{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2089cc5b-a025-4928-a331-ad33fd1b6a85",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lucascamillomd/pyaging/blob/main/tutorials/tutorial_rnaseq.ipynb) [![Open In nbviewer](https://img.shields.io/badge/View%20in-nbviewer-orange)](https://nbviewer.jupyter.org/github/lucascamillomd/pyaging/blob/main/tutorials/tutorial_rnaseq.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cf37ce-09ee-49d7-a411-719bf65e186e",
   "metadata": {},
   "source": [
    "# Blood chemistry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea2b570-56af-4e4f-9606-d4c6d071554c",
   "metadata": {},
   "source": [
    "This tutorial is a brief guide for the implementation of PhenoAge. Link to [paper](https://www.aging-us.com/article/101414/text)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a093c7d-dea7-4b34-91bf-08cde6c98011",
   "metadata": {},
   "source": [
    "We just need two packages for this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad192191-e44f-4994-80ad-ab16cdb7c7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import pyaging as pya"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87488d5-731c-469e-ad6f-79c4c9662371",
   "metadata": {},
   "source": [
    "## Download and load example data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c30471f-89e7-4e92-a176-aa3af14a5274",
   "metadata": {},
   "source": [
    "Let's download some example human blood data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0692cf7-e979-4f27-bc14-e1013057c16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-----> 🏗️ Starting download_example_data function\n",
      "|-----------> Data found in pyaging_data/blood_chemistry_example.pkl\n",
      "|-----> 🎉 Done! [0.0014s]\n"
     ]
    }
   ],
   "source": [
    "pya.data.download_example_data('blood_chemistry_example')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13aeb69a-4b0e-40f2-8094-194c9a6b42a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('pyaging_data/blood_chemistry_example.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0106112d-21ad-4991-af9f-74b92f46c55b",
   "metadata": {},
   "outputs": [
    {
     "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>albumin</th>\n",
       "      <th>creatinine</th>\n",
       "      <th>glucose</th>\n",
       "      <th>log_crp</th>\n",
       "      <th>lymphocyte_percent</th>\n",
       "      <th>mean_cell_volume</th>\n",
       "      <th>red_cell_distribution_width</th>\n",
       "      <th>alkaline_phosphatase</th>\n",
       "      <th>white_blood_cell_count</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient1</th>\n",
       "      <td>51.8</td>\n",
       "      <td>87.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>27.9</td>\n",
       "      <td>92.4</td>\n",
       "      <td>13.9</td>\n",
       "      <td>123.5</td>\n",
       "      <td>0.006037</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient2</th>\n",
       "      <td>53.1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>80.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>0.004135</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient3</th>\n",
       "      <td>37.4</td>\n",
       "      <td>114.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>83.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>124.4</td>\n",
       "      <td>0.007382</td>\n",
       "      <td>66.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient4</th>\n",
       "      <td>45.9</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>38.6</td>\n",
       "      <td>92.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>113.4</td>\n",
       "      <td>0.006537</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient5</th>\n",
       "      <td>40.7</td>\n",
       "      <td>45.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>38.3</td>\n",
       "      <td>88.8</td>\n",
       "      <td>13.5</td>\n",
       "      <td>107.8</td>\n",
       "      <td>0.004695</td>\n",
       "      <td>42.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          albumin  creatinine  glucose  log_crp  lymphocyte_percent  \\\n",
       "patient1     51.8        87.2      4.5     -0.2                27.9   \n",
       "patient2     53.1        57.3      6.1     -0.2                27.8   \n",
       "patient3     37.4       114.7      5.6     -0.2                23.6   \n",
       "patient4     45.9        88.1      5.4     -0.2                38.6   \n",
       "patient5     40.7        45.4      4.7     -0.2                38.3   \n",
       "\n",
       "          mean_cell_volume  red_cell_distribution_width  alkaline_phosphatase  \\\n",
       "patient1              92.4                         13.9                 123.5   \n",
       "patient2              80.9                         12.0                  81.5   \n",
       "patient3              83.2                         12.4                 124.4   \n",
       "patient4              92.5                         11.4                 113.4   \n",
       "patient5              88.8                         13.5                 107.8   \n",
       "\n",
       "          white_blood_cell_count   age  \n",
       "patient1                0.006037  70.2  \n",
       "patient2                0.004135  76.5  \n",
       "patient3                0.007382  66.4  \n",
       "patient4                0.006537  46.5  \n",
       "patient5                0.004695  42.3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cbc6e1-9cf7-46a8-ac92-18924a7a5cf8",
   "metadata": {},
   "source": [
    "## Convert data to AnnData object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae486006-b533-411b-b449-ff6d2261345a",
   "metadata": {},
   "source": [
    "AnnData objects are highly flexible and are thus our preferred method of organizing data for age prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acf93ebe-0440-4b1f-9040-05260df459f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-----> 🏗️ Starting df_to_adata function\n",
      "|-----> ⚙️ Create anndata object started\n",
      "|-----> ✅ Create anndata object finished [0.0036s]\n",
      "|-----> ⚙️ Add metadata to anndata started\n",
      "|-----------? No metadata provided. Leaving adata.obs empty\n",
      "|-----> ⚠️ Add metadata to anndata finished [0.0005s]\n",
      "|-----> ⚙️ Log data statistics started\n",
      "|-----------> There are 30 observations\n",
      "|-----------> There are 10 features\n",
      "|-----------> Total missing values: 0\n",
      "|-----------> Percentage of missing values: 0.00%\n",
      "|-----> ✅ Log data statistics finished [0.0010s]\n",
      "|-----> ⚙️ Impute missing values started\n",
      "|-----------> No missing values found. No imputation necessary\n",
      "|-----> ✅ Impute missing values finished [0.0008s]\n",
      "|-----> 🎉 Done! [0.0089s]\n"
     ]
    }
   ],
   "source": [
    "adata = pya.preprocess.df_to_adata(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54dcb802-6dd7-40cc-ab61-073f70778754",
   "metadata": {},
   "source": [
    "Note that the original DataFrame is stored in `X_original` under layers. is This is what the `adata` object looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cfcf1f4-01d8-4da2-81e9-fee50e051ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 30 × 10\n",
       "    var: 'percent_na'\n",
       "    layers: 'X_original'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2277ede6-ab9e-487b-a58d-c01cb21b6b68",
   "metadata": {},
   "source": [
    "## Predict age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889d2d5f-a596-41d0-b849-560b6bc856a1",
   "metadata": {},
   "source": [
    "We can either predict one clock at once or all at the same time. Given we only have one clock of interest for this tutorial, let's go with one. The function is invariant to the capitalization of the clock name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dbc7beb-79b8-4e99-b36f-36bcd693c864",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-----> 🏗️ Starting predict_age function\n",
      "|-----> ⚙️ Set PyTorch device started\n",
      "|-----------> Using device: cpu\n",
      "|-----> ✅ Set PyTorch device finished [0.0008s]\n",
      "|-----> 🕒 Processing clock: phenoage\n",
      "|-----------> ⚙️ Load clock started\n",
      "|-----------------> Data found in pyaging_data/phenoage.pt\n",
      "|-----------> ✅ Load clock finished [0.0148s]\n",
      "|-----------> ⚙️ Check features in adata started\n",
      "|-----------------> All features are present in adata.var_names.\n",
      "|-----------> ✅ Check features in adata finished [0.0006s]\n",
      "|-----------> ⚙️ Predict ages with model started\n",
      "|-----------------> There is no preprocessing necessary\n",
      "|-----------------> The postprocessing method is mortality_to_phenoage\n",
      "|-----------------> in progress: 100.0000%\n",
      "|-----------> ✅ Predict ages with model finished [0.0345s]\n",
      "|-----------> ⚙️ Add predicted ages and clock metadata to adata started\n",
      "|-----------> ✅ Add predicted ages and clock metadata to adata finished [0.0006s]\n",
      "|-----> 🎉 Done! [0.1068s]\n"
     ]
    }
   ],
   "source": [
    "pya.pred.predict_age(adata, 'PhenoAge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "032382f5-7d98-465e-a3cb-51165eeb7025",
   "metadata": {},
   "outputs": [
    {
     "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>phenoage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient1</th>\n",
       "      <td>70.643137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient2</th>\n",
       "      <td>64.834061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient3</th>\n",
       "      <td>70.258559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient4</th>\n",
       "      <td>42.979385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient5</th>\n",
       "      <td>41.677749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           phenoage\n",
       "patient1  70.643137\n",
       "patient2  64.834061\n",
       "patient3  70.258559\n",
       "patient4  42.979385\n",
       "patient5  41.677749"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acc80b1-f936-40e4-900a-ef4deb304558",
   "metadata": {},
   "source": [
    "Having so much information printed can be overwhelming, particularly when running several clocks at once. In such cases, just set verbose to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a587f129-a88b-46ec-a249-ac62737a0cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pya.data.download_example_data('blood_chemistry_example', verbose=False)\n",
    "df = pd.read_pickle('pyaging_data/blood_chemistry_example.pkl')\n",
    "adata = pya.preprocess.df_to_adata(df, verbose=False)\n",
    "pya.pred.predict_age(adata, ['PhenoAge'], verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99fbe406-d076-4979-a2f4-70469755937f",
   "metadata": {},
   "outputs": [
    {
     "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>phenoage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient1</th>\n",
       "      <td>70.643137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient2</th>\n",
       "      <td>64.834061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient3</th>\n",
       "      <td>70.258559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient4</th>\n",
       "      <td>42.979385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient5</th>\n",
       "      <td>41.677749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           phenoage\n",
       "patient1  70.643137\n",
       "patient2  64.834061\n",
       "patient3  70.258559\n",
       "patient4  42.979385\n",
       "patient5  41.677749"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f0eb22-76f2-41b5-b20f-824548215122",
   "metadata": {},
   "source": [
    "After age prediction, the clocks are added to `adata.obs`. Moreover, the percent of missing values for each clock and other metadata are included in `adata.uns`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a778028a-7ee6-419c-9be6-e7046a9d8f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 30 × 10\n",
       "    obs: 'phenoage'\n",
       "    var: 'percent_na'\n",
       "    uns: 'phenoage_percent_na', 'phenoage_missing_features', 'phenoage_metadata'\n",
       "    layers: 'X_original'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a73e164-a610-4cb6-93f5-6f8ac7d8d56f",
   "metadata": {},
   "source": [
    "## Get citation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7a070c-c448-4ad7-ae0b-21857dafd00e",
   "metadata": {},
   "source": [
    "The doi, citation, and some metadata are automatically added to the AnnData object under `adata.uns[CLOCKNAME_metadata]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9908d25a-9639-4684-9da6-353c7eb4a555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clock_name': 'phenoage',\n",
       " 'data_type': 'blood chemistry',\n",
       " 'species': 'Homo sapiens',\n",
       " 'year': 2018,\n",
       " 'approved_by_author': '⌛',\n",
       " 'citation': 'Levine, Morgan E., et al. \"An epigenetic biomarker of aging for lifespan and healthspan.\" Aging (albany NY) 10.4 (2018): 573.',\n",
       " 'doi': 'https://doi.org/10.18632%2Faging.101414',\n",
       " 'notes': None,\n",
       " 'research_only': None,\n",
       " 'version': None}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.uns['phenoage_metadata']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "research",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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 "nbformat": 4,
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