examples/Example_embed_shift_template_experiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Template: embed and shift template experiments\n",
    "\n",
    "This template notebook simulates gene expression data by encoding experiments into the latent space and then shifting samples from that experiment in the latent space. Unlike the \"Simulation by random sampling\" approach, this method accounts for experiment level information by shifting samples from the same experiment together. This notebook uses `embed_shift_template_experiment`, described [here](ponyo/simulate_expression_data.py), which inputs a single template experiment, which is an experiment included within the dataset used to train the VAE model. This simulation outputs multiple simulated experiments based on the one template by shifting that template experiment to different locations in the latent space. This simulation generates a collection of experiments of a similar design type. This approach is similar to `shift_template_experiment` however this is using a template experiment that is **not contained within the training dataset**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/alexandra/Documents/Repos/ponyo/ponyo/helper_vae.py:21: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alexandra/Documents/Repos/ponyo/ponyo/helper_vae.py:25: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/alexandra/Documents/Repos/ponyo/ponyo/helper_vae.py:25: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import umap\n",
    "import glob\n",
    "import pickle\n",
    "from keras.models import load_model\n",
    "from sklearn.decomposition import PCA\n",
    "from plotnine import (ggplot,\n",
    "                      labs,  \n",
    "                      geom_point,\n",
    "                      aes, \n",
    "                      ggsave, \n",
    "                      theme_bw,\n",
    "                      theme,\n",
    "                      facet_wrap,\n",
    "                      scale_color_manual,\n",
    "                      guides, \n",
    "                      guide_legend,\n",
    "                      element_blank,\n",
    "                      element_text,\n",
    "                      element_rect,\n",
    "                      element_line,\n",
    "                      coords)\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "from ponyo import utils, train_vae_modules, simulate_expression_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/alexandra/Documents/Repos/ponyo/ponyo/train_vae_modules.py:56: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set seeds to get reproducible VAE trained models\n",
    "train_vae_modules.set_all_seeds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in config variables\n",
    "config_filename = \"config_embed_shift_template_experiment.tsv\"\n",
    "\n",
    "params = utils.read_config(config_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load parameters\n",
    "local_dir = params[\"local_dir\"]\n",
    "raw_compendium_filename = params[\"raw_compendium_filename\"]\n",
    "normalized_compendium_filename = params[\"normalized_compendium_filename\"]\n",
    "raw_template_filename = params[\"raw_template_filename\"]\n",
    "mapped_template_filename = params[\"mapped_template_filename\"]\n",
    "normalized_template_filename = params[\"normalized_template_filename\"]\n",
    "latent_dim = params['latent_dim']\n",
    "training_stats_dir = params[\"training_stats_dir\"]\n",
    "vae_model_dir = params[\"vae_model_dir\"]\n",
    "scaler_transform_filename = params['scaler_transform_filename']\n",
    "project_id = params['project_id']\n",
    "num_simulated = params['num_simulated']\n",
    "simulated_data_dir = params[\"simulated_data_dir\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert os.path.exists(raw_compendium_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.setup_dir(config_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process compendium data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input: dataset contains 273 samples and 5000 genes\n",
      "Output: normalized dataset contains 273 samples and 5000 genes\n"
     ]
    }
   ],
   "source": [
    "train_vae_modules.normalize_expression_data(config_filename,\n",
    "                                            raw_compendium_filename,\n",
    "                                            normalized_compendium_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input dataset contains 273 samples and 5000 genes\n",
      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/test_ponyo/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/test_ponyo/lib/python3.7/site-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/test_ponyo/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 246 samples, validate on 27 samples\n",
      "Epoch 1/10\n",
      "246/246 [==============================] - 20s 82ms/step - loss: 2340.0547 - kl_loss: 13.9695 - recons_loss: 2340.0549 - val_loss: 2576.0480 - val_kl_loss: 138.0642 - val_recons_loss: 2576.0481\n",
      "Epoch 2/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2167.1981 - kl_loss: 13.5955 - recons_loss: 2167.1982 - val_loss: 2336.0543 - val_kl_loss: 77.9998 - val_recons_loss: 2336.0542\n",
      "Epoch 3/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2154.3693 - kl_loss: 13.6479 - recons_loss: 2154.3691 - val_loss: 2209.4096 - val_kl_loss: 48.1001 - val_recons_loss: 2209.4097\n",
      "Epoch 4/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2145.3798 - kl_loss: 14.4935 - recons_loss: 2145.3799 - val_loss: 2148.8787 - val_kl_loss: 27.0799 - val_recons_loss: 2148.8787\n",
      "Epoch 5/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2140.5616 - kl_loss: 14.7198 - recons_loss: 2140.5618 - val_loss: 2125.0939 - val_kl_loss: 29.0089 - val_recons_loss: 2125.0940\n",
      "Epoch 6/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2135.2608 - kl_loss: 14.7896 - recons_loss: 2135.2607 - val_loss: 2106.7844 - val_kl_loss: 32.8426 - val_recons_loss: 2106.7847\n",
      "Epoch 7/10\n",
      "246/246 [==============================] - 20s 81ms/step - loss: 2123.8124 - kl_loss: 15.0116 - recons_loss: 2123.8123 - val_loss: 2084.1353 - val_kl_loss: 29.0683 - val_recons_loss: 2084.1355\n",
      "Epoch 8/10\n",
      "246/246 [==============================] - 20s 82ms/step - loss: 2123.9799 - kl_loss: 15.5945 - recons_loss: 2123.9795 - val_loss: 2089.8382 - val_kl_loss: 27.0921 - val_recons_loss: 2089.8381\n",
      "Epoch 9/10\n",
      "246/246 [==============================] - 21s 84ms/step - loss: 2090.6535 - kl_loss: 16.3353 - recons_loss: 2090.6541 - val_loss: 2126.9455 - val_kl_loss: 36.0171 - val_recons_loss: 2126.9456\n",
      "Epoch 10/10\n",
      "246/246 [==============================] - 21s 84ms/step - loss: 2101.7460 - kl_loss: 16.4985 - recons_loss: 2101.7461 - val_loss: 2079.0770 - val_kl_loss: 29.5822 - val_recons_loss: 2079.0769\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train VAE\n",
    "train_vae_modules.train_vae(config_filename,\n",
    "                            normalized_compendium_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process template experiment\n",
    "\n",
    "In order to simulate a new gene expression experiment, we will need to encode this experiment into the learned latent space. This requires that the feature space (i.e. genes) in the template experiment match the features in the compendium used to train the VAE model. These cells process the template experiment to be of the expected input format:\n",
    "\n",
    "* Template data is expected to be a matrix that is sample x gene\n",
    "* Template experiment is expected to have the same genes as the compendium experiment. Genes that are in the template experiment but not in the compendium are removed. Genes that are in the compendium but missing in the template experiment are added and the gene expression value is set to the median gene expression value of that gene across the samples in the compendium.\n",
    "* Template values are expected to come from the same distribution as the compendium dataset (i.e. both the template and compendium expression measurements are estimated counts). This is necessary since SOPHIE applies the same scale factor used to normalize the compendium to normalize the template experiment. If the template has a different range of expression values, then the scaling will result in outliers (i.e. values greater than 1) which the cross entropy loss in the VAE will not handle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 5000)\n",
      "(273, 5000)\n"
     ]
    }
   ],
   "source": [
    "simulate_expression_data.process_template_experiment(\n",
    "    raw_template_filename,\n",
    "    normalized_compendium_filename,\n",
    "    scaler_transform_filename,\n",
    "    mapped_template_filename,\n",
    "    normalized_template_filename,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 5000)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSG00000000003.14</th>\n",
       "      <th>ENSG00000000005.5</th>\n",
       "      <th>ENSG00000000419.12</th>\n",
       "      <th>ENSG00000000457.13</th>\n",
       "      <th>ENSG00000000460.16</th>\n",
       "      <th>ENSG00000000938.12</th>\n",
       "      <th>ENSG00000000971.15</th>\n",
       "      <th>ENSG00000001036.13</th>\n",
       "      <th>ENSG00000001084.10</th>\n",
       "      <th>ENSG00000001167.14</th>\n",
       "      <th>...</th>\n",
       "      <th>ENSG00000119953.12</th>\n",
       "      <th>ENSG00000119965.12</th>\n",
       "      <th>ENSG00000119969.14</th>\n",
       "      <th>ENSG00000119973.5</th>\n",
       "      <th>ENSG00000119977.20</th>\n",
       "      <th>ENSG00000119979.16</th>\n",
       "      <th>ENSG00000119986.6</th>\n",
       "      <th>ENSG00000120008.15</th>\n",
       "      <th>ENSG00000120029.12</th>\n",
       "      <th>ENSG00000120049.18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR592745</th>\n",
       "      <td>52338</td>\n",
       "      <td>5</td>\n",
       "      <td>449</td>\n",
       "      <td>959</td>\n",
       "      <td>237</td>\n",
       "      <td>5</td>\n",
       "      <td>21814</td>\n",
       "      <td>1784</td>\n",
       "      <td>1228</td>\n",
       "      <td>690</td>\n",
       "      <td>...</td>\n",
       "      <td>587</td>\n",
       "      <td>256</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2602</td>\n",
       "      <td>605</td>\n",
       "      <td>327</td>\n",
       "      <td>2120</td>\n",
       "      <td>1222</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR592746</th>\n",
       "      <td>27719</td>\n",
       "      <td>2</td>\n",
       "      <td>630</td>\n",
       "      <td>429</td>\n",
       "      <td>195</td>\n",
       "      <td>45</td>\n",
       "      <td>14051</td>\n",
       "      <td>832</td>\n",
       "      <td>933</td>\n",
       "      <td>825</td>\n",
       "      <td>...</td>\n",
       "      <td>748</td>\n",
       "      <td>199</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2066</td>\n",
       "      <td>458</td>\n",
       "      <td>635</td>\n",
       "      <td>1331</td>\n",
       "      <td>1236</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR592747</th>\n",
       "      <td>34242</td>\n",
       "      <td>2</td>\n",
       "      <td>1329</td>\n",
       "      <td>487</td>\n",
       "      <td>183</td>\n",
       "      <td>4</td>\n",
       "      <td>17662</td>\n",
       "      <td>1411</td>\n",
       "      <td>1383</td>\n",
       "      <td>1015</td>\n",
       "      <td>...</td>\n",
       "      <td>1320</td>\n",
       "      <td>234</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>2698</td>\n",
       "      <td>568</td>\n",
       "      <td>706</td>\n",
       "      <td>1595</td>\n",
       "      <td>1558</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR592748</th>\n",
       "      <td>11768</td>\n",
       "      <td>88</td>\n",
       "      <td>612</td>\n",
       "      <td>860</td>\n",
       "      <td>370</td>\n",
       "      <td>8</td>\n",
       "      <td>8229</td>\n",
       "      <td>1927</td>\n",
       "      <td>451</td>\n",
       "      <td>1922</td>\n",
       "      <td>...</td>\n",
       "      <td>689</td>\n",
       "      <td>349</td>\n",
       "      <td>503</td>\n",
       "      <td>1</td>\n",
       "      <td>3163</td>\n",
       "      <td>586</td>\n",
       "      <td>113</td>\n",
       "      <td>2903</td>\n",
       "      <td>1099</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR592749</th>\n",
       "      <td>16803</td>\n",
       "      <td>713</td>\n",
       "      <td>855</td>\n",
       "      <td>799</td>\n",
       "      <td>447</td>\n",
       "      <td>4</td>\n",
       "      <td>5676</td>\n",
       "      <td>2099</td>\n",
       "      <td>379</td>\n",
       "      <td>2185</td>\n",
       "      <td>...</td>\n",
       "      <td>864</td>\n",
       "      <td>431</td>\n",
       "      <td>797</td>\n",
       "      <td>0</td>\n",
       "      <td>4314</td>\n",
       "      <td>659</td>\n",
       "      <td>128</td>\n",
       "      <td>2332</td>\n",
       "      <td>971</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ENSG00000000003.14  ENSG00000000005.5  ENSG00000000419.12  \\\n",
       "SRR592745               52338                  5                 449   \n",
       "SRR592746               27719                  2                 630   \n",
       "SRR592747               34242                  2                1329   \n",
       "SRR592748               11768                 88                 612   \n",
       "SRR592749               16803                713                 855   \n",
       "\n",
       "           ENSG00000000457.13  ENSG00000000460.16  ENSG00000000938.12  \\\n",
       "SRR592745                 959                 237                   5   \n",
       "SRR592746                 429                 195                  45   \n",
       "SRR592747                 487                 183                   4   \n",
       "SRR592748                 860                 370                   8   \n",
       "SRR592749                 799                 447                   4   \n",
       "\n",
       "           ENSG00000000971.15  ENSG00000001036.13  ENSG00000001084.10  \\\n",
       "SRR592745               21814                1784                1228   \n",
       "SRR592746               14051                 832                 933   \n",
       "SRR592747               17662                1411                1383   \n",
       "SRR592748                8229                1927                 451   \n",
       "SRR592749                5676                2099                 379   \n",
       "\n",
       "           ENSG00000001167.14  ...  ENSG00000119953.12  ENSG00000119965.12  \\\n",
       "SRR592745                 690  ...                 587                 256   \n",
       "SRR592746                 825  ...                 748                 199   \n",
       "SRR592747                1015  ...                1320                 234   \n",
       "SRR592748                1922  ...                 689                 349   \n",
       "SRR592749                2185  ...                 864                 431   \n",
       "\n",
       "           ENSG00000119969.14  ENSG00000119973.5  ENSG00000119977.20  \\\n",
       "SRR592745                  58                  0                2602   \n",
       "SRR592746                  62                  0                2066   \n",
       "SRR592747                  59                  0                2698   \n",
       "SRR592748                 503                  1                3163   \n",
       "SRR592749                 797                  0                4314   \n",
       "\n",
       "           ENSG00000119979.16  ENSG00000119986.6  ENSG00000120008.15  \\\n",
       "SRR592745                 605                327                2120   \n",
       "SRR592746                 458                635                1331   \n",
       "SRR592747                 568                706                1595   \n",
       "SRR592748                 586                113                2903   \n",
       "SRR592749                 659                128                2332   \n",
       "\n",
       "           ENSG00000120029.12  ENSG00000120049.18  \n",
       "SRR592745                1222                  77  \n",
       "SRR592746                1236                  79  \n",
       "SRR592747                1558                  45  \n",
       "SRR592748                1099                 225  \n",
       "SRR592749                 971                 123  \n",
       "\n",
       "[5 rows x 5000 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template = pd.read_csv(raw_template_filename, sep=\"\\t\", index_col=0, header=0)\n",
    "print(template.shape)\n",
    "template.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Run simulation\n",
    "simulate_expression_data.embed_shift_template_experiment(\n",
    "    normalized_compendium_filename,\n",
    "    normalized_template_filename,\n",
    "    vae_model_dir,\n",
    "    project_id,\n",
    "    scaler_transform_filename,\n",
    "    local_dir,\n",
    "    latent_dim,\n",
    "    num_simulated,\n",
    "    simulated_data_dir,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize latent transform compendium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load VAE models\n",
    "model_encoder_filename = glob.glob(os.path.join(\n",
    "        vae_model_dir,\n",
    "        \"*_encoder_model.h5\"))[0]\n",
    "\n",
    "weights_encoder_filename = glob.glob(os.path.join(\n",
    "    vae_model_dir,\n",
    "    \"*_encoder_weights.h5\"))[0]\n",
    "\n",
    "model_decoder_filename = glob.glob(os.path.join(\n",
    "    vae_model_dir,\n",
    "    \"*_decoder_model.h5\"))[0]\n",
    "\n",
    "weights_decoder_filename = glob.glob(os.path.join(\n",
    "    vae_model_dir,\n",
    "    \"*_decoder_weights.h5\"))[0]\n",
    "\n",
    "# Load saved models\n",
    "loaded_model = load_model(model_encoder_filename)\n",
    "loaded_decode_model = load_model(model_decoder_filename)\n",
    "\n",
    "loaded_model.load_weights(weights_encoder_filename)\n",
    "loaded_decode_model.load_weights(weights_decoder_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "normalized_compendium = pd.read_csv(normalized_compendium_filename, header=0, sep=\"\\t\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode normalized compendium into latent space\n",
    "compendium_encoded = loaded_model.predict_on_batch(normalized_compendium)\n",
    "\n",
    "compendium_encoded_df = pd.DataFrame(data=compendium_encoded, \n",
    "                                     index=normalized_compendium.index)\n",
    "\n",
    "# Get and save PCA model\n",
    "model = pca.fit(compendium_encoded_df)\n",
    "\n",
    "compendium_PCAencoded = model.transform(compendium_encoded_df)\n",
    "\n",
    "compendium_PCAencoded_df = pd.DataFrame(data=compendium_PCAencoded,\n",
    "                                         index=compendium_encoded_df.index,\n",
    "                                         columns=['1','2'])\n",
    "\n",
    "# Add label\n",
    "compendium_PCAencoded_df['experiment_id'] = 'background compendium'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embedding of real template experiment (encoded)\n",
    "template_filename = os.path.join(local_dir,\n",
    "    \"template_normalized_data_\"+project_id+\"_test.tsv\")\n",
    "\n",
    "template_data = pd.read_csv(template_filename, header=0, sep='\\t', index_col=0)\n",
    "\n",
    "# Encode template experiment into latent space\n",
    "template_encoded = loaded_model.predict_on_batch(template_data)\n",
    "template_encoded_df = pd.DataFrame(data=template_encoded,\n",
    "                                   index=template_data.index)\n",
    "\n",
    "template_PCAencoded = model.transform(template_encoded_df)\n",
    "\n",
    "template_PCAencoded_df = pd.DataFrame(data=template_PCAencoded,\n",
    "                                         index=template_encoded_df.index,\n",
    "                                         columns=['1','2'])\n",
    "\n",
    "# Add back label column\n",
    "template_PCAencoded_df['experiment_id'] = 'template_experiment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embedding of simulated experiment (encoded)\n",
    "encoded_simulated_filename = os.path.join(simulated_data_dir,\n",
    "    \"selected_simulated_encoded_data_\"+project_id+\"_1.tsv\")\n",
    "\n",
    "simulated_encoded_df = pd.read_csv(encoded_simulated_filename,header=0, sep='\\t', index_col=0)\n",
    "\n",
    "simulated_PCAencoded = model.transform(simulated_encoded_df)\n",
    "\n",
    "simulated_PCAencoded_df = pd.DataFrame(data=simulated_PCAencoded,\n",
    "                                         index=simulated_encoded_df.index,\n",
    "                                         columns=['1','2'])\n",
    "\n",
    "# Add back label column\n",
    "simulated_PCAencoded_df['experiment_id'] = 'simulated_experiment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(283, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>experiment_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SRR493816</th>\n",
       "      <td>1.848396</td>\n",
       "      <td>1.286768</td>\n",
       "      <td>background compendium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR493817</th>\n",
       "      <td>1.990783</td>\n",
       "      <td>0.579773</td>\n",
       "      <td>background compendium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR547975</th>\n",
       "      <td>2.150964</td>\n",
       "      <td>3.926265</td>\n",
       "      <td>background compendium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR547973</th>\n",
       "      <td>2.045691</td>\n",
       "      <td>1.005350</td>\n",
       "      <td>background compendium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR547968</th>\n",
       "      <td>2.227068</td>\n",
       "      <td>1.426534</td>\n",
       "      <td>background compendium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1         2          experiment_id\n",
       "SRR493816  1.848396  1.286768  background compendium\n",
       "SRR493817  1.990783  0.579773  background compendium\n",
       "SRR547975  2.150964  3.926265  background compendium\n",
       "SRR547973  2.045691  1.005350  background compendium\n",
       "SRR547968  2.227068  1.426534  background compendium"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate dataframes\n",
    "combined_PCAencoded_df = pd.concat([compendium_PCAencoded_df, \n",
    "                                    template_PCAencoded_df,\n",
    "                                    simulated_PCAencoded_df])\n",
    "\n",
    "print(combined_PCAencoded_df.shape)\n",
    "combined_PCAencoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (8749951611381)>\n"
     ]
    }
   ],
   "source": [
    "# Plot\n",
    "fig = ggplot(combined_PCAencoded_df, aes(x='1', y='2'))\n",
    "fig += geom_point(aes(color='experiment_id'), alpha=0.2)\n",
    "fig += labs(x ='PCA 1',\n",
    "            y = 'PCA 2',\n",
    "            title = 'PCA original data with experiments (latent space)')\n",
    "fig += theme_bw()\n",
    "fig += theme(\n",
    "    legend_title_align = \"center\",\n",
    "    plot_background=element_rect(fill='white'),\n",
    "    legend_key=element_rect(fill='white', colour='white'), \n",
    "    legend_title=element_text(family='sans-serif', size=15),\n",
    "    legend_text=element_text(family='sans-serif', size=12),\n",
    "    plot_title=element_text(family='sans-serif', size=15),\n",
    "    axis_text=element_text(family='sans-serif', size=12),\n",
    "    axis_title=element_text(family='sans-serif', size=15)\n",
    "    )\n",
    "fig += guides(colour=guide_legend(override_aes={'alpha': 1}))\n",
    "fig += scale_color_manual(['#bdbdbd', 'red', 'blue'])\n",
    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'template_experiment'],\n",
    "                  alpha=0.2, \n",
    "                  color='blue')\n",
    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'simulated_experiment'],\n",
    "                  alpha=0.1, \n",
    "                  color='red')\n",
    "\n",
    "print(fig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}