo0UC5I8ZA9wfVQKEl10CRynZAUjmHPycRh7/YyonASoufwDc3lsC7a5TAhvybwMr1hcCwh0zAbRoRwCetlb+KKrS/dDQrv9ZFl7+Skpq/DH+Gv4LKhb9eF8o+0kinPrtOoz/usQdA48gEQAhIEkAgHS5AqeMrQFkdNUDg6BVA49EEQIDpCT+PHzHA9WMzwFQAmMB39JXAij85wFtsYcB3gTPAT6YpwE9PR8CgnUrAFxgqwEK3QsCrPZnATvCBwJDLoMBs3aLA/MVxwAJleMDm81PAIFFTwGN4ZcDf3YXAjfuIwKqTJsBzjTbAUeizvzpcrr/wxRi/fOtKP4YNlz/lJrg//6nrP0XYND88Yng/7ABcvzwrmL/eEabAR1qlwHiZUMC8J07AfoZHwFlnXcBLaE7AzIVzwNb6W8ABvVPAKSpGwD7GP8Cg01rA/JofwKVwe8DjwGrAQLA+wPf2IsBjRkXAHdITwDYAHcDjbIy/09sDwGj1AsCoSTPAgbaDwJX0W8A6VGXAI+h3wFISbMCIQlfAZeRSwBt3UcANdU/A9YM2wFVUV8Dn3UfAc0FBwMGyPsCNMkjAiNdHwGDyJsDKzbe/T800v9EKcb/8rBLAx1+Sv84zYb/ivV6/sXrHvxWVgsCw+ZfA6jCHwMhOjMDnM4TAA4mAwCDtisAE+obAzL2MwBOpgcC/rUPADuY3wK5/T8ATUFTA3QoxwBghNMD3dzrADCodwGPdCcDe+qG/qacKPrQWW7/i2rG+4FPHvvEMEL9Zkaq/05dIwOK3osCBNqfAQ82nwDy4KcBd0AvAbFCBv0uRgr8165+/Lr/dv2bYqr/F9Jq/MVuXv+kLdb8IkMq/2u+avx4Jbr9+z6g+IqOMP9mTNz8orHE/Z/EZP8I/9z9WKDk/13M4P1dTXj1Vo2y/zDUmwO5FmMDyGKDA7K59wCEsU8CwvgQ/tP0tP6avez88psQ/1dNrP2VqTj98N+a9S9ugP4onvT8aN3U/CluDPxOkrD+XAR1AeD4FQJzANkCzUylAOzqvP1mvXkCcQRtA6QwSQNU0pT7TKJC+Uh+Lv3jwg8BKBobA8A8XwK/X7r9hqYu/RCkFQFTxyT+J1gtAfjoVQK8BJECcthtAfYzvP8eKK0AIjBZAWtYrQJktE0B4Yi1AYkxUQB8/M0BLXE1AWeXDQN0R2kDaqtlAex3MQD7S2UDOjrhA/c0gQJcooT6MVTK/u8hxvyrpjcCpuGfASLcowP/OzL8OokE/A2UdQCi8v0CiNqJACn2/QFXIt0B6UbdA/XS7QMGVq0CQYpFAp/CKQGyFh0CAaYBA/gOJQPeUvUD3H9FA/bbcQCybBEG+jBZBvfcYQUO5FEHrVRBBaU9vwNy3AEEBiqlA63RCPx0SvT3TWUq/TD8zwJY1ocDppiHAg6zJv3EzC72eqhFAEyyoQBxjGkH1UBtBoqUgQd7jHUE6LSNBA3MYQcR2F0FVcQhBn7ENQces4kB6kv9ACkL1QFXE7kCg6RFBPmQcQZ9fKkEUtjdBJ7NAQQJ1PEFt/DJBUFKbwOCpE0HmVcdAIKleP6gbBj94Pya/sfyWvy8hhsDtNdG/plHxPlu2J0AMB7RADUXsQG41LUGAxztBQDxFQRj7REFbw01BIeRCQVHYP0HhqDBBAbY7QdqeK0E2bCdBfW8qQYq8LUF0bjhBJVpGQS2bXUFSBl9BYuZiQYc/TUE8Zz9BNaKlwCmsHEH8yPVAIl1zP9Y5+r01ZU2/fmCHv1sR9L+GYgk/A/ENQDG0p0D4mfZAx+AVQT5+QMD2ApPATIxlwGX+TMCJI6PAgrOCwME9RcAuIh/AmJRQwJMgScA7NzvAi1BDwC80PMBNCC7AMIBLQfWBV0HvrXVBT0ZxQX0rdkH15G5BEEZ3Qf3tcEFjVG1BR4dkQa6VX0FrlFxBg1VjQePVaEHm83RBQwh5QTm8ekEqaXZBaV9aQYYWQ0FCl0jAI7MTQf1g9UB/uRc/OlwBv5EWD78jBMq/Unc/v8VbtD/nbnFAuMnVQJb6DEFjyjlBuk5VQY+EYUHK0HNB5y5uQYFEa0FEeGZBcvNnQbkMaEGph3RBDQV7QdtLfEFSfXtBna99QcTKekGR6HlBVyF5QS20eUHKQWVBLU9XQZK+OUGY1zvAZvcPQR9T4UAxGQ2/SkArv07nD7/HZnG/+1PWP9NzdEAeMPNA8eIUQU95QUEz/1JB3wNXQV9wTkH6+B7Au4UyQRo8SEFQA0BB1GdTQWRdTUFs+1ZBzLJgQaSddkEhr3hBlRl3QbE9eEE26nNBD4xrQTBmV0HcbzVBDQoZQbgyK8CMgf1As/KXQEd8z74dWxW/sss3v/Xm6L8jGzVAgvt+QDhR1UAUAiFBRgJCQS9fOEE04jZB5+AyQZ3zJkFhMTBBGNkyQbrPK0Gv8jtBvo04QccCPkGubk1Br+VMQWKtXkF49WBB/6pcQQ7lU0Evx01Br448Qb4xJUH9mApBXj46wIMM4kAEcpM+Itouv8uVnb+9CQjAhZotP4kmOUDOR8VAjxEqQYFXIEH48s9AUGzMQFZN3UAaJ75ANkhsQLKywECpOtpAyxMSQbFSGUEVSBlBaOMtQS3wPEEY9UhB1BRQQcqzSUH5Kj1BdQI3QVVxHEFLWxxBFfr9QNbtnUBhaSy/Fskiv/e6EMB/PyXAocnuv5a8gD/4aHJAFS3VQEKt7UB2W55AE51uQBZRsD/5A1Y/hsxRQAJMa0AdFbVAyEC7QPWo5kAEkBVBLO4XQXjFHEFc5zVBBJI0QTE1MUF1ui5BlXkrQcr2FkGsH+9ADBnYQNa+Zj8jLVy/g3yPv/XyNMDdrw3AHBFbP/bvFUAqMlhAwoaoQMF3pD9BI+s/nQnqP61gMkCLXzNAdFdsPyQUSkCHMlxAeyvKQHJD5kBfrgVB1f8QQY/9G0ElQhlBtl0dQSyY0UCaTfRArWfbQMNuQUB8JwO/4qMEv5ZsWcCz7gjASmXNPuGd9j9GJsA/5XPhPmonqz+mgGk/m4SJP8ZNGkDjGyhAkhQBQEXzRkCR4E1AmxaWQAGy20A95/tACv70QKfQBkHpmSRAME7kQF2O40CI2cNAwQ1FQPciMT8zO4y/UCUqwFYOEcAM+7++CFNrPqos9r8jaO6/jCzyvyZI1r8trZA9t2kCP3xZ1T/J5SJAx/YSQHwoTkDN4WVAyeKFQPxO20AswKNAGPTVQK7mrUBEfJJArW0LQJ0XBkCRKRW/VJQdwAQ2UcAhsQnAMEqAwLjhjsARcmjAyR07wKLfs78Fz5K/XIMnv//ctz9KRmI/NRAhQHctJEDo4yfAydVeQEdTa0COa90/m4jmP3GH9T4ZSDk/QUJlwGfzL8D6Wo/A3eGVwFNzmMClsJ/AiyuYwOewmsAML5DA200bwNVcpb93cq6/Soa6v6FjIb4b+Uu/qI/hvxD9/r98BOK/C8MtwAhfKMBlYInA/NaAwLRRfsBu/4fAp/cewNoB2L9UKRi/uL4RP8ePeT8x2lFAS4/ePw3wTz/k+qO/zFJGPp2XMcB9j0zAzQdLwNJfU8CNjW3ACsZEwHBxvL8Wlr6/JzbXv2PTjb8z7gjAM9Q/wNazIsAMQlnA0aYcwO1FH8BrZjHAWnxFwM/UUcB2SkzAQtIywBFPJsBitlbAKokqwKrIMsBKkyfAj5a3v+BrOMBtcibAigVjwC3aYcA+60XAfBmRwLGfo8DHBqTAUSWYwHQ/n8BPDZnAsAKAwKQcK8CI27q/JbbpvtEUGj8/Ys0/FX4oQNjSF0CWpPk/qqQsPzN7Hj9wCKG//4Hrv2jXWMDooUfAv1qFwLg6CMDT/C/Ago0bwF07SsAhjFvAoGpRwOdegb9lCkO/ihm5PgPLC0BETvg/CGsjQJ50DkCUBS5AR2A0QEbcD0Bf9BlAWOfGP4vvxj26EpS/FjFRwIbw4r92Pve/MpHsvtyldr77HkO/8f7mv68dDMAxbWi/8QzLPV5JLD8RDiVAKjsWQExhYkCTe3pAbcuuQDUBxkAEU7ZArq6MQDtkVEBGHzdAA2ILQPcJl71n5yHA7Dv/vypBMsCR5FvA1sJdvskxtT5L3A1AxYvsP4J9vj/tJBg/H0P6vhyPE78NTB8/dtkHQA75D0CqcydA+s25QOFp8EC6RQVBJfcIQQRaCUGWE+1As+DaQJGpoUBXYyNAGOblP8uFGD+ebOi/y7AkwFeoVMC3NhXAmOG+v0uaG0ArHoVAS/CiQJ7CeUAeODVATgUXQIIiJUDt9fA/s+cKQNIETkCq2h9Af+SwQCL67EBophFBKJIbQSe4IkFHBBtBXFgZQTTSHEEzH+ZAHo61QDRSSUBo4B9ASNvNvrxKDMA+pSjAijsmwLVrIL9mViw/PuTKQII990BNYfdA/EroQMv3vEBq16dA1xKUQEqEZ0DXg01A3deEQNn9s0C+g+dAg9MbQaggLkH5AzpBTO9FQScpREFWpC9BW5YjQWNFFEHO2eRA0SmkQNTQPkAcciM/NKSAvxWENsDY6jLAdGRFwO+CAsD0njG+xKhYQMWWlMANazhAwCcYQfpAPEEUPwPA84EpwI7tRD+xaIHAIZLzvzyhZ8DofFQ8jJ0NQSlUFMCPVok/K+NKwGLid8Axux1A+NVYwB/ISMDDaf6/kRMhwGGfEsD9dKvA4AS0P626h8Cb7QLAqQjBQMagHkFpu2hB3geiwCxbUUF7YjLA74TTQFSznsB/nRFBkylUQUTMokDuuUtBd+dMwGow08CtlaLAjT8kwCJ3WcBpa1dBPuwNQNQ9ij+E4WO/AUIgwHWvdcBy8VhAb1MFwOPjJ0FRAR1AEno9QFrpMkBKVkpAuxRRQRhfeECPfT3A/t9dwN6HLsCaRmdBEI5OwPsMCT7owHBBwNhmwFIgkEBt3crAgt1kwGAZkMAvgt5A6cM+QWsv5EB0rgdB10oPQXhRS0EpxGTALGSUQGuJdsCiS6bA05vuP4VGZkBFHkNB6RdTwOLvZkB7I0hAqpM7wE1GOMCD+XnAR5BKQWnWKUFb5arAJH0qQaAwXD9HA4/AzgYzQdr4psBLfonAUXRxQQB7OsAHhlFB+L7OwHqKVMDW+dVAD5EbwIcJSkBSWAxBNLQWP5Q/MEGwCKTA0RJLwKtvVcAMeVhBMOUIwGlcV0AL1UfAuWkVQcq5ob+wxDO/p9dbwAiWH0DFzDpBDZjJvzdpmUCSTWXAvl6VQKvyaEGBYp6/rQy4wNTvnb+WjrrAE1OvP5oGhMCB8llB9xpxQesLPEExZR5Bke4ewIT8D8CD61vAhkOUwHifREBcmXVAPz/ov0OvQkG/PMFAPLySwHM1pEClv1VBgyzcvxs5lcBFe6rAZ+nhvys3ScBvkIHAJjICQWIzUkGLozlBLr0rwFE3DsCI1tPAGgvQv5a7GkEBgxC/peqEwLrylcBS9U9ArkuRwHKUVsCPbi9AmysPwPIHgUCGAwtBN4Z2QWRoQUElAYTApKAuvz9Ok0CIOEFBLxCXwPXgpcCmZXfAavHzP0D0qcAOTti+cpYYQeP/MECFPGnA2n6AQMB12r+HshjAASSSwCY0Qj8Bqk5AjsMnwPdeVEC4wCFA5lIVPyODHUEJdaa+4hipwBzZTEAiQj/AZpZFQOy9x7+uh4nA+P5rwGq3h0CifWBAw80PQS4XPUCiKpjAhks5QRGWPkBBXlJBAcaIwEpNcsBv1i3AJwjBwMe4lsBBIdc/hbF/wEJ3i0AdvU5Ay9AzQTWyJUACxI9ArkU9wIm0XsCxZxNApHfSwCd9c0FCJto/utEuwCWil8CXLhdB5lLoQDAmeMDSjNLAxtW9PzfBGsBVjovAW5g5QUxLKEHKKGDAcicZP35FV0CwAzFApkpRQS41kzv64W/AdTUeQMy040DpaihBP2EcQdjQAkC3pypB3eNpwAY7t0BSvLnAB0M6wE60LUAuLKNAXM4HPoNbV0Ch4EVBw16UwP7Mz8DGF4hA0vXfPkuGlkB9rkHA715RQNn2b8Dj7AdBfsE4QV++X8CoTLPAsblnwLGfyT5L98++ygOHwLZSTUGhnVg/g2b4v4blPcADcBhB2TpSQZjkUj9EBoPAp4WrvTH+jEDPt2fAScoYQef4HECaxlhAgLQYwL+gjUCLg7S/1gtWQNJlDUHFKcC+Up2XQGZ23z/W+6NAmVk6wDRXHsDzYfG+hydmQEXuBECKGRdAoKLbv4ielMDe8TPA54pkQNsxmcAR0DFAsopQQZjxH8DRiY9ALg98wHQNpL6eEgJB4X+TQLc0DkEJJhNB8MgiQWJJdr+14ylBCHygwPEOJEFUPTXABxSDwPwtgsA2O09BiRZGwNqpSkBvCLS/GaFCwCK7t783gMXAMh/IwFDirEA44knAJvkBQVRCDkGOOiBACHodwE6kK8Bmb13AVMIzwDBgdMCutkzAaNe+wKQVlMCIFjTA2etEQJts60BQtNQ/elf4QPjKR0H8aF1AS+rRQPNiMEFxOkRByZuwQJJ3jcDV8VtAWigCwJQ2ZkCkfnNBPwXkvwfQM8BD8ZA/AGGAQDE8OMAecsHAcG4+wNgfesAQbQW+xlsFQRGBF0GuGlLA1ThOQOhFDkHOylPALHgzQbzbHEEUypnAkd5dQLeOYUCuXVZAOENQwCzXOL64UH4/TSp6wAP2bcD3nDDAWHJiQRFpxsBtsU5ByHeAwPfBVkBJso3A1N/NQOg1EkEq7RJBvD4BQX5iHkF9g5HAevEiQK4fH8DzZoFA1n0eP/Qh47/+IYpA4qk6QHY5K0GI1RlA7y2jwFWD70BUHIDAU2/dQKBHocCHkh5BhnFzwLKGaMBB7srAIWBoQWqEVb2Qi5W/3rpcwNnEREAvvR1BJQGdwPafOUGC1l5AlZ0bwPKiCsB5KFVBx+JuwAEFYsAXM2zAIaVyQYw8XEAsF5XAQKidP/k6v0Azs5pAOgHkv0ECrcB1go/AkMrfQEe9SsCqu649irOevkSxOEHjERBBnjIqQI6ts8BO7TlAUnpowMNd7UCs+FnAvpuAwPSYpUBciKHA1ANjQKKDvEA3xLk/6qE0QCbJckFvb5XAcI2JQL37WsAYr67AsPlMwJyqMMDKs1DA4Urov3fWPMAblAvAoINZQOn+EkEFnx1Bjy5/QI9oGEAl5mTA0Y/eQJctd8B/orW/PscdwN8pRkGgKRNBJRphQVQPocBDJW/A491PwLLlHUEWdT1B/OuiQCGpukBzsC1B5h+HwFfxI0F14XA/OsmQQEKnakD8Km3ATosewIXgbcBLnmjAT8sZwKbwIEGJJ25ABA7Av9V008B3CiNBWAsgQZR1ssDuwzLAlGcrQeqQPsCIZp3AKKI3QWsUXEAyC3PA4CzPwPIlUcA4P0BB2hXJwH6NQ0HM+iDA9ObuQGA+C8COAajA2ivqvz+ZgUDHBxZBR7tvwMuoj8B02g9AlL46QLUyX0Dm2V1AZycKwCr5WEDaEDjASYYXv1FYLUFSjhpAQYJbQVUDQkCvZGNAUW2VQEWAFEE1CJNA8YxvQEkb9r+j1CZAfSrQwM4kZsAsCKFA9kVDQUwNRb+4Mk1BoipHP0kucz/Sa5a/uC6ewNMnnsABqllB9ZZLQJWcHkHeC1dAxQ8KwEajw8ASSdZAuvcLPgG6a0Bb1X7ABGR3P0QCQcCtuZM+8nq4wJbk0cA+PybAAc7VwDS4gcAT2eu/xlJeQFjvDkF8PSJBS9+bPZkOQcAXvhPA/bk9QSkZEkG+QHJAlI2fwCuwFD+w30hBJ3N6QIIDgcC8eB9BNVI7QDVFPECMVTDA22U7QALkTkFG6pHABxlFwIMjxsAxzENBmbglwAh0ZkEc7zJAXlI1QYb7Tb91CF9BuSIUv/LKPsBLaj1AWqMywDLBVMBHAxK/oAwuQZgXG0ENmWXAYAU/QBjdV0DTj2dByo93wEU/U0Gg8ENB8AGiv3UcWkDVf6fAqBZDQTCfk8B6uohAbodFQeGhf8BNdpHA8jxAQW+zW0GxRoDAl8dVQVOirkAq/D9BS2DCwIlcU0GXcDFAkT/oQEXNVcC3qTBAb/4cQUGmTMAsj21BYC4nQbdfiMBGRlnAfa+LwAX3RsBWoRdBEXtPQXobFMDxDazAfYYRQfaZKUFaWIDAOEpcQdVKesAidyLAnVGPPwBcHkGIV0vAGnaFQOtPIkG7zEA+5wFwQYNGYMBpg+u/U+UJQNOdPEFb0xtBHesIwNVyNEG2gz3AEhxsQdIaU0GmPrpAyV4hQfs8sMCQwXZBhJwwwGioH0EZ1oFARq6XwGaXRUFGSwZBChZBvVCQVUDmZD5Aw1j3v2yrlcCl3W/AUQiQwF5lKcCliCJBYkQIwMixnkAdyErAo0yLwMfgw8AbyMlAXhSpv17G6UBt6E9AFQpIwDiJYMDjsVRBnA4sQPSNmcCfVw2+prqTQFwaEUEFIBbAo+c9QUZZjMCO+oY/cNBQQCs9LsCDnIW+ffZQQNezXT/CvBJB4RcyQMGjM8CxtVxABQtcQPc2Q0Cvvc++zdBOwDY6fMAcc5xAsURcQK12OsDKoytBwSs2wM4nVcA2V1pBkuDAwBM+T0GPbYU/L2lLwG8diz8qQ5xAm/EuQTLcR0FdzDFACNy9wJ84i0DEAh9BBqNUQfz3IEFs8x1BA6tLQLn9F0Fo4zXAayFWQZpgFkF+SwDAcEQXQWlXD8BrIAHAnUw6QMF/UkHok6O/lqeOwD/2NUCvRzxAvl8fwBvy6L+sxkBArJVVQePzLEFzUnlADmc1QLe7VkD1dpfAR24hwEQNOUG7U0lBV4JsQAvHN0GS8lhAAX8rPvaGOkAwWtVAxS0IwEIyar86paZAjahFwGToWEFrhy3AF10BQK+hT0Cue3VB86COQMEAPEGe7PS/5Js6QAl+Ar/KSclAe/1ZQXhgZEDzSbfAQc92wKtpP0ETonRBVenFvrGz1sD6W2NBiW5yQHf+AUGWt29B5hKmPKmRn8D9BmjAgeDhQBo0FEGjPB1BYKZqQfcbTUE1nDXAmrgdQdCQa0Dg/BdA7OvHwKCXUEAbpTnAmF6EwGbCMUCTeV9B6/INQeMyqMDzqk1Am4dhQJSfTUHXxDtBbHQ3wOPvGEHv9yrAhumOQPAqnz6cEvc+dPt7v75gRkCZIVBBKhodQQ3riD4I25JARDG9wCLvRsDhBVFAVNoiQYCJPUHf4qDAUGRdwJf0g7+griFBaGmoQGdUikAZbjvAnMAXQRr8V8DumInALu04QMNVdkBYIIbArBh0wIgDycCIoLtAONAYQbgoNcC/iJLAnNmqQJhipcBjiBLAkGPLwKO29r90Es2/KxqCwIc+gUDZARjAs89DQe+2CUGL/HxAZz+BwAsJJcDQLKXAXnUdQQht4EAyk0VATJsWQSMHHMBkJgpB+JKOwJ3TOUBB7ArAUrJrQB+qhMBxtINAOAgoQBkGQkB506tAAs6kQJyEGEHEzJa/dUIUwCVGJcDL6NtAdmzUwPOJxUAjSZq+ddywwPObMUHHsBjAN+WEwONAzsAEEXbAFeOEwEQQIEDCLOW/DqFewDaxXsA12FTATqFUwLVDCMCaIwBBkSY2wJsMUUH2AgnA6cu/QC/E3j8nCx9ApSARwBCLxMCzno3AEyxhQHZOU8Aw6PG/hUMwwFlUgsAkPTNBcOcYQRz/c0Ha1lBAks2AwHKMX8C/Z1RAN59SwHg7UECedmJAwdqfP3HXPEEf3RjAT7uCwI/RdUGISTPAEcTIwN0kE0C+VYdAJJtzwCKpBMCv1OO/9GFLQPkdQkEzbjDAQKwmwCa+M8Dh4LlA75GWwB67PUFfSIXAOrxXQIqXiMBg36vAS7TeQHKvcUHuSYHAqBxfQZWcuEBwQq3AgWAdwC04VsDNKIzAxEVjwBTWcMBCWh2/iQ0jQVDYS0FUC9ZA63MbQdRO4b9FUPxA0f3KwC6NmEAQBRPANCsuQAeOJkFvCC3AsimWv5tPS0AfxjNBjaT9P9ulo8DenJDANQV9vwpUaEDvnt1Ao6GVwECCM0BB8AHAJRI5QYlsYEEN2mzAaTZGQZWWaEAyUKm+pnofwD7yn8Ad32nAcOEkQTCzUEAwkHlAgm54wFx/GkH7bLRAz2QTwK+az79SpSE8wCUAQF1FP0FqvsjApTGwwJ8v7r8GmTdBBLVGwDRDGUGvwV4/S8Q5QBL7R0GewSZBUlwBwNVYL0EOcUNAG3kDQbEeWMAs6VHABTuxQM2xXr7wiitB7tTUwEtBOEERbVhB1wOLQOjBPEA1zm5Bw8VWQQDVYz814BnAO7i4wE+QkUAXkUpBvyhXQR7QesA+XztBYEd6wLTsjsBJTyJBmzozQDewaTxkT5DA/RsbQKy7QEAMlY9AJNiBwFNscUCWh5FAGgtdwNN/F8AALkQ/ifC4wBIDoMAJ4FBAbt5VQaWWj8Di0iNBMQqPwHi4D796M5DAgrowP4hyur9q+SvAzhe6wH+SLMAMF1hBGyowQWQhoEBgOg++w76OwDwgdcBzfm9BIzI2QMYfuL9rGBO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     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_data.plt.batches_umap(res_key='umap')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ba2424-228e-4d2a-8e9a-d346280a2550",
   "metadata": {},
   "source": [
    "We can see, the distribution of these two batches of data on UMAP space is so different, they are separated from each other obviously."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3859e3-60f4-4ac9-9788-b19f59f61bf4",
   "metadata": {},
   "source": [
    "## Spatial Alignment\n",
    "\n",
    "This function will ouput a corrected full expression matrix and a reduced dimensional matrix.\n",
    "\n",
    "By default, the corrected full expression matrix is saved in `data.layers`, if the parameter `inplace` is set to True, the original expression matrix will be replaced.\n",
    "\n",
    "The reduced dimensional matrix is stored by key **'aligned_reduction'**, you can specify the dimensions to reduction by parameter `latent_dims`.\n",
    "\n",
    "More details about other parameters in [API](../content/stereo.algorithm.spatial_alignment.SpatialAlignment.main.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804a960e",
   "metadata": {},
   "source": [
    "**Spatial Alignment** can be run with the data only containing highly variable genes, this usually can lead to better performance, in this case, the data will be filtered to only contain highly variable genes after running **Spatial Alignment**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dacad033",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:38.947341Z",
     "iopub.status.busy": "2025-02-20T09:32:38.947124Z",
     "iopub.status.idle": "2025-02-20T09:32:38.951137Z",
     "shell.execute_reply": "2025-02-20T09:32:38.950207Z",
     "shell.execute_reply.started": "2025-02-20T09:32:38.947313Z"
    }
   },
   "outputs": [],
   "source": [
    "# ms_data.tl.highly_variable_genes(\n",
    "#     min_mean=0.0125,\n",
    "#     max_mean=3,\n",
    "#     min_disp=0.5,\n",
    "#     n_top_genes=2000,\n",
    "#     res_key='highly_variable_genes'\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2f4a9e",
   "metadata": {},
   "source": [
    "Here we run **Spatial Alignment** with entire data, if you want to only use highly variable genes, you need to uncomment the codes in the notebook cell above to run `ms_data.tl.highly_variable_genes` beforehand and set the parameter `use_hvg` to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e2b69b3-227e-43d5-aa2c-6d138224706a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:32:38.951917Z",
     "iopub.status.busy": "2025-02-20T09:32:38.951736Z",
     "iopub.status.idle": "2025-02-20T09:45:05.909546Z",
     "shell.execute_reply": "2025-02-20T09:45:05.908073Z",
     "shell.execute_reply.started": "2025-02-20T09:32:38.951901Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:32:40][Stereo][3375092][MainThread][140719273752384][ms_pipeline][115][INFO]: register algorithm spatial_alignment to <class 'stereo.core.stereo_exp_data.AnnBasedStereoExpData'>-140717210444080\n",
      "[2025-02-20 17:32:40][Stereo][3375092][MainThread][140719273752384][dataset][144][INFO]: The spatial coordinates specified by spatial are used to calculate nearest neighbor graphs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-02-20 17:32:40 Method: 'init_bank' Running...\n",
      "  Running time: 0.0 min 1.4996 s\n",
      "2025-02-20 17:32:42 Method: 'train' Running...\n",
      "  2025-02-20 17:45:02 Epoch: 139 Loss: 7.0915 Loss min: 7.0402 EarlyStopping counter: 15 out of 15\n",
      "  2025-02-20 17:45:02 Model Training Finished!\n",
      "  Running time: 12.0 min 20.3509 s\n",
      "2025-02-20 17:45:02 Method: 'alignment' Running...\n",
      "2025-02-20 17:45:05 Batch Alignment Finished!\n",
      "  Running time: 0.0 min 3.1240 s\n"
     ]
    }
   ],
   "source": [
    "ms_data.tl.spatial_alignment(\n",
    "    use_hvg=False,\n",
    "    n_neighors=15,\n",
    "    is_undirected=True,\n",
    "    latent_dims=100,\n",
    "    tau1=0.05,\n",
    "    tau2=0.01,\n",
    "    tau3=0.1,\n",
    "    inplace=False,\n",
    "    is_verbose=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6eb8ab1f-9a82-4ece-8b7a-3c4058460abe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:45:05.912299Z",
     "iopub.status.busy": "2025-02-20T09:45:05.911523Z",
     "iopub.status.idle": "2025-02-20T09:45:05.951866Z",
     "shell.execute_reply": "2025-02-20T09:45:05.950342Z",
     "shell.execute_reply.started": "2025-02-20T09:45:05.912253Z"
    }
   },
   "outputs": [
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       "      <td>-0.136572</td>\n",
       "      <td>-0.264383</td>\n",
       "      <td>0.152184</td>\n",
       "      <td>-0.228118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198014</td>\n",
       "      <td>-0.131973</td>\n",
       "      <td>0.067535</td>\n",
       "      <td>-0.281841</td>\n",
       "      <td>0.290346</td>\n",
       "      <td>-0.156325</td>\n",
       "      <td>0.006843</td>\n",
       "      <td>0.300321</td>\n",
       "      <td>-0.121258</td>\n",
       "      <td>-0.078710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>0.175933</td>\n",
       "      <td>0.154506</td>\n",
       "      <td>-0.121935</td>\n",
       "      <td>-0.212189</td>\n",
       "      <td>-0.285967</td>\n",
       "      <td>0.216793</td>\n",
       "      <td>-0.014016</td>\n",
       "      <td>-0.147251</td>\n",
       "      <td>-0.018139</td>\n",
       "      <td>0.019123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038161</td>\n",
       "      <td>0.045721</td>\n",
       "      <td>0.067250</td>\n",
       "      <td>-0.466156</td>\n",
       "      <td>0.359383</td>\n",
       "      <td>0.022651</td>\n",
       "      <td>0.134133</td>\n",
       "      <td>0.186027</td>\n",
       "      <td>-0.064617</td>\n",
       "      <td>-0.087383</td>\n",
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       "  </tbody>\n",
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       "<p>2307 rows × 100 columns</p>\n",
       "</div>"
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      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0     0.009977 -0.091885 -0.106940  0.145098  0.089974 -0.008399  0.162432   \n",
       "1    -0.076175 -0.239181 -0.171321  0.228486  0.024512  0.017261  0.080949   \n",
       "2    -0.036215 -0.233678 -0.163343  0.184396  0.114535  0.101297  0.147587   \n",
       "3    -0.062999 -0.305679 -0.179629  0.098737  0.140392  0.254699  0.062434   \n",
       "4     0.081919 -0.314154 -0.131669  0.022888  0.037666  0.142007 -0.056440   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2302  0.356144  0.078517  0.435474 -0.211080 -0.091554 -0.124335 -0.088007   \n",
       "2303 -0.006577 -0.225398  0.104167  0.171594 -0.265057  0.188282 -0.131274   \n",
       "2304  0.235737  0.388830 -0.197873 -0.283984 -0.063494  0.065340 -0.130950   \n",
       "2305  0.030273  0.065268 -0.096198 -0.190912 -0.169639  0.747086 -0.136572   \n",
       "2306  0.175933  0.154506 -0.121935 -0.212189 -0.285967  0.216793 -0.014016   \n",
       "\n",
       "            7         8         9   ...        90        91        92  \\\n",
       "0    -0.314220  0.239323 -0.151989  ... -0.256612  0.202301 -0.169625   \n",
       "1    -0.253091  0.123381  0.017464  ... -0.285186  0.214535 -0.196208   \n",
       "2    -0.299150  0.153697 -0.072609  ... -0.296751  0.125169 -0.182926   \n",
       "3    -0.322186  0.101131 -0.168877  ... -0.189232 -0.007272 -0.269327   \n",
       "4    -0.323457  0.072555 -0.095923  ... -0.221495  0.058406 -0.209400   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2302  0.018068 -0.367442  0.370240  ... -0.355697 -0.023487  0.274631   \n",
       "2303  0.209731 -0.066610  0.026982  ... -0.115878  0.106854  0.299534   \n",
       "2304 -0.278878  0.273952 -0.049268  ...  0.479300 -0.318692  0.026394   \n",
       "2305 -0.264383  0.152184 -0.228118  ...  0.198014 -0.131973  0.067535   \n",
       "2306 -0.147251 -0.018139  0.019123  ...  0.038161  0.045721  0.067250   \n",
       "\n",
       "            93        94        95        96        97        98        99  \n",
       "0     0.070727  0.058331 -0.072663 -0.213056  0.276813 -0.194957 -0.036370  \n",
       "1     0.082946  0.063450  0.009587 -0.216828  0.179361 -0.280448 -0.104205  \n",
       "2     0.224416  0.107030 -0.019341 -0.276176  0.260968 -0.242852 -0.041470  \n",
       "3     0.233675  0.138923  0.048628 -0.311299  0.243654 -0.276228  0.054752  \n",
       "4     0.087003  0.099748  0.104273 -0.297579  0.188548 -0.273973  0.052728  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2302  0.198752 -0.272967 -0.418953 -0.037732 -0.202396  0.363269 -0.308682  \n",
       "2303  0.020559 -0.024644 -0.157923  0.020815 -0.281706 -0.018450  0.064131  \n",
       "2304 -0.242704  0.492687  0.289297 -0.069553  0.286800  0.156109  0.370372  \n",
       "2305 -0.281841  0.290346 -0.156325  0.006843  0.300321 -0.121258 -0.078710  \n",
       "2306 -0.466156  0.359383  0.022651  0.134133  0.186027 -0.064617 -0.087383  \n",
       "\n",
       "[2307 rows x 100 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_data.tl.result['aligned_reduction']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53766a58-1ef6-4daf-a256-ab12478c07d8",
   "metadata": {},
   "source": [
    "## UMAP based on aligned data\n",
    "\n",
    "Here you need to set the `pca_res_key` to **'aligned_reduction'**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e58bb77-9835-4621-b4dc-2a0f3661e592",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:45:05.953761Z",
     "iopub.status.busy": "2025-02-20T09:45:05.953400Z",
     "iopub.status.idle": "2025-02-20T09:45:09.270524Z",
     "shell.execute_reply": "2025-02-20T09:45:09.269895Z",
     "shell.execute_reply.started": "2025-02-20T09:45:05.953733Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:45:05][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run neighbors\n",
      "[2025-02-20 17:45:05][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run neighbors...\n",
      "[2025-02-20 17:45:06][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: neighbors end, consume time 0.2505s.\n",
      "[2025-02-20 17:45:06][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run umap\n",
      "[2025-02-20 17:45:06][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run umap...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tcompleted  0  /  500 epochs\n",
      "\tcompleted  50  /  500 epochs\n",
      "\tcompleted  100  /  500 epochs\n",
      "\tcompleted  150  /  500 epochs\n",
      "\tcompleted  200  /  500 epochs\n",
      "\tcompleted  250  /  500 epochs\n",
      "\tcompleted  300  /  500 epochs\n",
      "\tcompleted  350  /  500 epochs\n",
      "\tcompleted  400  /  500 epochs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: umap end, consume time 3.0552s.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tcompleted  450  /  500 epochs\n"
     ]
    }
   ],
   "source": [
    "ms_data.tl.neighbors(pca_res_key='aligned_reduction', res_key='neighbors_aligned', n_jobs=-1)\n",
    "ms_data.tl.umap(pca_res_key='aligned_reduction', neighbors_res_key='neighbors_aligned', res_key='umap_aligned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c17b1a8-1295-401a-91f6-eef85301295a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:45:09.271382Z",
     "iopub.status.busy": "2025-02-20T09:45:09.271228Z",
     "iopub.status.idle": "2025-02-20T09:45:09.534852Z",
     "shell.execute_reply": "2025-02-20T09:45:09.533521Z",
     "shell.execute_reply.started": "2025-02-20T09:45:09.271368Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run batches_umap\n"
     ]
    },
    {
     "data": {},
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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[2307]},\"y\":{\"__ndarray__\":\"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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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      ],
      "text/plain": [
       "Column\n",
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       "    [1] Row\n",
       "        [0] Column\n",
       "            [0] Bokeh(Figure)\n",
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       "            [0] Row\n",
       "                [0] Bokeh(Figure)\n",
       "                [1] Bokeh(Figure)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1522"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_data.plt.batches_umap(res_key='umap_aligned')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9875db6f-af5f-4173-9dc3-cbab3eee77da",
   "metadata": {},
   "source": [
    "The distribution on UMAP space which is created based on aligned data is almost overlap."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650b06ed-cb46-465a-878c-8e98a163b8d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T08:39:31.960887Z",
     "iopub.status.busy": "2023-11-17T08:39:31.960237Z",
     "iopub.status.idle": "2023-11-17T08:39:31.967495Z",
     "shell.execute_reply": "2023-11-17T08:39:31.965893Z",
     "shell.execute_reply.started": "2023-11-17T08:39:31.960832Z"
    }
   },
   "source": [
    "## Cluster based on aligned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eac16622-53ab-45e0-a7a8-ab1608839739",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:45:09.535969Z",
     "iopub.status.busy": "2025-02-20T09:45:09.535795Z",
     "iopub.status.idle": "2025-02-20T09:45:09.958263Z",
     "shell.execute_reply": "2025-02-20T09:45:09.957309Z",
     "shell.execute_reply.started": "2025-02-20T09:45:09.535953Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run leiden\n",
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][st_pipeline][41][INFO]: start to run leiden...\n",
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][st_pipeline][44][INFO]: leiden end, consume time 0.4079s.\n"
     ]
    }
   ],
   "source": [
    "ms_data.tl.leiden(neighbors_res_key='neighbors_aligned', res_key='leiden')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1492b07-25a2-4ede-9281-bb8b9db4a7f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-20T09:45:09.959461Z",
     "iopub.status.busy": "2025-02-20T09:45:09.959221Z",
     "iopub.status.idle": "2025-02-20T09:45:10.432312Z",
     "shell.execute_reply": "2025-02-20T09:45:10.431539Z",
     "shell.execute_reply.started": "2025-02-20T09:45:09.959443Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-02-20 17:45:09][Stereo][3375092][MainThread][140719273752384][ms_pipeline][125][INFO]: data_obj(idx=0) in ms_data start to run cluster_scatter\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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       "    var docs_json = {\"1d8b4335-1eb1-4804-be2e-8fe1690d56aa\":{\"defs\":[{\"extends\":null,\"module\":null,\"name\":\"ReactiveHTML1\",\"overrides\":[],\"properties\":[]},{\"extends\":null,\"module\":null,\"name\":\"FlexBox1\",\"overrides\":[],\"properties\":[{\"default\":\"flex-start\",\"kind\":null,\"name\":\"align_content\"},{\"default\":\"flex-start\",\"kind\":null,\"name\":\"align_items\"},{\"default\":\"row\",\"kind\":null,\"name\":\"flex_direction\"},{\"default\":\"wrap\",\"kind\":null,\"name\":\"flex_wrap\"},{\"default\":\"flex-start\",\"kind\":null,\"name\":\"justify_content\"}]},{\"extends\":null,\"module\":null,\"name\":\"GridStack1\",\"overrides\":[],\"properties\":[{\"default\":\"warn\",\"kind\":null,\"name\":\"mode\"},{\"default\":null,\"kind\":null,\"name\":\"ncols\"},{\"default\":null,\"kind\":null,\"name\":\"nrows\"},{\"default\":true,\"kind\":null,\"name\":\"allow_resize\"},{\"default\":true,\"kind\":null,\"name\":\"allow_drag\"},{\"default\":[],\"kind\":null,\"name\":\"state\"}]},{\"extends\":null,\"module\":null,\"name\":\"click1\",\"overrides\":[],\"properties\":[{\"default\":\"\",\"kind\":null,\"name\":\"terminal_output\"},{\"default\":\"\",\"kind\":null,\"name\":\"debug_name\"},{\"default\":0,\"kind\":null,\"name\":\"clears\"}]},{\"extends\":null,\"module\":null,\"name\":\"NotificationAreaBase1\",\"overrides\":[],\"properties\":[{\"default\":\"bottom-right\",\"kind\":null,\"name\":\"position\"},{\"default\":0,\"kind\":null,\"name\":\"_clear\"}]},{\"extends\":null,\"module\":null,\"name\":\"NotificationArea1\",\"overrides\":[],\"properties\":[{\"default\":[],\"kind\":null,\"name\":\"notifications\"},{\"default\":\"bottom-right\",\"kind\":null,\"name\":\"position\"},{\"default\":0,\"kind\":null,\"name\":\"_clear\"},{\"default\":[{\"background\":\"#ffc107\",\"icon\":{\"className\":\"fas fa-exclamation-triangle\",\"color\":\"white\",\"tagName\":\"i\"},\"type\":\"warning\"},{\"background\":\"#007bff\",\"icon\":{\"className\":\"fas fa-info-circle\",\"color\":\"white\",\"tagName\":\"i\"},\"type\":\"info\"}],\"kind\":null,\"name\":\"types\"}]},{\"extends\":null,\"module\":null,\"name\":\"Notification\",\"overrides\":[],\"properties\":[{\"default\":null,\"kind\":null,\"name\":\"background\"},{\"default\":3000,\"kind\":null,\"name\":\"duration\"},{\"default\":null,\"kind\":null,\"name\":\"icon\"},{\"default\":\"\",\"kind\":null,\"name\":\"message\"},{\"default\":null,\"kind\":null,\"name\":\"notification_type\"},{\"default\":false,\"kind\":null,\"name\":\"_destroyed\"}]},{\"extends\":null,\"module\":null,\"name\":\"TemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]},{\"extends\":null,\"module\":null,\"name\":\"MaterialTemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]}],\"roots\":{\"references\":[{\"attributes\":{\"margin\":[5,10,5,10],\"name\":\"\",\"width\":800},\"id\":\"1807\",\"type\":\"Div\"},{\"attributes\":{\"margin\":[5,10,5,10],\"max_length\":5000,\"min_width\":200,\"placeholder\":\"Enter a file name...\",\"title\":\"file name\",\"width\":200},\"id\":\"1802\",\"type\":\"TextInput\"},{\"attributes\":{\"children\":[{\"id\":\"1800\"},{\"id\":\"1801\"},{\"id\":\"1805\"}],\"margin\":[0,0,0,0],\"name\":\"Column01552\"},\"id\":\"1799\",\"type\":\"Column\"},{\"attributes\":{\"low\":0,\"margin\":[5,10,5,10],\"min_width\":200,\"mode\":\"int\",\"placeholder\":\"Enter the dip...\",\"title\":\"dpi\",\"value\":100,\"value_throttled\":100,\"width\":200},\"id\":\"1804\",\"type\":\"Spinner\"},{\"attributes\":{\"css_classes\":[\"markdown\"],\"margin\":[5,5,5,5],\"name\":\"Markdown01550\",\"text\":\"&lt;p&gt;&lt;font size=&quot;3&quot;&gt;&lt;br&gt;Exporting the plot.&lt;/br&gt;&lt;/font&gt;&lt;/p&gt;\"},\"id\":\"1800\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"children\":[{\"id\":\"1802\"},{\"id\":\"1803\"},{\"id\":\"1804\"}],\"margin\":[0,0,0,0],\"name\":\"Row01548\"},\"id\":\"1801\",\"type\":\"Row\"},{\"attributes\":{\"button_type\":\"primary\",\"icon\":null,\"label\":\"export\",\"margin\":[5,10,5,10],\"min_width\":100,\"subscribed_events\":[\"button_click\"],\"width\":100},\"id\":\"1806\",\"type\":\"Button\"},{\"attributes\":{\"client_comm_id\":\"643be89c7e9b4931b2d73afdd45ede38\",\"comm_id\":\"646fc86b53804b71888e5ce0461c140f\",\"plot_id\":\"1799\"},\"id\":\"1808\",\"type\":\"panel.models.comm_manager.CommManager\"},{\"attributes\":{\"children\":[{\"id\":\"1806\"},{\"id\":\"1807\"}],\"margin\":[0,0,0,0],\"name\":\"Row01549\"},\"id\":\"1805\",\"type\":\"Row\"},{\"attributes\":{\"margin\":[5,10,5,10],\"min_width\":60,\"options\":[\"png\",\"pdf\"],\"title\":\"file format\",\"value\":\"png\",\"width\":60},\"id\":\"1803\",\"type\":\"panel.models.widgets.CustomSelect\"}],\"root_ids\":[\"1799\",\"1808\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
       "    var render_items = [{\"docid\":\"1d8b4335-1eb1-4804-be2e-8fe1690d56aa\",\"root_ids\":[\"1799\"],\"roots\":{\"1799\":\"df354357-c33c-47a6-ac1d-4bc0e22e59dd\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Markdown(str)\n",
       "    [1] Row\n",
       "        [0] TextInput(name='file name', placeholder='Enter a file name...', width=200)\n",
       "        [1] Select(name='file format', options=['png', 'pdf'], value='png', width=60)\n",
       "        [2] IntInput(name='dpi', placeholder='Enter the dip...', start=0, value=100, width=200)\n",
       "    [2] Row\n",
       "        [0] Button(button_type='primary', name='export', width=100)\n",
       "        [1] StaticText(width=800)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1799"
      }
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x273.089 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_data.plt.cluster_scatter(\n",
    "    res_key='leiden',\n",
    "    show_plotting_scale=True,\n",
    "    reorganize_coordinate=3,\n",
    "    horizontal_offset_additional=1000\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "stereopy",
   "language": "python",
   "name": "stereopy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}