docs/source/tutorials/vimcca_protein_rna_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joint analyse of RNA + Protein multimodal dataset using VIMCCA\n",
    "\n",
    "This tutorial shows loading, preprocessing, VIMCCA joint analyse of RNA + Protein multimodal dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages\n",
    "Here, we’ll import scbean along with other popular packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('TkAgg')\n",
    "import scanpy as sc\n",
    "from scbean.model import vimcca\n",
    "\n",
    "# Command for Jupyter Notebooks only\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from matplotlib.axes._axes import _log as matplotlib_axes_logger\n",
    "matplotlib_axes_logger.setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading dataset\n",
    "This tutorial uses multimodal single cell data of human PBMCs from a healthy donor, which measured 36,601 genes and 17 proteins on 8,258 cells at the same time, which can be downloaded from [10X Genomics](https://support.10xgenomics.com/single-cell-vdj/datasets/3.0.0/vdj_v1_hs_pbmc2_5gex_protein).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    }
   ],
   "source": [
    "base_path = '/Users/zhongyuanke/data/'\n",
    "file_rna = base_path + 'multimodal/protein/vdj_v1_hs_pbmc2_5gex_protein_filtered_feature_bc_matrix.h5'\n",
    "file_protein = base_path + 'multimodal/protein/protein.csv'\n",
    "\n",
    "adata_x= sc.read_10x_h5(file_rna)\n",
    "adata_y = sc.read_csv(file_protein)\n",
    "adata_x.var_names_make_unique()\n",
    "adata_y.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing using scanpy\n",
    "Here, we filter and normalize each modal separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.filter_genes(adata_x, min_cells=10)\n",
    "sc.pp.log1p(adata_x)\n",
    "sc.pp.log1p(adata_y)\n",
    "sc.pp.scale(adata_x)\n",
    "sc.pp.scale(adata_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint analyse using VIMCCA\n",
    "The returned z represents the complementary low-dimensional features of the two modalities learned by VIMCCA, which can be used for downstream analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"VIMCCA_mlp\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inputs_x (InputLayer)           [(None, 15446)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder_z (Functional)          [(None, 8), (None, 8 1988752     inputs_x[0][0]                   \n",
      "                                                                 inputs_x[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "inputs_y (InputLayer)           [(None, 17)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "decoder_x (Functional)          (None, 15446)        2003926     encoder_z[0][2]                  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_y (Functional)          (None, 17)           13585       encoder_z[1][2]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128)          1977216     inputs_x[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128)          384         dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 128)          0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 128)          0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 64)           8256        dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 64)           192         dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 64)           0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 64)           0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract_2 (TFOpLambda) (None, 17)           0           inputs_y[0][0]                   \n",
      "                                                                 decoder_y[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 32)           2080        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract (TFOpLambda)   (None, 15446)        0           inputs_x[0][0]                   \n",
      "                                                                 decoder_x[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_4 (TFOpLamb (1, 1)               0           tf.math.subtract_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32)           96          dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_2 (TFOpLamb (1, 1)               0           tf.math.subtract[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract_3 (TFOpLambda) (None, 17)           0           tf.math.subtract_2[0][0]         \n",
      "                                                                 tf.math.reduce_mean_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32)           0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract_1 (TFOpLambda) (None, 15446)        0           tf.math.subtract[0][0]           \n",
      "                                                                 tf.math.reduce_mean_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.convert_to_tensor_1 (TFOpLam (None, 17)           0           decoder_y[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.cast_1 (TFOpLambda)          (None, 17)           0           inputs_y[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.square_1 (TFOpLambda)   (None, 17)           0           tf.math.subtract_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 32)           0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf.convert_to_tensor (TFOpLambd (None, 15446)        0           decoder_x[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.cast (TFOpLambda)            (None, 15446)        0           inputs_x[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.square (TFOpLambda)     (None, 15446)        0           tf.math.subtract_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.squared_difference_1 (T (None, 17)           0           tf.convert_to_tensor_1[0][0]     \n",
      "                                                                 tf.cast_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_5 (TFOpLamb ()                   0           tf.math.square_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "z_log_var (Dense)               (None, 8)            264         dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "z_mean (Dense)                  (None, 8)            264         dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.squared_difference (TFO (None, 15446)        0           tf.convert_to_tensor[0][0]       \n",
      "                                                                 tf.cast[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_3 (TFOpLamb ()                   0           tf.math.square[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_1 (TFOpLamb (None,)              0           tf.math.squared_difference_1[0][0\n",
      "__________________________________________________________________________________________________\n",
      "tf.math.truediv_2 (TFOpLambda)  ()                   0           tf.math.reduce_mean_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.truediv_3 (TFOpLambda)  ()                   0           tf.math.reduce_mean_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.log_1 (TFOpLambda)      ()                   0           tf.math.reduce_mean_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add_2 (TFOpLam (None, 8)            0           z_log_var[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.square_2 (TFOpLambda)   (None, 8)            0           z_mean[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean (TFOpLambda (None,)              0           tf.math.squared_difference[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.truediv (TFOpLambda)    ()                   0           tf.math.reduce_mean_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.truediv_1 (TFOpLambda)  ()                   0           tf.math.reduce_mean_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.log (TFOpLambda)        ()                   0           tf.math.reduce_mean_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_2 (TFOpLambda) (None,)              0           tf.math.reduce_mean_1[0][0]      \n",
      "                                                                 tf.math.truediv_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_3 (TFOpLambda) ()                   0           tf.math.truediv_3[0][0]          \n",
      "                                                                 tf.math.log_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract_4 (TFOpLambda) (None, 8)            0           tf.__operators__.add_2[0][0]     \n",
      "                                                                 tf.math.square_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.exp (TFOpLambda)        (None, 8)            0           z_log_var[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply (TFOpLambda)   (None,)              0           tf.math.reduce_mean[0][0]        \n",
      "                                                                 tf.math.truediv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_1 (TFOpLambda) ()                   0           tf.math.truediv_1[0][0]          \n",
      "                                                                 tf.math.log[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add_1 (TFOpLam (None,)              0           tf.math.multiply_2[0][0]         \n",
      "                                                                 tf.math.multiply_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.subtract_5 (TFOpLambda) (None, 8)            0           tf.math.subtract_4[0][0]         \n",
      "                                                                 tf.math.exp[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add (TFOpLambd (None,)              0           tf.math.multiply[0][0]           \n",
      "                                                                 tf.math.multiply_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_5 (TFOpLambda) (None,)              0           tf.__operators__.add_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_sum (TFOpLambda) (None,)              0           tf.math.subtract_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add_3 (TFOpLam (None,)              0           tf.__operators__.add[0][0]       \n",
      "                                                                 tf.math.multiply_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_4 (TFOpLambda) (None,)              0           tf.math.reduce_sum[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.add_4 (TFOpLam (None,)              0           tf.__operators__.add_3[0][0]     \n",
      "                                                                 tf.math.multiply_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.reduce_mean_6 (TFOpLamb ()                   0           tf.__operators__.add_4[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "add_loss (AddLoss)              ()                   0           tf.math.reduce_mean_6[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 4,006,263\n",
      "Trainable params: 4,004,919\n",
      "Non-trainable params: 1,344\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65/65 [==============================] - 8s 68ms/step - loss: 8005.9545\n",
      "Epoch 2/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7536.9300\n",
      "Epoch 3/50\n",
      "65/65 [==============================] - 4s 67ms/step - loss: 7426.0055\n",
      "Epoch 4/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7366.3191\n",
      "Epoch 5/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7226.3800\n",
      "Epoch 6/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7316.1741\n",
      "Epoch 7/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7248.8826\n",
      "Epoch 8/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7314.9281\n",
      "Epoch 9/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7208.9047\n",
      "Epoch 10/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7110.3514\n",
      "Epoch 11/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7283.3101\n",
      "Epoch 12/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7121.3090\n",
      "Epoch 13/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7228.4224\n",
      "Epoch 14/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7121.4047\n",
      "Epoch 15/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7187.0503\n",
      "Epoch 16/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7312.7754\n",
      "Epoch 17/50\n",
      "65/65 [==============================] - 4s 69ms/step - loss: 7070.5891\n",
      "Epoch 18/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7033.9288\n",
      "Epoch 19/50\n",
      "65/65 [==============================] - 5s 75ms/step - loss: 7188.7630\n",
      "Epoch 20/50\n",
      "65/65 [==============================] - 5s 70ms/step - loss: 7290.6376\n",
      "Epoch 21/50\n",
      "65/65 [==============================] - 5s 75ms/step - loss: 7130.6075\n",
      "Epoch 22/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 7203.3686\n",
      "Epoch 23/50\n",
      "65/65 [==============================] - 5s 75ms/step - loss: 7146.6050\n",
      "Epoch 24/50\n",
      "65/65 [==============================] - 5s 73ms/step - loss: 7017.8652\n",
      "Epoch 25/50\n",
      "65/65 [==============================] - 5s 78ms/step - loss: 7106.8645\n",
      "Epoch 26/50\n",
      "65/65 [==============================] - 5s 81ms/step - loss: 7049.5582\n",
      "Epoch 27/50\n",
      "65/65 [==============================] - 5s 77ms/step - loss: 7137.5227\n",
      "Epoch 28/50\n",
      "65/65 [==============================] - 5s 75ms/step - loss: 7212.4039\n",
      "Epoch 29/50\n",
      "65/65 [==============================] - 5s 70ms/step - loss: 7166.1505\n",
      "Epoch 30/50\n",
      "65/65 [==============================] - 5s 70ms/step - loss: 7054.5878\n",
      "Epoch 31/50\n",
      "65/65 [==============================] - 5s 77ms/step - loss: 7043.2081\n",
      "Epoch 32/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7165.7558\n",
      "Epoch 33/50\n",
      "65/65 [==============================] - 4s 65ms/step - loss: 7078.6018\n",
      "Epoch 34/50\n",
      "65/65 [==============================] - 4s 64ms/step - loss: 7193.8797\n",
      "Epoch 35/50\n",
      "65/65 [==============================] - 4s 68ms/step - loss: 7016.1505\n",
      "Epoch 36/50\n",
      "65/65 [==============================] - 5s 85ms/step - loss: 7078.3934\n",
      "Epoch 37/50\n",
      "65/65 [==============================] - 5s 79ms/step - loss: 7079.0337\n",
      "Epoch 38/50\n",
      "65/65 [==============================] - 5s 73ms/step - loss: 7047.6853\n",
      "Epoch 39/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 7048.9301\n",
      "Epoch 40/50\n",
      "65/65 [==============================] - 5s 73ms/step - loss: 7116.9205\n",
      "Epoch 41/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 7015.4815\n",
      "Epoch 42/50\n",
      "65/65 [==============================] - 5s 71ms/step - loss: 6962.5304\n",
      "Epoch 43/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 6987.1895\n",
      "Epoch 44/50\n",
      "65/65 [==============================] - 5s 78ms/step - loss: 7015.4488\n",
      "Epoch 45/50\n",
      "65/65 [==============================] - 5s 75ms/step - loss: 7129.7388\n",
      "Epoch 46/50\n",
      "65/65 [==============================] - 5s 71ms/step - loss: 6935.9163\n",
      "Epoch 47/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 6935.7562\n",
      "Epoch 48/50\n",
      "65/65 [==============================] - 5s 71ms/step - loss: 7085.3036\n",
      "Epoch 49/50\n",
      "65/65 [==============================] - 5s 71ms/step - loss: 7040.0532\n",
      "Epoch 50/50\n",
      "65/65 [==============================] - 5s 72ms/step - loss: 7090.8747\n"
     ]
    }
   ],
   "source": [
    "z = vimcca.fit_integration(\n",
    "    adata_x, adata_y,\n",
    "    sparse_x=False,\n",
    "    sparse_y=False,\n",
    "    hidden_layers=[128, 64, 32, 8],\n",
    "    epochs=50\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_x.obsm['X_vimcca'] = z\n",
    "sc.pp.neighbors(adata_x, use_rep='X_vimcca')\n",
    "sc.tl.leiden(adata_x, resolution=3)\n",
    "sc.tl.umap(adata_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'feature_types' as categorical\n",
      "... storing 'genome' as categorical\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata_x, color='leiden', legend_loc='on data', s=3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test-scbean",
   "language": "python",
   "name": "test-scbean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}