examples/null_size.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining null size for mAP p-value calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.special import comb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from copairs import map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_grid(\n",
    "    data,\n",
    "    null_size_col=\"null_size\",\n",
    "    x_col=\"mAP\",\n",
    "    y_col=\"-log10(p-value)\",\n",
    "    color_col=\"below_corrected_p\",\n",
    "    cmap=\"tab10\",\n",
    "    figsize=(12, 6),\n",
    "):\n",
    "    \"\"\"Plot a grid of scatter plots for different values of a given column.\n",
    "\n",
    "    Args:\n",
    "        data (pd.DataFrame): Input DataFrame containing the data.\n",
    "        null_size_col (str): Column to split data into subplots. Defaults to \"null_size\".\n",
    "        x_col (str): Column to use for the x-axis. Defaults to \"mean_average_precision\".\n",
    "        y_col (str): Column to use for the y-axis. Defaults to \"-log10(p-value)\".\n",
    "        color_col (str): Column for coloring points. Defaults to \"below_corrected_p\".\n",
    "        cmap (str): Colormap for the scatter plot. Defaults to \"tab10\".\n",
    "        figsize (tuple): Figure size. Defaults to (12, 6).\n",
    "    \"\"\"\n",
    "    unique_null_sizes = sorted(data[null_size_col].unique())  # Get unique values\n",
    "    n_rows, n_cols = 3, 4  # Define grid shape\n",
    "\n",
    "    fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize, sharex=True, sharey=True)\n",
    "    axes = axes.flatten()  # Flatten for easy iteration\n",
    "\n",
    "    for i, null_size in enumerate(unique_null_sizes):\n",
    "        ax = axes[i]\n",
    "        subset = data[data[null_size_col] == null_size]  # Filter data for current panel\n",
    "\n",
    "        # Compute active ratio for the subset\n",
    "        active_ratio = subset[color_col].mean()\n",
    "\n",
    "        # Scatter plot\n",
    "        _ = ax.scatter(\n",
    "            subset[x_col], subset[y_col], c=subset[color_col], cmap=cmap, s=10\n",
    "        )\n",
    "\n",
    "        ax.axhline(\n",
    "            -np.log10(0.05), color=\"black\", linestyle=\"--\"\n",
    "        )  # Significance threshold\n",
    "        ax.set_title(f\"{null_size_col} = {null_size}\")\n",
    "\n",
    "        # Display active ratio per panel\n",
    "        ax.text(\n",
    "            0.4,\n",
    "            5,\n",
    "            f\"Active = {100 * active_ratio:.2f}%\",\n",
    "            va=\"center\",\n",
    "            ha=\"left\",\n",
    "            fontsize=9,\n",
    "        )\n",
    "\n",
    "        if i % n_cols == 0:  # Leftmost column\n",
    "            ax.set_ylabel(y_col)\n",
    "        if i >= (n_rows - 1) * n_cols:  # Bottom row\n",
    "            ax.set_xlabel(x_col)\n",
    "\n",
    "    fig.suptitle(f\"Scatter plots across different {null_size_col} values\", fontsize=14)\n",
    "    plt.tight_layout(rect=[0, 0.05, 1, 0.95])  # Adjust layout\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "This example relies on data and results from the [Phenotypic activity](./phenotypic_activity.ipynb) example, so run that one first if you haven't.\n",
    "\n",
    "Let's define some helper functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_activity = pd.read_csv(\"data/2016_04_01_a549_48hr_batch1_plateSQ00014812.csv\")\n",
    "activity_ap = pd.read_csv(\"data/activity_ap.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete null size\n",
    "\n",
    "We estimate the statistical significance of a mAP score with respect to a random baseline using a permutation testing approach, a non-parametric, assumption-free method for testing the null hypothesis of sample exchangeability. The complete AP null distribution consists of all possible rank list re-shuffles.\n",
    "\n",
    "Let $m$ to be the number of perturbation replicates and $n$ to be the number of control profiles. Given that one perturbation profile serves as a query, the complete null size $d$ can be calculated as a binomial coefficient:\n",
    "\n",
    "\\begin{equation}\n",
    "    d_{null} = \\binom{(m-1)}{(m-1)*n}\n",
    "\\end{equation}\n",
    "\n",
    "Let's calculate the complete null size for the example dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m=6, n=24, d=118755\n"
     ]
    }
   ],
   "source": [
    "# almost all perturbations have 6 replicates\n",
    "m = (\n",
    "    df_activity.query(\"Metadata_broad_sample != 'DMSO'\")\n",
    "    .groupby(\"Metadata_broad_sample\")\n",
    "    .size()\n",
    "    .mode()[0]\n",
    ")\n",
    "# the number of control profiles is 24\n",
    "n = df_activity.query(\"Metadata_broad_sample == 'DMSO'\").shape[0]\n",
    "\n",
    "# using SciPy's comb function for numerical stability\n",
    "d = comb(m - 1 + n, m - 1, exact=True)\n",
    "\n",
    "print(f\"{m=}, {n=}, {d=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For large datasets, computing the full combinatorial null is infeasible. Instead, we approximate the null distribution using Monte Carlo sampling with $d_{\\text{perm}}$ permutations:\n",
    "\n",
    "\\begin{equation}\n",
    "    null\\_size \\approx d_{null}\n",
    "\\end{equation}\n",
    "\n",
    "where $null\\_size$ is the number of random rank list shufflings applied to estimate the null distribution.\n",
    "\n",
    "## Effect of null size on mAP p-value calculation\n",
    "\n",
    "Let's calculate mAP significance on the given dataset using `null_size` values from $10$ to $5*10^6$ and plot results below."
   ]
  },
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",
      "text/plain": [
       "<Figure size 1200x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activity_maps = []\n",
    "for ns_pow in range(1, 7):\n",
    "    null_size = 10**ns_pow\n",
    "\n",
    "    replicate_map = map.mean_average_precision(\n",
    "        activity_ap,\n",
    "        [\"Metadata_broad_sample\"],\n",
    "        null_size=null_size,\n",
    "        threshold=0.05,\n",
    "        seed=0,\n",
    "    )\n",
    "    replicate_map[\"null_size\"] = null_size\n",
    "    activity_maps.append(replicate_map)\n",
    "\n",
    "    replicate_map = map.mean_average_precision(\n",
    "        activity_ap,\n",
    "        [\"Metadata_broad_sample\"],\n",
    "        null_size=5 * null_size,\n",
    "        threshold=0.05,\n",
    "        seed=0,\n",
    "    )\n",
    "    replicate_map[\"null_size\"] = 5 * null_size\n",
    "    activity_maps.append(replicate_map)\n",
    "\n",
    "activity_maps = pd.concat(activity_maps)\n",
    "activity_maps.rename(columns={\"mean_average_precision\": \"mAP\"}, inplace=True)\n",
    "activity_maps[\"-log10(p-value)\"] = -activity_maps[\"corrected_p_value\"].apply(np.log10)\n",
    "\n",
    "plot_scatter_grid(activity_maps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the full null size $d_{null}=118755$, smaller sample sizes ($<5,000$) lead to poor estimation of significance for these data, while very large values ($>100,000$) cover the whole null and do not affect perturbation ranking results.\n",
    "\n",
    "## Practical consideration for choosing null size\n",
    "\n",
    "In practice, drawing a large number of samples is not always feasible, because compute time for each AP calculation grows with the higher number of perturbations of the dataset, the number of metadata constraints for profile grouping, sizes of perturbation groups (the number of perturbation replicates) and control groups (the number of control replicates), and profile dimensionality (the number of features in a profile).\n",
    "\n",
    "Finding a `null_size` that works for a particular dataset is balancing between test resolution (for example, being able to tell apart vary small p-values) and compute. We provided `null_size` values for each real-world dataset in Supplemental Materials to our paper—please refer to:\n",
    "\n",
    "> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "copairs",
   "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": "3.9.19"
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