{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5efbcf3d",
   "metadata": {},
   "source": [
    "# Tutorial:RNA+ATAC (well-labeled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9ea2a4",
   "metadata": {},
   "source": [
    "In this tutorial, we will show how to cluster RNA+ATAC(well-labeled) data using scMAGCA. As an example, we use a human peripheral blood mononuclear sample dataset 'human_brain_10x' containing 2592 cells with two omics. Among them, ATAC has 130862 features and RNA contains 36601 features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a950b589",
   "metadata": {},
   "source": [
    "## Loading package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f7654c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import scanpy as sc\n",
    "import random\n",
    "import warnings\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy.io import mmread\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b7439565",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scMAGCA.preprocess import read_dataset, preprocess_dataset\n",
    "from scMAGCA.utils import *\n",
    "from scMAGCA.scMAGCA import scMultiCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "23591661",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set seed\n",
    "random.seed(3407)\n",
    "np.random.seed(3407)\n",
    "torch.manual_seed(3407)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.enabled = False\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9189f5",
   "metadata": {},
   "source": [
    "## Reading  dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc2c12d",
   "metadata": {},
   "source": [
    "The required input files include: \n",
    "1) x1: Chromatin accessibility matrix (data format is mtx file) : human_brain_10x_ATAC_matrix.mtx;\\\n",
    "2) x2: Gene expression matrix (data format is mtx file) : human_brain_10x_RNA_matrix.mtx.\\\n",
    "3) Real label (stored as csv file) : human_brain_10x_label.csv.\n",
    "\n",
    "To ensure reproducibility of the results, please read the above data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6393bc59",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = csr_matrix(mmread('../datasets/human_brain_10x/human_brain_10x_ATAC_matrix.mtx').T).toarray()\n",
    "x2 = csr_matrix(mmread('../datasets/human_brain_10x/human_brain_10x_RNA_matrix.mtx').T).toarray()\n",
    "y = np.array(pd.read_csv('../datasets/human_brain_10x/human_brain_10x_label.csv', index_col=0)['cell_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b6cdd5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 2, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]),\n",
       " array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]),\n",
       " array([0, 0, 5, ..., 3, 0, 6]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bed2d05",
   "metadata": {},
   "source": [
    "We select the two omics data for high expression, and the number of chosen features are both set to 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5090ee68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen offset: 1.21\n",
      "Chosen offset: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importantGenes = geneSelection(x1, n=2000)\n",
    "x1 = x1[:, importantGenes]\n",
    "importantGenes = geneSelection(x2, n=2000)\n",
    "x2 = x2[:, importantGenes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4135d2f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 2000 features and 2855 cells.\n"
     ]
    }
   ],
   "source": [
    "adata1 = sc.AnnData(x1)\n",
    "adata1 = read_dataset(adata1, copy=True)\n",
    "adata1 = preprocess_dataset(adata1, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8426b298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2855 × 2000\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c77a2cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 2000 features and 2855 cells.\n"
     ]
    }
   ],
   "source": [
    "adata2 = sc.AnnData(x2)\n",
    "adata2 = read_dataset(adata2, copy=True)\n",
    "adata2 = preprocess_dataset(adata2, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2b3307e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2855 × 2000\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084dd681",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "335669ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = scMultiCluster(input_dim1=adata1.n_vars,input_dim2=adata2.n_vars,\n",
    "                       alpha=0.2,beta=0.5,gama=0.01,device='cuda').to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "440448be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scMultiCluster(\n",
       "  (encoder): Encoder(\n",
       "    (stacked_gnn): ModuleList(\n",
       "      (0): GCNConv(4000, 1024)\n",
       "      (1): GCNConv(1024, 256)\n",
       "      (2): GCNConv(256, 64)\n",
       "      (3): GCNConv(64, 32)\n",
       "    )\n",
       "    (stacked_bns): ModuleList(\n",
       "      (0): BatchNorm1d(1024, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (2): BatchNorm1d(64, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (3): BatchNorm1d(32, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (stacked_prelus): ModuleList(\n",
       "      (0-3): 4 x PReLU(num_parameters=1)\n",
       "    )\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=512, bias=True)\n",
       "    (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): PReLU(num_parameters=1)\n",
       "    (3): Linear(in_features=512, out_features=1024, bias=True)\n",
       "    (4): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): PReLU(num_parameters=1)\n",
       "    (6): Linear(in_features=1024, out_features=4000, bias=True)\n",
       "  )\n",
       "  (dec_mean): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4000, bias=True)\n",
       "    (3): MeanAct()\n",
       "  )\n",
       "  (dec_disp): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4000, bias=True)\n",
       "    (3): DispAct()\n",
       "  )\n",
       "  (dec_pi): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4000, bias=True)\n",
       "    (3): Sigmoid()\n",
       "  )\n",
       "  (zinb_loss): ZINBLoss()\n",
       ")"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fccebe5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretraining stage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing full batch data\n",
      "Pretrain epoch 1, recon_loss:1.169784, zinb_loss:1.408436, adversial_loss:1.388419\n",
      "Pretrain epoch 2, recon_loss:1.070342, zinb_loss:1.344619, adversial_loss:1.398618\n",
      "Pretrain epoch 3, recon_loss:0.856114, zinb_loss:1.233490, adversial_loss:1.383326\n",
      "Pretrain epoch 4, recon_loss:0.777161, zinb_loss:1.138344, adversial_loss:1.387546\n",
      "Pretrain epoch 5, recon_loss:0.756221, zinb_loss:1.058208, adversial_loss:1.387960\n",
      "Pretrain epoch 6, recon_loss:0.727621, zinb_loss:0.997085, adversial_loss:1.388463\n",
      "Pretrain epoch 7, recon_loss:0.714406, zinb_loss:0.949877, adversial_loss:1.388127\n",
      "Pretrain epoch 8, recon_loss:0.706106, zinb_loss:0.913157, adversial_loss:1.387559\n",
      "Pretrain epoch 9, recon_loss:0.698698, zinb_loss:0.883854, adversial_loss:1.386571\n",
      "Pretrain epoch 10, recon_loss:0.691434, zinb_loss:0.861529, adversial_loss:1.386741\n",
      "Pretrain epoch 11, recon_loss:0.685417, zinb_loss:0.845830, adversial_loss:1.386302\n",
      "Pretrain epoch 12, recon_loss:0.680195, zinb_loss:0.836537, adversial_loss:1.385211\n",
      "Pretrain epoch 13, recon_loss:0.676537, zinb_loss:0.831823, adversial_loss:1.383904\n",
      "Pretrain epoch 14, recon_loss:0.673439, zinb_loss:0.827924, adversial_loss:1.382640\n",
      "Pretrain epoch 15, recon_loss:0.670256, zinb_loss:0.822974, adversial_loss:1.381075\n",
      "Pretrain epoch 16, recon_loss:0.667548, zinb_loss:0.817790, adversial_loss:1.379253\n",
      "Pretrain epoch 17, recon_loss:0.665054, zinb_loss:0.812265, adversial_loss:1.377839\n",
      "Pretrain epoch 18, recon_loss:0.663192, zinb_loss:0.805716, adversial_loss:1.375915\n",
      "Pretrain epoch 19, recon_loss:0.661385, zinb_loss:0.798364, adversial_loss:1.373804\n",
      "Pretrain epoch 20, recon_loss:0.659305, zinb_loss:0.791880, adversial_loss:1.372130\n",
      "Pretrain epoch 21, recon_loss:0.657309, zinb_loss:0.786778, adversial_loss:1.370895\n",
      "Pretrain epoch 22, recon_loss:0.655640, zinb_loss:0.782695, adversial_loss:1.369990\n",
      "Pretrain epoch 23, recon_loss:0.654056, zinb_loss:0.779225, adversial_loss:1.369219\n",
      "Pretrain epoch 24, recon_loss:0.652686, zinb_loss:0.776025, adversial_loss:1.368398\n",
      "Pretrain epoch 25, recon_loss:0.651429, zinb_loss:0.772747, adversial_loss:1.367414\n",
      "Pretrain epoch 26, recon_loss:0.650082, zinb_loss:0.769552, adversial_loss:1.366338\n",
      "Pretrain epoch 27, recon_loss:0.648808, zinb_loss:0.766707, adversial_loss:1.365443\n",
      "Pretrain epoch 28, recon_loss:0.647645, zinb_loss:0.763927, adversial_loss:1.364937\n",
      "Pretrain epoch 29, recon_loss:0.646524, zinb_loss:0.761138, adversial_loss:1.364495\n",
      "Pretrain epoch 30, recon_loss:0.645425, zinb_loss:0.758414, adversial_loss:1.363612\n",
      "Pretrain epoch 31, recon_loss:0.644396, zinb_loss:0.755813, adversial_loss:1.362422\n",
      "Pretrain epoch 32, recon_loss:0.643441, zinb_loss:0.753517, adversial_loss:1.361408\n",
      "Pretrain epoch 33, recon_loss:0.642569, zinb_loss:0.751526, adversial_loss:1.360630\n",
      "Pretrain epoch 34, recon_loss:0.641707, zinb_loss:0.749856, adversial_loss:1.360004\n",
      "Pretrain epoch 35, recon_loss:0.640825, zinb_loss:0.748321, adversial_loss:1.359344\n",
      "Pretrain epoch 36, recon_loss:0.639981, zinb_loss:0.746828, adversial_loss:1.358500\n",
      "Pretrain epoch 37, recon_loss:0.639136, zinb_loss:0.745318, adversial_loss:1.357426\n",
      "Pretrain epoch 38, recon_loss:0.638216, zinb_loss:0.743760, adversial_loss:1.355848\n",
      "Pretrain epoch 39, recon_loss:0.637380, zinb_loss:0.742236, adversial_loss:1.353912\n",
      "Pretrain epoch 40, recon_loss:0.636687, zinb_loss:0.740813, adversial_loss:1.352156\n",
      "Pretrain epoch 41, recon_loss:0.636052, zinb_loss:0.739468, adversial_loss:1.350634\n",
      "Pretrain epoch 42, recon_loss:0.635389, zinb_loss:0.738166, adversial_loss:1.349464\n",
      "Pretrain epoch 43, recon_loss:0.634717, zinb_loss:0.736892, adversial_loss:1.348795\n",
      "Pretrain epoch 44, recon_loss:0.634095, zinb_loss:0.735710, adversial_loss:1.348157\n",
      "Pretrain epoch 45, recon_loss:0.633546, zinb_loss:0.734619, adversial_loss:1.347267\n",
      "Pretrain epoch 46, recon_loss:0.632996, zinb_loss:0.733619, adversial_loss:1.346388\n",
      "Pretrain epoch 47, recon_loss:0.632496, zinb_loss:0.732654, adversial_loss:1.345413\n",
      "Pretrain epoch 48, recon_loss:0.631952, zinb_loss:0.731729, adversial_loss:1.344848\n",
      "Pretrain epoch 49, recon_loss:0.631508, zinb_loss:0.730977, adversial_loss:1.344065\n",
      "Pretrain epoch 50, recon_loss:0.631420, zinb_loss:0.730854, adversial_loss:1.343822\n",
      "Pretrain epoch 51, recon_loss:0.631906, zinb_loss:0.731071, adversial_loss:1.343062\n",
      "Pretrain epoch 52, recon_loss:0.630444, zinb_loss:0.729330, adversial_loss:1.342873\n",
      "Pretrain epoch 53, recon_loss:0.629704, zinb_loss:0.728187, adversial_loss:1.341585\n",
      "Pretrain epoch 54, recon_loss:0.630057, zinb_loss:0.728062, adversial_loss:1.341042\n",
      "Pretrain epoch 55, recon_loss:0.628796, zinb_loss:0.726502, adversial_loss:1.340571\n",
      "Pretrain epoch 56, recon_loss:0.629088, zinb_loss:0.726583, adversial_loss:1.340474\n",
      "Pretrain epoch 57, recon_loss:0.628052, zinb_loss:0.725286, adversial_loss:1.339191\n",
      "Pretrain epoch 58, recon_loss:0.628274, zinb_loss:0.725267, adversial_loss:1.338458\n",
      "Pretrain epoch 59, recon_loss:0.627336, zinb_loss:0.724268, adversial_loss:1.338002\n",
      "Pretrain epoch 60, recon_loss:0.627312, zinb_loss:0.724190, adversial_loss:1.337632\n",
      "Pretrain epoch 61, recon_loss:0.626776, zinb_loss:0.723404, adversial_loss:1.337488\n",
      "Pretrain epoch 62, recon_loss:0.627032, zinb_loss:0.723379, adversial_loss:1.336732\n",
      "Pretrain epoch 63, recon_loss:0.626438, zinb_loss:0.723189, adversial_loss:1.335974\n",
      "Pretrain epoch 64, recon_loss:0.626476, zinb_loss:0.722660, adversial_loss:1.334697\n",
      "Pretrain epoch 65, recon_loss:0.625597, zinb_loss:0.721975, adversial_loss:1.334619\n",
      "Pretrain epoch 66, recon_loss:0.624939, zinb_loss:0.721154, adversial_loss:1.334243\n",
      "Pretrain epoch 67, recon_loss:0.624900, zinb_loss:0.721179, adversial_loss:1.333704\n",
      "Pretrain epoch 68, recon_loss:0.624376, zinb_loss:0.721021, adversial_loss:1.333103\n",
      "Pretrain epoch 69, recon_loss:0.624289, zinb_loss:0.720321, adversial_loss:1.332160\n",
      "Pretrain epoch 70, recon_loss:0.623500, zinb_loss:0.719786, adversial_loss:1.331645\n",
      "Pretrain epoch 71, recon_loss:0.623196, zinb_loss:0.719570, adversial_loss:1.331787\n",
      "Pretrain epoch 72, recon_loss:0.623136, zinb_loss:0.719509, adversial_loss:1.331422\n",
      "Pretrain epoch 73, recon_loss:0.622260, zinb_loss:0.718859, adversial_loss:1.330395\n",
      "Pretrain epoch 74, recon_loss:0.621775, zinb_loss:0.718441, adversial_loss:1.329675\n",
      "Pretrain epoch 75, recon_loss:0.621847, zinb_loss:0.718343, adversial_loss:1.329239\n",
      "Pretrain epoch 76, recon_loss:0.621217, zinb_loss:0.718222, adversial_loss:1.328940\n",
      "Pretrain epoch 77, recon_loss:0.620634, zinb_loss:0.717626, adversial_loss:1.328496\n",
      "Pretrain epoch 78, recon_loss:0.620175, zinb_loss:0.717431, adversial_loss:1.327847\n",
      "Pretrain epoch 79, recon_loss:0.620133, zinb_loss:0.717494, adversial_loss:1.326998\n",
      "Pretrain epoch 80, recon_loss:0.619830, zinb_loss:0.717256, adversial_loss:1.327035\n",
      "Pretrain epoch 81, recon_loss:0.619155, zinb_loss:0.716793, adversial_loss:1.326318\n",
      "Pretrain epoch 82, recon_loss:0.618669, zinb_loss:0.716444, adversial_loss:1.324780\n",
      "Pretrain epoch 83, recon_loss:0.618459, zinb_loss:0.716006, adversial_loss:1.324427\n",
      "Pretrain epoch 84, recon_loss:0.617861, zinb_loss:0.716251, adversial_loss:1.324360\n",
      "Pretrain epoch 85, recon_loss:0.619684, zinb_loss:0.718155, adversial_loss:1.322299\n",
      "Pretrain epoch 86, recon_loss:0.622860, zinb_loss:0.721925, adversial_loss:1.325042\n",
      "Pretrain epoch 87, recon_loss:0.618629, zinb_loss:0.716601, adversial_loss:1.323538\n",
      "Pretrain epoch 88, recon_loss:0.620597, zinb_loss:0.718052, adversial_loss:1.322110\n",
      "Pretrain epoch 89, recon_loss:0.617523, zinb_loss:0.716413, adversial_loss:1.322977\n",
      "Pretrain epoch 90, recon_loss:0.618598, zinb_loss:0.717059, adversial_loss:1.323720\n",
      "Pretrain epoch 91, recon_loss:0.616655, zinb_loss:0.715781, adversial_loss:1.322824\n",
      "Pretrain epoch 92, recon_loss:0.617477, zinb_loss:0.716070, adversial_loss:1.320699\n",
      "Pretrain epoch 93, recon_loss:0.616034, zinb_loss:0.715122, adversial_loss:1.319759\n",
      "Pretrain epoch 94, recon_loss:0.616195, zinb_loss:0.715299, adversial_loss:1.320488\n",
      "Pretrain epoch 95, recon_loss:0.615437, zinb_loss:0.714518, adversial_loss:1.321364\n",
      "Pretrain epoch 96, recon_loss:0.614937, zinb_loss:0.714601, adversial_loss:1.320296\n",
      "Pretrain epoch 97, recon_loss:0.614476, zinb_loss:0.714020, adversial_loss:1.318747\n",
      "Pretrain epoch 98, recon_loss:0.613963, zinb_loss:0.713980, adversial_loss:1.318743\n",
      "Pretrain epoch 99, recon_loss:0.613066, zinb_loss:0.713427, adversial_loss:1.319201\n",
      "Pretrain epoch 100, recon_loss:0.613295, zinb_loss:0.713396, adversial_loss:1.318682\n",
      "Pretrain epoch 101, recon_loss:0.612209, zinb_loss:0.713091, adversial_loss:1.317570\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 102, recon_loss:0.613080, zinb_loss:0.713600, adversial_loss:1.316063\n",
      "Pretrain epoch 103, recon_loss:0.612514, zinb_loss:0.714490, adversial_loss:1.316991\n",
      "Pretrain epoch 104, recon_loss:0.615369, zinb_loss:0.716108, adversial_loss:1.316522\n",
      "Pretrain epoch 105, recon_loss:0.611436, zinb_loss:0.712903, adversial_loss:1.317131\n",
      "Pretrain epoch 106, recon_loss:0.612847, zinb_loss:0.713966, adversial_loss:1.316021\n",
      "Pretrain epoch 107, recon_loss:0.611477, zinb_loss:0.712955, adversial_loss:1.313847\n",
      "Pretrain epoch 108, recon_loss:0.611159, zinb_loss:0.712711, adversial_loss:1.314746\n",
      "Pretrain epoch 109, recon_loss:0.610682, zinb_loss:0.712481, adversial_loss:1.316079\n",
      "Pretrain epoch 110, recon_loss:0.609920, zinb_loss:0.712227, adversial_loss:1.314570\n",
      "Pretrain epoch 111, recon_loss:0.610153, zinb_loss:0.711834, adversial_loss:1.312195\n",
      "Pretrain epoch 112, recon_loss:0.609225, zinb_loss:0.711619, adversial_loss:1.312415\n",
      "Pretrain epoch 113, recon_loss:0.608771, zinb_loss:0.711495, adversial_loss:1.313976\n",
      "Pretrain epoch 114, recon_loss:0.608198, zinb_loss:0.711220, adversial_loss:1.313467\n",
      "Pretrain epoch 115, recon_loss:0.607821, zinb_loss:0.711039, adversial_loss:1.311498\n",
      "Pretrain epoch 116, recon_loss:0.607257, zinb_loss:0.710707, adversial_loss:1.310960\n",
      "Pretrain epoch 117, recon_loss:0.606806, zinb_loss:0.710808, adversial_loss:1.311819\n",
      "Pretrain epoch 118, recon_loss:0.606044, zinb_loss:0.710267, adversial_loss:1.311558\n",
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      "Pretrain epoch 121, recon_loss:0.604866, zinb_loss:0.709800, adversial_loss:1.310044\n",
      "Pretrain epoch 122, recon_loss:0.604549, zinb_loss:0.709613, adversial_loss:1.309614\n",
      "Pretrain epoch 123, recon_loss:0.604242, zinb_loss:0.709369, adversial_loss:1.309200\n",
      "Pretrain epoch 124, recon_loss:0.603497, zinb_loss:0.709295, adversial_loss:1.309094\n",
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      "Pretrain epoch 126, recon_loss:0.603990, zinb_loss:0.710021, adversial_loss:1.308666\n",
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      "Pretrain epoch 128, recon_loss:0.605739, zinb_loss:0.711473, adversial_loss:1.307892\n",
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      "Pretrain epoch 132, recon_loss:0.603544, zinb_loss:0.708957, adversial_loss:1.305543\n",
      "Pretrain epoch 133, recon_loss:0.602284, zinb_loss:0.709018, adversial_loss:1.305049\n",
      "Pretrain epoch 134, recon_loss:0.602843, zinb_loss:0.708935, adversial_loss:1.305499\n",
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      "Pretrain epoch 150, recon_loss:0.599595, zinb_loss:0.707788, adversial_loss:1.301056\n",
      "Pretrain epoch 151, recon_loss:0.599993, zinb_loss:0.708395, adversial_loss:1.301516\n",
      "Pretrain epoch 152, recon_loss:0.599089, zinb_loss:0.707397, adversial_loss:1.299956\n",
      "Pretrain epoch 153, recon_loss:0.598739, zinb_loss:0.707469, adversial_loss:1.298957\n",
      "Pretrain epoch 154, recon_loss:0.598425, zinb_loss:0.707356, adversial_loss:1.299692\n",
      "Pretrain epoch 155, recon_loss:0.597808, zinb_loss:0.706784, adversial_loss:1.299692\n",
      "Pretrain epoch 156, recon_loss:0.597573, zinb_loss:0.706741, adversial_loss:1.299087\n",
      "Pretrain epoch 157, recon_loss:0.597131, zinb_loss:0.706274, adversial_loss:1.298956\n",
      "Pretrain epoch 158, recon_loss:0.596734, zinb_loss:0.706350, adversial_loss:1.298658\n",
      "Pretrain epoch 159, recon_loss:0.596623, zinb_loss:0.706123, adversial_loss:1.297456\n",
      "Pretrain epoch 160, recon_loss:0.596065, zinb_loss:0.706056, adversial_loss:1.297482\n",
      "Pretrain epoch 161, recon_loss:0.596104, zinb_loss:0.706041, adversial_loss:1.298301\n",
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      "Pretrain epoch 351, recon_loss:0.560808, zinb_loss:0.694140, adversial_loss:1.268728\n",
      "Pretrain epoch 352, recon_loss:0.560411, zinb_loss:0.694014, adversial_loss:1.268849\n",
      "Pretrain epoch 353, recon_loss:0.560027, zinb_loss:0.694024, adversial_loss:1.268387\n",
      "Pretrain epoch 354, recon_loss:0.559603, zinb_loss:0.693875, adversial_loss:1.268141\n",
      "Pretrain epoch 355, recon_loss:0.559307, zinb_loss:0.693825, adversial_loss:1.268409\n",
      "Pretrain epoch 356, recon_loss:0.559076, zinb_loss:0.693744, adversial_loss:1.268051\n",
      "Pretrain epoch 357, recon_loss:0.558890, zinb_loss:0.693794, adversial_loss:1.267981\n",
      "Pretrain epoch 358, recon_loss:0.559313, zinb_loss:0.694058, adversial_loss:1.267395\n",
      "Pretrain epoch 359, recon_loss:0.560934, zinb_loss:0.694830, adversial_loss:1.268287\n",
      "Pretrain epoch 360, recon_loss:0.564220, zinb_loss:0.695833, adversial_loss:1.266836\n",
      "Pretrain epoch 361, recon_loss:0.562481, zinb_loss:0.695736, adversial_loss:1.268215\n",
      "Pretrain epoch 362, recon_loss:0.559264, zinb_loss:0.694615, adversial_loss:1.266947\n",
      "Pretrain epoch 363, recon_loss:0.560289, zinb_loss:0.694338, adversial_loss:1.266766\n",
      "Pretrain epoch 364, recon_loss:0.558944, zinb_loss:0.694178, adversial_loss:1.267188\n",
      "Pretrain epoch 365, recon_loss:0.559822, zinb_loss:0.694396, adversial_loss:1.266704\n",
      "Pretrain epoch 366, recon_loss:0.559244, zinb_loss:0.694315, adversial_loss:1.266500\n",
      "Pretrain epoch 367, recon_loss:0.558058, zinb_loss:0.693502, adversial_loss:1.265925\n",
      "Pretrain epoch 368, recon_loss:0.558544, zinb_loss:0.693841, adversial_loss:1.266192\n",
      "Pretrain epoch 369, recon_loss:0.557731, zinb_loss:0.693881, adversial_loss:1.266490\n",
      "Pretrain epoch 370, recon_loss:0.556779, zinb_loss:0.693673, adversial_loss:1.265290\n",
      "Pretrain epoch 371, recon_loss:0.556251, zinb_loss:0.693696, adversial_loss:1.265866\n",
      "Pretrain epoch 372, recon_loss:0.556175, zinb_loss:0.693686, adversial_loss:1.265823\n",
      "Pretrain epoch 373, recon_loss:0.556255, zinb_loss:0.693634, adversial_loss:1.265458\n",
      "Pretrain epoch 374, recon_loss:0.556866, zinb_loss:0.693745, adversial_loss:1.265560\n",
      "Pretrain epoch 375, recon_loss:0.558082, zinb_loss:0.693928, adversial_loss:1.265528\n",
      "Pretrain epoch 376, recon_loss:0.558450, zinb_loss:0.694458, adversial_loss:1.265274\n",
      "Pretrain epoch 377, recon_loss:0.558478, zinb_loss:0.694805, adversial_loss:1.265821\n",
      "Pretrain epoch 378, recon_loss:0.555960, zinb_loss:0.693787, adversial_loss:1.264408\n",
      "Pretrain epoch 379, recon_loss:0.555198, zinb_loss:0.693192, adversial_loss:1.265303\n",
      "Pretrain epoch 380, recon_loss:0.555539, zinb_loss:0.693390, adversial_loss:1.265130\n",
      "Pretrain epoch 381, recon_loss:0.555723, zinb_loss:0.693409, adversial_loss:1.264141\n",
      "Pretrain epoch 382, recon_loss:0.554729, zinb_loss:0.693121, adversial_loss:1.264623\n",
      "Pretrain epoch 383, recon_loss:0.553665, zinb_loss:0.693121, adversial_loss:1.264693\n",
      "Pretrain epoch 384, recon_loss:0.554122, zinb_loss:0.693272, adversial_loss:1.264246\n",
      "Pretrain epoch 385, recon_loss:0.553154, zinb_loss:0.692905, adversial_loss:1.264270\n",
      "Pretrain epoch 386, recon_loss:0.552059, zinb_loss:0.692707, adversial_loss:1.264224\n",
      "Pretrain epoch 387, recon_loss:0.553133, zinb_loss:0.692854, adversial_loss:1.263952\n",
      "Pretrain epoch 388, recon_loss:0.551707, zinb_loss:0.692689, adversial_loss:1.263738\n",
      "Pretrain epoch 389, recon_loss:0.550766, zinb_loss:0.692705, adversial_loss:1.264012\n",
      "Pretrain epoch 390, recon_loss:0.551155, zinb_loss:0.692796, adversial_loss:1.263491\n",
      "Pretrain epoch 391, recon_loss:0.550909, zinb_loss:0.692766, adversial_loss:1.263624\n",
      "Pretrain epoch 392, recon_loss:0.551803, zinb_loss:0.692801, adversial_loss:1.263383\n",
      "Pretrain epoch 393, recon_loss:0.554112, zinb_loss:0.693404, adversial_loss:1.263530\n",
      "Pretrain epoch 394, recon_loss:0.554287, zinb_loss:0.694016, adversial_loss:1.262836\n",
      "Pretrain epoch 395, recon_loss:0.554616, zinb_loss:0.694944, adversial_loss:1.263437\n",
      "Pretrain epoch 396, recon_loss:0.551981, zinb_loss:0.693592, adversial_loss:1.262114\n",
      "Pretrain epoch 397, recon_loss:0.551498, zinb_loss:0.692753, adversial_loss:1.263146\n",
      "Pretrain epoch 398, recon_loss:0.551548, zinb_loss:0.693153, adversial_loss:1.262805\n",
      "Pretrain epoch 399, recon_loss:0.550567, zinb_loss:0.693110, adversial_loss:1.261824\n",
      "Pretrain epoch 400, recon_loss:0.549860, zinb_loss:0.692564, adversial_loss:1.262827\n"
     ]
    }
   ],
   "source": [
    "pretrain_latent = model.pretrain_autoencoder(\n",
    "                        X1=adata1.X, X2=adata2.X, X1_raw=adata1.raw.X, X2_raw=adata2.raw.X, \n",
    "                        epochs=400, file='human_brain_10x',ad_out=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2b4a33b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering stage\n",
      "Initializing cluster centers with kmeans.\n",
      "Initializing k-means: AMI= 0.8622, NMI= 0.8630, ARI= 0.9469, ACC= 0.9205\n",
      "Training epoch 1, recon_loss:0.549451, zinb_loss:0.692488, cluster_loss:0.162371\n",
      "Clustering   1: AMI= 0.8622, NMI= 0.8630, ARI= 0.9469, ACC= 0.9205\n",
      "0.0\n",
      "Training epoch 2, recon_loss:0.640089, zinb_loss:0.732201, cluster_loss:0.163547\n",
      "Clustering   2: AMI= 0.8498, NMI= 0.8507, ARI= 0.9406, ACC= 0.9103\n",
      "0.011208406304728547\n",
      "Training epoch 3, recon_loss:0.685449, zinb_loss:0.832192, cluster_loss:0.168767\n",
      "Clustering   3: AMI= 0.8698, NMI= 0.8706, ARI= 0.9536, ACC= 0.9314\n",
      "0.026619964973730297\n",
      "Training epoch 4, recon_loss:0.675634, zinb_loss:0.851753, cluster_loss:0.165142\n",
      "Clustering   4: AMI= 0.8535, NMI= 0.8544, ARI= 0.9412, ACC= 0.9156\n",
      "0.023117338003502626\n",
      "Training epoch 5, recon_loss:0.651831, zinb_loss:0.733959, cluster_loss:0.164657\n",
      "Clustering   5: AMI= 0.8559, NMI= 0.8568, ARI= 0.9430, ACC= 0.9152\n",
      "0.003852889667250438\n",
      "Training epoch 6, recon_loss:0.624178, zinb_loss:0.719711, cluster_loss:0.158578\n",
      "Clustering   6: AMI= 0.8557, NMI= 0.8566, ARI= 0.9459, ACC= 0.9191\n",
      "0.009807355516637478\n",
      "Training epoch 7, recon_loss:0.624887, zinb_loss:0.721676, cluster_loss:0.161169\n",
      "Clustering   7: AMI= 0.8542, NMI= 0.8551, ARI= 0.9426, ACC= 0.9131\n",
      "0.010858143607705779\n",
      "Training epoch 8, recon_loss:0.626065, zinb_loss:0.729868, cluster_loss:0.158423\n",
      "Clustering   8: AMI= 0.8643, NMI= 0.8651, ARI= 0.9482, ACC= 0.9240\n",
      "0.012259194395796848\n",
      "Training epoch 9, recon_loss:0.625682, zinb_loss:0.726136, cluster_loss:0.158547\n",
      "Clustering   9: AMI= 0.8533, NMI= 0.8542, ARI= 0.9427, ACC= 0.9131\n",
      "0.012259194395796848\n",
      "Training epoch 10, recon_loss:0.613201, zinb_loss:0.726208, cluster_loss:0.155239\n",
      "Clustering   10: AMI= 0.8640, NMI= 0.8648, ARI= 0.9484, ACC= 0.9229\n",
      "0.011558669001751313\n",
      "Training epoch 11, recon_loss:0.617372, zinb_loss:0.720601, cluster_loss:0.153553\n",
      "Clustering   11: AMI= 0.8549, NMI= 0.8558, ARI= 0.9442, ACC= 0.9156\n",
      "0.009106830122591943\n",
      "Training epoch 12, recon_loss:0.615435, zinb_loss:0.723348, cluster_loss:0.154792\n",
      "Clustering   12: AMI= 0.8651, NMI= 0.8659, ARI= 0.9492, ACC= 0.9233\n",
      "0.009457092819614711\n",
      "Training epoch 13, recon_loss:0.625145, zinb_loss:0.725200, cluster_loss:0.154635\n",
      "Clustering   13: AMI= 0.8545, NMI= 0.8554, ARI= 0.9434, ACC= 0.9156\n",
      "0.009457092819614711\n",
      "Training epoch 14, recon_loss:0.624145, zinb_loss:0.728885, cluster_loss:0.155729\n",
      "Clustering   14: AMI= 0.8616, NMI= 0.8624, ARI= 0.9482, ACC= 0.9212\n",
      "0.007355516637478109\n",
      "Training epoch 15, recon_loss:0.620394, zinb_loss:0.725838, cluster_loss:0.152966\n",
      "Clustering   15: AMI= 0.8588, NMI= 0.8597, ARI= 0.9455, ACC= 0.9184\n",
      "0.003852889667250438\n",
      "Training epoch 16, recon_loss:0.618429, zinb_loss:0.726171, cluster_loss:0.152424\n",
      "Clustering   16: AMI= 0.8603, NMI= 0.8612, ARI= 0.9466, ACC= 0.9177\n",
      "0.0028021015761821367\n",
      "Training epoch 17, recon_loss:0.611081, zinb_loss:0.721310, cluster_loss:0.150749\n",
      "Clustering   17: AMI= 0.8616, NMI= 0.8625, ARI= 0.9474, ACC= 0.9212\n",
      "0.005954465849387041\n",
      "Training epoch 18, recon_loss:0.616190, zinb_loss:0.723877, cluster_loss:0.150925\n",
      "Clustering   18: AMI= 0.8576, NMI= 0.8584, ARI= 0.9454, ACC= 0.9166\n",
      "0.005604203152364273\n",
      "Training epoch 19, recon_loss:0.610348, zinb_loss:0.720660, cluster_loss:0.149796\n",
      "Clustering   19: AMI= 0.8640, NMI= 0.8648, ARI= 0.9484, ACC= 0.9229\n",
      "0.007355516637478109\n",
      "Training epoch 20, recon_loss:0.614614, zinb_loss:0.721962, cluster_loss:0.150279\n",
      "Clustering   20: AMI= 0.8560, NMI= 0.8569, ARI= 0.9444, ACC= 0.9149\n",
      "0.009106830122591943\n",
      "Training epoch 21, recon_loss:0.609703, zinb_loss:0.718648, cluster_loss:0.149179\n",
      "Clustering   21: AMI= 0.8649, NMI= 0.8658, ARI= 0.9491, ACC= 0.9233\n",
      "0.009807355516637478\n",
      "Training epoch 22, recon_loss:0.608740, zinb_loss:0.718995, cluster_loss:0.148605\n",
      "Clustering   22: AMI= 0.8566, NMI= 0.8575, ARI= 0.9446, ACC= 0.9152\n",
      "0.009457092819614711\n",
      "Training epoch 23, recon_loss:0.607299, zinb_loss:0.716110, cluster_loss:0.147979\n",
      "Clustering   23: AMI= 0.8641, NMI= 0.8649, ARI= 0.9489, ACC= 0.9229\n",
      "0.009807355516637478\n",
      "Training epoch 24, recon_loss:0.604798, zinb_loss:0.717080, cluster_loss:0.146979\n",
      "Clustering   24: AMI= 0.8578, NMI= 0.8586, ARI= 0.9461, ACC= 0.9166\n",
      "0.008056042031523642\n",
      "Training epoch 25, recon_loss:0.606213, zinb_loss:0.715191, cluster_loss:0.146699\n",
      "Clustering   25: AMI= 0.8638, NMI= 0.8646, ARI= 0.9486, ACC= 0.9226\n",
      "0.007705779334500876\n",
      "Training epoch 26, recon_loss:0.604126, zinb_loss:0.717078, cluster_loss:0.146109\n",
      "Clustering   26: AMI= 0.8597, NMI= 0.8605, ARI= 0.9466, ACC= 0.9177\n",
      "0.006654991243432574\n",
      "Training epoch 27, recon_loss:0.606793, zinb_loss:0.715622, cluster_loss:0.145879\n",
      "Clustering   27: AMI= 0.8638, NMI= 0.8646, ARI= 0.9486, ACC= 0.9226\n",
      "0.006654991243432574\n",
      "Training epoch 28, recon_loss:0.603360, zinb_loss:0.717227, cluster_loss:0.145428\n",
      "Clustering   28: AMI= 0.8613, NMI= 0.8622, ARI= 0.9471, ACC= 0.9187\n",
      "0.005604203152364273\n",
      "Training epoch 29, recon_loss:0.607269, zinb_loss:0.716282, cluster_loss:0.145180\n",
      "Clustering   29: AMI= 0.8622, NMI= 0.8631, ARI= 0.9483, ACC= 0.9219\n",
      "0.004903677758318739\n",
      "Training epoch 30, recon_loss:0.603986, zinb_loss:0.717796, cluster_loss:0.145236\n",
      "Clustering   30: AMI= 0.8643, NMI= 0.8651, ARI= 0.9475, ACC= 0.9198\n",
      "0.005253940455341506\n",
      "Training epoch 31, recon_loss:0.608581, zinb_loss:0.717860, cluster_loss:0.144896\n",
      "Clustering   31: AMI= 0.8620, NMI= 0.8629, ARI= 0.9490, ACC= 0.9215\n",
      "0.005954465849387041\n",
      "Training epoch 32, recon_loss:0.606029, zinb_loss:0.719616, cluster_loss:0.145814\n",
      "Clustering   32: AMI= 0.8640, NMI= 0.8649, ARI= 0.9473, ACC= 0.9194\n",
      "0.006304728546409807\n",
      "Training epoch 33, recon_loss:0.611021, zinb_loss:0.721108, cluster_loss:0.145631\n",
      "Clustering   33: AMI= 0.8637, NMI= 0.8645, ARI= 0.9509, ACC= 0.9219\n",
      "0.007355516637478109\n",
      "Training epoch 34, recon_loss:0.608186, zinb_loss:0.721887, cluster_loss:0.145688\n",
      "Clustering   34: AMI= 0.8630, NMI= 0.8638, ARI= 0.9470, ACC= 0.9191\n",
      "0.007705779334500876\n",
      "Training epoch 35, recon_loss:0.613040, zinb_loss:0.723958, cluster_loss:0.145933\n",
      "Clustering   35: AMI= 0.8649, NMI= 0.8658, ARI= 0.9518, ACC= 0.9222\n",
      "0.008056042031523642\n",
      "Training epoch 36, recon_loss:0.611296, zinb_loss:0.724701, cluster_loss:0.145112\n",
      "Clustering   36: AMI= 0.8632, NMI= 0.8641, ARI= 0.9473, ACC= 0.9198\n",
      "0.007705779334500876\n",
      "Training epoch 37, recon_loss:0.613010, zinb_loss:0.726143, cluster_loss:0.145694\n",
      "Clustering   37: AMI= 0.8644, NMI= 0.8652, ARI= 0.9514, ACC= 0.9215\n",
      "0.0070052539404553416\n",
      "Training epoch 38, recon_loss:0.610502, zinb_loss:0.725018, cluster_loss:0.144277\n",
      "Clustering   38: AMI= 0.8643, NMI= 0.8652, ARI= 0.9477, ACC= 0.9205\n",
      "0.0070052539404553416\n",
      "Training epoch 39, recon_loss:0.608361, zinb_loss:0.724345, cluster_loss:0.144785\n",
      "Clustering   39: AMI= 0.8661, NMI= 0.8669, ARI= 0.9515, ACC= 0.9215\n",
      "0.005253940455341506\n",
      "Training epoch 40, recon_loss:0.607315, zinb_loss:0.722422, cluster_loss:0.143454\n",
      "Clustering   40: AMI= 0.8657, NMI= 0.8665, ARI= 0.9495, ACC= 0.9208\n",
      "0.004203152364273205\n",
      "Training epoch 41, recon_loss:0.604846, zinb_loss:0.721579, cluster_loss:0.144169\n",
      "Clustering   41: AMI= 0.8667, NMI= 0.8675, ARI= 0.9514, ACC= 0.9212\n",
      "0.0035026269702276708\n",
      "Training epoch 42, recon_loss:0.605260, zinb_loss:0.720154, cluster_loss:0.142992\n",
      "Clustering   42: AMI= 0.8672, NMI= 0.8680, ARI= 0.9513, ACC= 0.9219\n",
      "0.0035026269702276708\n",
      "Training epoch 43, recon_loss:0.603215, zinb_loss:0.719708, cluster_loss:0.143956\n",
      "Clustering   43: AMI= 0.8662, NMI= 0.8670, ARI= 0.9510, ACC= 0.9205\n",
      "0.0035026269702276708\n",
      "Training epoch 44, recon_loss:0.604579, zinb_loss:0.719022, cluster_loss:0.142965\n",
      "Clustering   44: AMI= 0.8667, NMI= 0.8675, ARI= 0.9514, ACC= 0.9222\n",
      "0.004553415061295972\n",
      "Training epoch 45, recon_loss:0.603409, zinb_loss:0.718984, cluster_loss:0.144157\n",
      "Clustering   45: AMI= 0.8657, NMI= 0.8665, ARI= 0.9506, ACC= 0.9198\n",
      "0.005253940455341506\n",
      "Training epoch 46, recon_loss:0.604425, zinb_loss:0.718567, cluster_loss:0.143013\n",
      "Clustering   46: AMI= 0.8679, NMI= 0.8687, ARI= 0.9517, ACC= 0.9229\n",
      "0.005954465849387041\n",
      "Training epoch 47, recon_loss:0.603239, zinb_loss:0.718713, cluster_loss:0.144000\n",
      "Clustering   47: AMI= 0.8659, NMI= 0.8668, ARI= 0.9503, ACC= 0.9194\n",
      "0.006304728546409807\n",
      "Training epoch 48, recon_loss:0.603336, zinb_loss:0.718226, cluster_loss:0.142696\n",
      "Clustering   48: AMI= 0.8670, NMI= 0.8678, ARI= 0.9516, ACC= 0.9226\n",
      "0.005954465849387041\n",
      "Training epoch 49, recon_loss:0.602034, zinb_loss:0.718417, cluster_loss:0.143298\n",
      "Clustering   49: AMI= 0.8650, NMI= 0.8658, ARI= 0.9497, ACC= 0.9198\n",
      "0.005253940455341506\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 50, recon_loss:0.602262, zinb_loss:0.718224, cluster_loss:0.142242\n",
      "Clustering   50: AMI= 0.8670, NMI= 0.8678, ARI= 0.9516, ACC= 0.9226\n",
      "0.005253940455341506\n",
      "Training epoch 51, recon_loss:0.601119, zinb_loss:0.718334, cluster_loss:0.142575\n",
      "Clustering   51: AMI= 0.8661, NMI= 0.8669, ARI= 0.9500, ACC= 0.9205\n",
      "0.004553415061295972\n",
      "Training epoch 52, recon_loss:0.601837, zinb_loss:0.718602, cluster_loss:0.141834\n",
      "Clustering   52: AMI= 0.8670, NMI= 0.8678, ARI= 0.9516, ACC= 0.9226\n",
      "0.004203152364273205\n",
      "Training epoch 53, recon_loss:0.601436, zinb_loss:0.718703, cluster_loss:0.142080\n",
      "Clustering   53: AMI= 0.8663, NMI= 0.8671, ARI= 0.9503, ACC= 0.9208\n",
      "0.003852889667250438\n",
      "Training epoch 54, recon_loss:0.602931, zinb_loss:0.719448, cluster_loss:0.141679\n",
      "Clustering   54: AMI= 0.8679, NMI= 0.8687, ARI= 0.9515, ACC= 0.9222\n",
      "0.0024518388791593695\n",
      "Training epoch 55, recon_loss:0.603656, zinb_loss:0.719185, cluster_loss:0.142018\n",
      "Clustering   55: AMI= 0.8677, NMI= 0.8685, ARI= 0.9508, ACC= 0.9219\n",
      "0.0014010507880910684\n",
      "Training epoch 56, recon_loss:0.606028, zinb_loss:0.720632, cluster_loss:0.142011\n",
      "Clustering   56: AMI= 0.8672, NMI= 0.8681, ARI= 0.9511, ACC= 0.9212\n",
      "0.0014010507880910684\n",
      "Training epoch 57, recon_loss:0.607589, zinb_loss:0.719708, cluster_loss:0.142362\n",
      "Clustering   57: AMI= 0.8676, NMI= 0.8684, ARI= 0.9519, ACC= 0.9226\n",
      "0.003852889667250438\n",
      "Training epoch 58, recon_loss:0.608578, zinb_loss:0.720954, cluster_loss:0.142743\n",
      "Clustering   58: AMI= 0.8668, NMI= 0.8676, ARI= 0.9507, ACC= 0.9205\n",
      "0.004553415061295972\n",
      "Training epoch 59, recon_loss:0.608005, zinb_loss:0.719159, cluster_loss:0.142378\n",
      "Clustering   59: AMI= 0.8687, NMI= 0.8696, ARI= 0.9527, ACC= 0.9243\n",
      "0.006654991243432574\n",
      "Training epoch 60, recon_loss:0.606807, zinb_loss:0.719807, cluster_loss:0.142749\n",
      "Clustering   60: AMI= 0.8660, NMI= 0.8669, ARI= 0.9501, ACC= 0.9194\n",
      "0.007705779334500876\n",
      "Training epoch 61, recon_loss:0.605815, zinb_loss:0.718432, cluster_loss:0.142026\n",
      "Clustering   61: AMI= 0.8687, NMI= 0.8696, ARI= 0.9527, ACC= 0.9243\n",
      "0.007705779334500876\n",
      "Training epoch 62, recon_loss:0.604664, zinb_loss:0.718820, cluster_loss:0.142141\n",
      "Clustering   62: AMI= 0.8644, NMI= 0.8652, ARI= 0.9492, ACC= 0.9191\n",
      "0.007355516637478109\n",
      "Training epoch 63, recon_loss:0.603523, zinb_loss:0.717936, cluster_loss:0.141239\n",
      "Clustering   63: AMI= 0.8679, NMI= 0.8687, ARI= 0.9525, ACC= 0.9240\n",
      "0.0070052539404553416\n",
      "Training epoch 64, recon_loss:0.603136, zinb_loss:0.718222, cluster_loss:0.141502\n",
      "Clustering   64: AMI= 0.8644, NMI= 0.8652, ARI= 0.9492, ACC= 0.9191\n",
      "0.0070052539404553416\n",
      "Training epoch 65, recon_loss:0.601645, zinb_loss:0.717459, cluster_loss:0.140634\n",
      "Clustering   65: AMI= 0.8679, NMI= 0.8687, ARI= 0.9525, ACC= 0.9240\n",
      "0.0070052539404553416\n",
      "Training epoch 66, recon_loss:0.601711, zinb_loss:0.717768, cluster_loss:0.140978\n",
      "Clustering   66: AMI= 0.8644, NMI= 0.8652, ARI= 0.9492, ACC= 0.9191\n",
      "0.0070052539404553416\n",
      "Training epoch 67, recon_loss:0.600909, zinb_loss:0.717509, cluster_loss:0.140251\n",
      "Clustering   67: AMI= 0.8665, NMI= 0.8673, ARI= 0.9519, ACC= 0.9229\n",
      "0.005954465849387041\n",
      "Training epoch 68, recon_loss:0.601650, zinb_loss:0.717700, cluster_loss:0.140891\n",
      "Clustering   68: AMI= 0.8662, NMI= 0.8670, ARI= 0.9508, ACC= 0.9205\n",
      "0.0035026269702276708\n",
      "Training epoch 69, recon_loss:0.602169, zinb_loss:0.718253, cluster_loss:0.140751\n",
      "Clustering   69: AMI= 0.8658, NMI= 0.8666, ARI= 0.9515, ACC= 0.9222\n",
      "0.003852889667250438\n",
      "Training epoch 70, recon_loss:0.603932, zinb_loss:0.717988, cluster_loss:0.141681\n",
      "Clustering   70: AMI= 0.8673, NMI= 0.8682, ARI= 0.9512, ACC= 0.9212\n",
      "0.004203152364273205\n",
      "Training epoch 71, recon_loss:0.604666, zinb_loss:0.718659, cluster_loss:0.142015\n",
      "Clustering   71: AMI= 0.8654, NMI= 0.8662, ARI= 0.9513, ACC= 0.9215\n",
      "0.003852889667250438\n",
      "Training epoch 72, recon_loss:0.606795, zinb_loss:0.717479, cluster_loss:0.142350\n",
      "Clustering   72: AMI= 0.8691, NMI= 0.8699, ARI= 0.9519, ACC= 0.9222\n",
      "0.0035026269702276708\n",
      "Training epoch 73, recon_loss:0.604675, zinb_loss:0.717435, cluster_loss:0.141867\n",
      "Clustering   73: AMI= 0.8641, NMI= 0.8650, ARI= 0.9506, ACC= 0.9198\n",
      "0.004553415061295972\n",
      "Training epoch 74, recon_loss:0.607221, zinb_loss:0.717044, cluster_loss:0.142258\n",
      "Clustering   74: AMI= 0.8677, NMI= 0.8685, ARI= 0.9518, ACC= 0.9226\n",
      "0.005954465849387041\n",
      "Training epoch 75, recon_loss:0.605994, zinb_loss:0.717295, cluster_loss:0.141995\n",
      "Clustering   75: AMI= 0.8616, NMI= 0.8625, ARI= 0.9491, ACC= 0.9184\n",
      "0.007705779334500876\n",
      "Training epoch 76, recon_loss:0.607849, zinb_loss:0.718216, cluster_loss:0.141943\n",
      "Clustering   76: AMI= 0.8689, NMI= 0.8697, ARI= 0.9526, ACC= 0.9240\n",
      "0.008756567425569177\n",
      "Training epoch 77, recon_loss:0.607392, zinb_loss:0.719233, cluster_loss:0.141893\n",
      "Clustering   77: AMI= 0.8625, NMI= 0.8634, ARI= 0.9494, ACC= 0.9177\n",
      "0.010157618213660246\n",
      "Training epoch 78, recon_loss:0.607323, zinb_loss:0.719040, cluster_loss:0.141912\n",
      "Clustering   78: AMI= 0.8687, NMI= 0.8695, ARI= 0.9527, ACC= 0.9240\n",
      "0.010157618213660246\n",
      "Training epoch 79, recon_loss:0.605948, zinb_loss:0.719666, cluster_loss:0.141002\n",
      "Clustering   79: AMI= 0.8644, NMI= 0.8653, ARI= 0.9498, ACC= 0.9187\n",
      "0.008756567425569177\n",
      "Training epoch 80, recon_loss:0.605869, zinb_loss:0.719446, cluster_loss:0.141503\n",
      "Clustering   80: AMI= 0.8685, NMI= 0.8693, ARI= 0.9525, ACC= 0.9240\n",
      "0.008056042031523642\n",
      "Training epoch 81, recon_loss:0.603889, zinb_loss:0.719383, cluster_loss:0.139484\n",
      "Clustering   81: AMI= 0.8679, NMI= 0.8688, ARI= 0.9510, ACC= 0.9212\n",
      "0.006304728546409807\n",
      "Training epoch 82, recon_loss:0.603303, zinb_loss:0.719154, cluster_loss:0.139949\n",
      "Clustering   82: AMI= 0.8685, NMI= 0.8693, ARI= 0.9520, ACC= 0.9229\n",
      "0.004903677758318739\n",
      "Training epoch 83, recon_loss:0.605164, zinb_loss:0.719830, cluster_loss:0.139907\n",
      "Clustering   83: AMI= 0.8684, NMI= 0.8692, ARI= 0.9512, ACC= 0.9215\n",
      "0.0035026269702276708\n",
      "Training epoch 84, recon_loss:0.608950, zinb_loss:0.721405, cluster_loss:0.142167\n",
      "Clustering   84: AMI= 0.8669, NMI= 0.8678, ARI= 0.9517, ACC= 0.9226\n",
      "0.004203152364273205\n",
      "Training epoch 85, recon_loss:0.604936, zinb_loss:0.720668, cluster_loss:0.139509\n",
      "Clustering   85: AMI= 0.8677, NMI= 0.8685, ARI= 0.9505, ACC= 0.9215\n",
      "0.004203152364273205\n",
      "Training epoch 86, recon_loss:0.602480, zinb_loss:0.719328, cluster_loss:0.139739\n",
      "Clustering   86: AMI= 0.8677, NMI= 0.8685, ARI= 0.9519, ACC= 0.9226\n",
      "0.003852889667250438\n",
      "Training epoch 87, recon_loss:0.601794, zinb_loss:0.719214, cluster_loss:0.138519\n",
      "Clustering   87: AMI= 0.8690, NMI= 0.8698, ARI= 0.9519, ACC= 0.9226\n",
      "0.0024518388791593695\n",
      "Training epoch 88, recon_loss:0.601587, zinb_loss:0.719017, cluster_loss:0.139194\n",
      "Clustering   88: AMI= 0.8676, NMI= 0.8684, ARI= 0.9516, ACC= 0.9219\n",
      "0.0017513134851138354\n",
      "Training epoch 89, recon_loss:0.602505, zinb_loss:0.719565, cluster_loss:0.138428\n",
      "Clustering   89: AMI= 0.8693, NMI= 0.8701, ARI= 0.9521, ACC= 0.9226\n",
      "0.0024518388791593695\n",
      "Training epoch 90, recon_loss:0.602561, zinb_loss:0.719266, cluster_loss:0.139306\n",
      "Clustering   90: AMI= 0.8665, NMI= 0.8673, ARI= 0.9512, ACC= 0.9212\n",
      "0.0031523642732049035\n",
      "Training epoch 91, recon_loss:0.604219, zinb_loss:0.719951, cluster_loss:0.138682\n",
      "Clustering   91: AMI= 0.8685, NMI= 0.8693, ARI= 0.9519, ACC= 0.9226\n",
      "0.0035026269702276708\n",
      "Training epoch 92, recon_loss:0.603979, zinb_loss:0.719540, cluster_loss:0.139611\n",
      "Clustering   92: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.004203152364273205\n",
      "Training epoch 93, recon_loss:0.604886, zinb_loss:0.719569, cluster_loss:0.138756\n",
      "Clustering   93: AMI= 0.8695, NMI= 0.8703, ARI= 0.9521, ACC= 0.9229\n",
      "0.004553415061295972\n",
      "Training epoch 94, recon_loss:0.603619, zinb_loss:0.719021, cluster_loss:0.139620\n",
      "Clustering   94: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.004553415061295972\n",
      "Training epoch 95, recon_loss:0.603860, zinb_loss:0.718555, cluster_loss:0.138465\n",
      "Clustering   95: AMI= 0.8691, NMI= 0.8699, ARI= 0.9521, ACC= 0.9233\n",
      "0.005253940455341506\n",
      "Training epoch 96, recon_loss:0.602480, zinb_loss:0.718075, cluster_loss:0.139278\n",
      "Clustering   96: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.005253940455341506\n",
      "Training epoch 97, recon_loss:0.602678, zinb_loss:0.717587, cluster_loss:0.137989\n",
      "Clustering   97: AMI= 0.8691, NMI= 0.8699, ARI= 0.9521, ACC= 0.9233\n",
      "0.005253940455341506\n",
      "Training epoch 98, recon_loss:0.601232, zinb_loss:0.717243, cluster_loss:0.138816\n",
      "Clustering   98: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.005253940455341506\n",
      "Training epoch 99, recon_loss:0.601591, zinb_loss:0.716944, cluster_loss:0.137535\n",
      "Clustering   99: AMI= 0.8691, NMI= 0.8699, ARI= 0.9521, ACC= 0.9233\n",
      "0.005253940455341506\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 100, recon_loss:0.600434, zinb_loss:0.716724, cluster_loss:0.138437\n",
      "Clustering   100: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.005253940455341506\n",
      "Training epoch 101, recon_loss:0.601307, zinb_loss:0.716692, cluster_loss:0.137250\n",
      "Clustering   101: AMI= 0.8691, NMI= 0.8699, ARI= 0.9521, ACC= 0.9233\n",
      "0.005253940455341506\n",
      "Training epoch 102, recon_loss:0.600167, zinb_loss:0.716487, cluster_loss:0.138199\n",
      "Clustering   102: AMI= 0.8660, NMI= 0.8668, ARI= 0.9508, ACC= 0.9205\n",
      "0.005253940455341506\n",
      "Training epoch 103, recon_loss:0.601496, zinb_loss:0.716779, cluster_loss:0.137125\n",
      "Clustering   103: AMI= 0.8691, NMI= 0.8699, ARI= 0.9521, ACC= 0.9233\n",
      "0.005253940455341506\n",
      "Training epoch 104, recon_loss:0.600451, zinb_loss:0.716624, cluster_loss:0.138094\n",
      "Clustering   104: AMI= 0.8669, NMI= 0.8678, ARI= 0.9509, ACC= 0.9208\n",
      "0.004903677758318739\n",
      "Training epoch 105, recon_loss:0.602454, zinb_loss:0.717330, cluster_loss:0.137207\n",
      "Clustering   105: AMI= 0.8683, NMI= 0.8691, ARI= 0.9522, ACC= 0.9233\n",
      "0.004903677758318739\n",
      "Training epoch 106, recon_loss:0.601487, zinb_loss:0.717454, cluster_loss:0.138186\n",
      "Clustering   106: AMI= 0.8669, NMI= 0.8678, ARI= 0.9509, ACC= 0.9208\n",
      "0.004903677758318739\n",
      "Training epoch 107, recon_loss:0.603991, zinb_loss:0.718396, cluster_loss:0.137502\n",
      "Clustering   107: AMI= 0.8681, NMI= 0.8689, ARI= 0.9519, ACC= 0.9229\n",
      "0.004553415061295972\n",
      "Training epoch 108, recon_loss:0.602912, zinb_loss:0.719125, cluster_loss:0.138430\n",
      "Clustering   108: AMI= 0.8688, NMI= 0.8696, ARI= 0.9514, ACC= 0.9215\n",
      "0.004553415061295972\n",
      "Training epoch 109, recon_loss:0.605555, zinb_loss:0.719818, cluster_loss:0.137868\n",
      "Clustering   109: AMI= 0.8678, NMI= 0.8686, ARI= 0.9517, ACC= 0.9226\n",
      "0.004203152364273205\n",
      "Training epoch 110, recon_loss:0.603538, zinb_loss:0.720865, cluster_loss:0.138547\n",
      "Clustering   110: AMI= 0.8690, NMI= 0.8698, ARI= 0.9518, ACC= 0.9222\n",
      "0.0031523642732049035\n",
      "Training epoch 111, recon_loss:0.605642, zinb_loss:0.720811, cluster_loss:0.137891\n",
      "Clustering   111: AMI= 0.8678, NMI= 0.8686, ARI= 0.9517, ACC= 0.9226\n",
      "0.0031523642732049035\n",
      "Training epoch 112, recon_loss:0.602853, zinb_loss:0.721649, cluster_loss:0.138275\n",
      "Clustering   112: AMI= 0.8702, NMI= 0.8710, ARI= 0.9524, ACC= 0.9233\n",
      "0.0014010507880910684\n",
      "Training epoch 113, recon_loss:0.604192, zinb_loss:0.721005, cluster_loss:0.137401\n",
      "Clustering   113: AMI= 0.8685, NMI= 0.8693, ARI= 0.9519, ACC= 0.9229\n",
      "0.0007005253940455342\n",
      "delta_label  0.0007005253940455342 < tol  0.001\n",
      "Reach tolerance threshold. Stopping training.\n",
      "Final Result : AMI= 0.8685, NMI= 0.8693, ARI= 0.9519, ACC= 0.9229\n"
     ]
    }
   ],
   "source": [
    "y_pred, final_latent = model.fit(y=y, n_clusters=8, file='human_brain_10x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "652e1cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_pred_ = best_map(y, y_pred).astype(int)\n",
    "np.savetxt(\"../results/human_brain_10x_pred.csv\", y_pred, delimiter=\",\")\n",
    "np.savetxt(\"../results/human_brain_10x_embedding.csv\", final_latent.cpu().detach().numpy(), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2a5e7b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(umap)\n",
    "library(ggplot2)\n",
    "library(scattermore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f6866dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent <- read.csv(file = \"../results/human_brain_10x_embedding.csv\", sep = \",\", header = FALSE)\n",
    "y_pred <- read.csv(file = \"../results/human_brain_10x_pred.csv\", sep = \",\", header = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "782629b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "z.umap<-umap(latent)\n",
    "y_pred$V1<-as.factor(y_pred$V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60e5f26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bm_table_num <- data.frame(UMAP1=z.umap$layout[,1], UMAP2=z.umap$layout[,2],Cluster = y_pred$V1)\n",
    "bm_plot1 <- ggplot(bm_table_num, aes(x = UMAP1, y = UMAP2, color = Cluster)) +\n",
    "  geom_point(size = 0.1) +\n",
    "  ggtitle('scMAGCA') +\n",
    "  theme_bw(base_line_size = 1,base_rect_size = 1)+\n",
    "  scale_color_manual(values = c( \"0\"=\"#7587b1\", \"1\"=\"#e0bc58\", \"2\"=\"#64abc0\", \"3\"=\"#dc8e97\", \"4\"=\"#74a893\", \"5\"=\"#ac9141\",\n",
    "                               \"6\"=\"#ebce8e\", \"7\"=\"#ac5092\"))+\n",
    "  labs(x = \"\", y = \"\", color = \"Label\")+  # Set x and y axis labels\n",
    "  theme(\n",
    "    plot.title = element_text(size = 20, hjust = 0.5, face = 'bold'),  # Set title to center\n",
    "    panel.border = element_blank(),\n",
    "    axis.ticks.length=unit(0, \"lines\"),  # Remove axis ticks but keep axis labels\n",
    "    axis.text = element_blank(),\n",
    "    panel.grid.minor = element_blank(),\n",
    "    panel.grid.major = element_line(color = NA),\n",
    "    axis.title.x = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    axis.title.y = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    legend.title = element_blank(),\n",
    "    legend.text = element_text(size = 15),\n",
    "    legend.position = \"none\")+\n",
    "  guides(color = guide_legend(override.aes = list(size = 5), nrow=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c31655e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DFp7o8vlyhlKZP04SO17xxu21U39dpZIqUkqyp/6vVV9R8dD3qDxi9a81eUj+Lz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\nizSTRiDg4TjZPzSxlMCOy+E8cHvJ4Ii7tmo5l3dvEOnmhGSDUijg2cYDs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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm_plot1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}