{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a1042cd",
   "metadata": {},
   "source": [
    "# Performance comparison of inference on toy GRN data\n",
    "\n",
    "Notebook compares metrics for velocity, latent time and GRN inference across different methods applied to toy GRN data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a486fec1",
   "metadata": {},
   "source": [
    "## Library imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd119f55-1837-43ca-8368-e12c55652f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mplscience\n",
    "import seaborn as sns\n",
    "\n",
    "from rgv_tools import DATA_DIR, FIG_DIR\n",
    "from rgv_tools.core import METHOD_PALETTE\n",
    "from rgv_tools.plotting._significance import add_significance, get_significance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b6b48f3e-9560-47d4-bd6d-25aa5db08c13",
   "metadata": {},
   "source": [
    "## General settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d83dfd1f-3a26-4e26-a06e-26a975e4a48f",
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_FIGURES = False\n",
    "if SAVE_FIGURES:\n",
    "    (FIG_DIR / \"toy_grn\").mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "FIGURE_FORMATE = \"svg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a6a6ae",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c5f8863",
   "metadata": {},
   "outputs": [],
   "source": [
    "VELOCITY_METHODS = [\"regvelo\", \"velovi\", \"scvelo\"]\n",
    "TIME_METHODS = [\"regvelo\", \"velovi\", \"scvelo\", \"dpt\"]\n",
    "GRN_METHODS = [\"regvelo\", \"correlation\", \"grnboost2\", \"celloracle\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2baf9be8",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a94803b0",
   "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>regvelo_velocity</th>\n",
       "      <th>regvelo_time</th>\n",
       "      <th>regvelo_grn</th>\n",
       "      <th>velovi_velocity</th>\n",
       "      <th>velovi_time</th>\n",
       "      <th>scvelo_velocity</th>\n",
       "      <th>scvelo_time</th>\n",
       "      <th>dpt_time</th>\n",
       "      <th>correlation_grn</th>\n",
       "      <th>grnboost2_grn</th>\n",
       "      <th>celloracle_grn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.555112</td>\n",
       "      <td>0.564590</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.523382</td>\n",
       "      <td>-0.462360</td>\n",
       "      <td>0.461017</td>\n",
       "      <td>0.466543</td>\n",
       "      <td>-0.081997</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.747088</td>\n",
       "      <td>0.861632</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.565394</td>\n",
       "      <td>0.810599</td>\n",
       "      <td>0.621778</td>\n",
       "      <td>0.779928</td>\n",
       "      <td>-0.008727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.747022</td>\n",
       "      <td>0.786513</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.719559</td>\n",
       "      <td>0.613784</td>\n",
       "      <td>0.710150</td>\n",
       "      <td>0.833685</td>\n",
       "      <td>0.012410</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.795365</td>\n",
       "      <td>0.604589</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.706493</td>\n",
       "      <td>0.125379</td>\n",
       "      <td>0.765667</td>\n",
       "      <td>0.678434</td>\n",
       "      <td>0.824321</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.696613</td>\n",
       "      <td>0.914910</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.680337</td>\n",
       "      <td>0.199152</td>\n",
       "      <td>0.646673</td>\n",
       "      <td>0.718848</td>\n",
       "      <td>-0.102730</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   regvelo_velocity  regvelo_time  regvelo_grn  velovi_velocity  velovi_time  \\\n",
       "0          0.555112      0.564590        0.530         0.523382    -0.462360   \n",
       "1          0.747088      0.861632        0.725         0.565394     0.810599   \n",
       "2          0.747022      0.786513        0.550         0.719559     0.613784   \n",
       "3          0.795365      0.604589        0.530         0.706493     0.125379   \n",
       "4          0.696613      0.914910        0.830         0.680337     0.199152   \n",
       "\n",
       "   scvelo_velocity  scvelo_time  dpt_time  correlation_grn  grnboost2_grn  \\\n",
       "0         0.461017     0.466543 -0.081997             0.59          0.475   \n",
       "1         0.621778     0.779928 -0.008727             0.56          0.530   \n",
       "2         0.710150     0.833685  0.012410             0.71          0.565   \n",
       "3         0.765667     0.678434  0.824321             0.62          0.620   \n",
       "4         0.646673     0.718848 -0.102730             0.69          0.550   \n",
       "\n",
       "   celloracle_grn  \n",
       "0           0.470  \n",
       "1           0.555  \n",
       "2           0.520  \n",
       "3           0.500  \n",
       "4           0.595  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_df = []\n",
    "\n",
    "for method in VELOCITY_METHODS:\n",
    "    df = pd.read_parquet(DATA_DIR / \"toy_grn\" / \"results\" / f\"{method}_correlation.parquet\")\n",
    "    df.columns = f\"{method}_\" + df.columns\n",
    "    correlation_df.append(df)\n",
    "del df\n",
    "\n",
    "for method in TIME_METHODS:\n",
    "    if method in VELOCITY_METHODS:\n",
    "        continue\n",
    "    df = pd.read_parquet(DATA_DIR / \"toy_grn\" / \"results\" / f\"{method}_correlation.parquet\")\n",
    "    df.columns = f\"{method}_\" + df.columns\n",
    "    correlation_df.append(df)\n",
    "\n",
    "for method in GRN_METHODS:\n",
    "    if method in VELOCITY_METHODS + TIME_METHODS:\n",
    "        continue\n",
    "    df = pd.read_parquet(DATA_DIR / \"toy_grn\" / \"results\" / f\"{method}_correlation.parquet\")\n",
    "    df.columns = f\"{method}_\" + df.columns\n",
    "    correlation_df.append(df)\n",
    "\n",
    "correlation_df = pd.concat(correlation_df, axis=1)\n",
    "correlation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d13a375",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71a37df",
   "metadata": {},
   "source": [
    "### Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d785c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = correlation_df.loc[:, correlation_df.columns.str.contains(\"velocity\")]\n",
    "df.columns = df.columns.str.removesuffix(\"_velocity\")\n",
    "df = pd.melt(df, var_name=\"method\", value_name=\"correlation\")\n",
    "\n",
    "with mplscience.style_context():\n",
    "    sns.set_style(style=\"whitegrid\")\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    sns.violinplot(\n",
    "        data=df, x=\"correlation\", y=\"method\", hue=\"method\", order=VELOCITY_METHODS, palette=METHOD_PALETTE, ax=ax\n",
    "    )\n",
    "\n",
    "    ttest_res = ttest_ind(\n",
    "        correlation_df[\"regvelo_velocity\"],\n",
    "        correlation_df[\"velovi_velocity\"],\n",
    "        equal_var=False,\n",
    "        alternative=\"greater\",\n",
    "    )\n",
    "    significance = get_significance(pvalue=ttest_res.pvalue)\n",
    "    add_significance(\n",
    "        ax=ax,\n",
    "        left=0,\n",
    "        right=1,\n",
    "        significance=significance,\n",
    "        lw=1,\n",
    "        bracket_level=1.05,\n",
    "        c=\"k\",\n",
    "        level=0,\n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "\n",
    "    ttest_res = ttest_ind(\n",
    "        correlation_df[\"regvelo_velocity\"],\n",
    "        correlation_df[\"scvelo_velocity\"],\n",
    "        equal_var=False,\n",
    "        alternative=\"greater\",\n",
    "    )\n",
    "    significance = get_significance(pvalue=ttest_res.pvalue)\n",
    "    add_significance(\n",
    "        ax=ax,\n",
    "        left=0,\n",
    "        right=2,\n",
    "        significance=significance,\n",
    "        lw=1,\n",
    "        bracket_level=1.05,\n",
    "        c=\"k\",\n",
    "        level=0,\n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "\n",
    "    ax.set(\n",
    "        xlabel=\"Pearson correlation\",\n",
    "        ylabel=\"Method\",\n",
    "        yticks=ax.get_yticks(),\n",
    "        yticklabels=[\"RegVelo\", \"veloVI\", \"scVelo\"],\n",
    "    )\n",
    "\n",
    "    if SAVE_FIGURES:\n",
    "        fig.savefig(\n",
    "            FIG_DIR / \"toy_GRN\" / \"velocity_benchmark.svg\",\n",
    "            format=\"svg\",\n",
    "            transparent=True,\n",
    "            bbox_inches=\"tight\",\n",
    "        )\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2634e5",
   "metadata": {},
   "source": [
    "### Latent time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b80c854a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = correlation_df.loc[:, correlation_df.columns.str.contains(\"time\")]\n",
    "df.columns = df.columns.str.removesuffix(\"_time\")\n",
    "df = pd.melt(df, var_name=\"method\", value_name=\"correlation\")\n",
    "\n",
    "with mplscience.style_context():\n",
    "    sns.set_style(style=\"whitegrid\")\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    sns.violinplot(\n",
    "        data=df, x=\"correlation\", y=\"method\", hue=\"method\", order=TIME_METHODS, palette=METHOD_PALETTE, ax=ax\n",
    "    )\n",
    "\n",
    "    ttest_res = ttest_ind(\n",
    "        correlation_df[\"regvelo_time\"],\n",
    "        correlation_df[\"velovi_time\"],\n",
    "        equal_var=False,\n",
    "        alternative=\"greater\",\n",
    "    )\n",
    "    significance = get_significance(pvalue=ttest_res.pvalue)\n",
    "    add_significance(\n",
    "        ax=ax,\n",
    "        left=0,\n",
    "        right=1,\n",
    "        significance=significance,\n",
    "        lw=1,\n",
    "        bracket_level=1.05,\n",
    "        c=\"k\",\n",
    "        level=0,\n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "\n",
    "    ttest_res = ttest_ind(\n",
    "        correlation_df[\"velovi_time\"],\n",
    "        correlation_df[\"scvelo_time\"],\n",
    "        equal_var=False,\n",
    "        alternative=\"greater\",\n",
    "    )\n",
    "    significance = get_significance(pvalue=ttest_res.pvalue)\n",
    "    add_significance(\n",
    "        ax=ax,\n",
    "        left=0,\n",
    "        right=2,\n",
    "        significance=significance,\n",
    "        lw=1,\n",
    "        bracket_level=1.05,\n",
    "        c=\"k\",\n",
    "        level=0,\n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "\n",
    "    ax.set(\n",
    "        xlabel=\"Spearman correlation\",\n",
    "        ylabel=\"Method\",\n",
    "        yticks=ax.get_yticks(),\n",
    "        yticklabels=[\"RegVelo\", \"veloVI\", \"scVelo\", \"DPT\"],\n",
    "    )\n",
    "\n",
    "    if SAVE_FIGURES:\n",
    "        fig.savefig(\n",
    "            FIG_DIR / \"toy_GRN\" / \"time_benchmark.svg\",\n",
    "            format=\"svg\",\n",
    "            transparent=True,\n",
    "            bbox_inches=\"tight\",\n",
    "        )\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb46384",
   "metadata": {},
   "source": [
    "### GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65cb5576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = correlation_df.loc[:, correlation_df.columns.str.contains(\"grn\")]\n",
    "df.columns = df.columns.str.removesuffix(\"_grn\")\n",
    "df = pd.melt(df, var_name=\"method\").rename(columns={\"value\": \"correlation\"})\n",
    "\n",
    "with mplscience.style_context():\n",
    "    sns.set_style(style=\"whitegrid\")\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    sns.violinplot(data=df, x=\"correlation\", y=\"method\", hue=\"method\", order=GRN_METHODS, palette=METHOD_PALETTE, ax=ax)\n",
    "\n",
    "    ttest_res = ttest_ind(\n",
    "        correlation_df[\"regvelo_grn\"],\n",
    "        correlation_df[\"correlation_grn\"],\n",
    "        equal_var=False,\n",
    "        alternative=\"greater\",\n",
    "    )\n",
    "    significance = get_significance(pvalue=ttest_res.pvalue)\n",
    "    add_significance(\n",
    "        ax=ax,\n",
    "        left=0,\n",
    "        right=1,\n",
    "        significance=significance,\n",
    "        lw=1,\n",
    "        bracket_level=1.05,\n",
    "        c=\"k\",\n",
    "        level=0,\n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "\n",
    "    ax.set(\n",
    "        xlabel=\"AUROC\",\n",
    "        ylabel=\"Method\",\n",
    "        yticks=ax.get_yticks(),\n",
    "        yticklabels=[\"RegVelo\", \"Correlation\", \"GRNBoost2\", \"CellOracle\"],\n",
    "    )\n",
    "\n",
    "    if SAVE_FIGURES:\n",
    "        fig.savefig(\n",
    "            FIG_DIR / \"toy_GRN\" / \"grn_benchmark.svg\",\n",
    "            format=\"svg\",\n",
    "            transparent=True,\n",
    "            bbox_inches=\"tight\",\n",
    "        )\n",
    "\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "regvelo-py310",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}