novel_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of Maize rhizosphere microbiome in novel ecoystems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions to run the prediction:\n",
    "### 1.- Run the first 3 cells in a row (using \"Run\" button on the top menu)\n",
    "### 2.- Then, introduce the values you want for the environmental features (temperature, rain and plant age), using the sliders control\n",
    "### 3.- Move to the next cell, and run the remaining 4-10 cells in a row (\"Run\" button), the same as in point 1, and see the graph with your predicted microbiome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import layers\n",
    "from ipywidgets import interact, widgets\n",
    "import rpy2.rinterface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=4)\n",
    "np.set_printoptions(suppress=True)\n",
    "%reload_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f152e0f438ad41cc8e14d49522b8950e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='\\\\(\\\\textbf{Introduce environmental features values}\\\\)'), Label(value='Please, se…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = widgets.FloatSlider(description='Temperature',min=40,max=100,value=60)\n",
    "b = widgets.FloatSlider(description='Rain (3 days)',min=0,max=5,value=1.5)\n",
    "c = widgets.IntSlider(description='Plant age',min=1,max=20,value=4)\n",
    "t0 = widgets.Label(value=r'\\(\\textbf{'+'Introduce environmental features values'+'}\\)')\n",
    "t1 = widgets.Label(value=\"Please, select the environmental features associated to the maize plant/soil to\") \n",
    "t2 = widgets.Label(value=\"what you would like to predict its rhizosphere microbiome:\")\n",
    "t = widgets.Label(value=\"(Units: temperature in ºF, rain in inches, plant age in weeks)\")\n",
    "ui = widgets.VBox([t0, t1, t2, t, a, b, c])\n",
    "\n",
    "display(ui)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation3Days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X.SampleID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new01</th>\n",
       "      <td>17</td>\n",
       "      <td>75.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  Temperature  Precipitation3Days\n",
       "X.SampleID                                      \n",
       "new01        17         75.6                 2.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=a.value\n",
    "rain=b.value\n",
    "age=c.value\n",
    "metadata = pd.DataFrame({'X.SampleID': ['new01'], 'age': [age], 'Temperature': [temp], 'Precipitation3Days':[rain]}, \n",
    "                  columns=['X.SampleID','age','Temperature','Precipitation3Days'])\n",
    "metadata = metadata.set_index('X.SampleID')\n",
    "domain = metadata[['age',\n",
    "                   'Temperature',\n",
    "                   'Precipitation3Days']]\n",
    "domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_novel_samples = domain.to_numpy(dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = tf.keras.models.load_model('Models/decoder.h5', compile=False)\n",
    "encoder_domain = tf.keras.models.load_model('Models/encoder_domain_model_latent.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute new predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_predicted_otu_table(pred,sample_names,otu_names,suffix=''):\n",
    "    df_otu = pd.DataFrame(pred, index=sample_names, columns=otu_names)\n",
    "    df_otu.T.to_csv('abundances_'+suffix+'.tsv', index=True, header=True, sep='\\t')  \n",
    "    return df_otu\n",
    "\n",
    "# Input only domain (i.e. environmental features)\n",
    "pred_latent_novel_samples = encoder_domain.predict(domain_novel_samples)\n",
    "pred_domain_novel_samples = decoder.predict(pred_latent_novel_samples)\n",
    "\n",
    "# Get otu IDs\n",
    "otu = pd.read_csv('otu_column_otuids.csv', index_col=0, header=None, sep='\\t').T\n",
    "otu = otu.set_index('otuids')\n",
    "otuids = otu.columns\n",
    "\n",
    "df_pred_otu = save_predicted_otu_table(pred_domain_novel_samples,domain.index,otuids,'predicted_otus_novel_features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>4479944</th>\n",
       "      <th>513055</th>\n",
       "      <th>519510</th>\n",
       "      <th>810959</th>\n",
       "      <th>849092</th>\n",
       "      <th>1042671</th>\n",
       "      <th>818174</th>\n",
       "      <th>585221</th>\n",
       "      <th>1033380</th>\n",
       "      <th>875704</th>\n",
       "      <th>...</th>\n",
       "      <th>966091</th>\n",
       "      <th>55354</th>\n",
       "      <th>515709</th>\n",
       "      <th>902698</th>\n",
       "      <th>591499</th>\n",
       "      <th>569066</th>\n",
       "      <th>823091</th>\n",
       "      <th>219094</th>\n",
       "      <th>961783</th>\n",
       "      <th>823633</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X.SampleID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new01</th>\n",
       "      <td>0.000334</td>\n",
       "      <td>8.513715e-07</td>\n",
       "      <td>0.00123</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.00027</td>\n",
       "      <td>2.229224e-07</td>\n",
       "      <td>0.08835</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 717 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0            4479944        513055   519510    810959    849092   1042671  \\\n",
       "X.SampleID                                                                  \n",
       "new01       0.000334  8.513715e-07  0.00123  0.000007  0.003613  0.000003   \n",
       "\n",
       "0             818174    585221   1033380    875704  ...    966091     55354  \\\n",
       "X.SampleID                                          ...                       \n",
       "new01       0.000275  0.000001  0.000001  0.000169  ...  0.000266  0.000136   \n",
       "\n",
       "0             515709    902698    591499    569066   823091        219094  \\\n",
       "X.SampleID                                                                  \n",
       "new01       0.000149  0.001325  0.000249  0.002784  0.00027  2.229224e-07   \n",
       "\n",
       "0            961783    823633  \n",
       "X.SampleID                     \n",
       "new01       0.08835  0.000021  \n",
       "\n",
       "[1 rows x 717 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred_otu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot new predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%Rpush temp rain age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "source('Src/functionsR_plotMicrobiome.r')\n",
    "physeq_pred = build_physeq_object_otu_tax('abundances_predicted_otus_novel_features.tsv','tax_table_cleanNames.csv')\n",
    "plot_taxa_agg(physeq_pred,\"Class\",temp,rain,age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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