single_cell_analysis.ipynb

{
 "cells": [
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   "metadata": {
    "id": "ca89c69d-9fec-47ac-b259-9b02df39b4b6"
   },
   "source": [
    "# NMA CN Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf2a32b-3365-4ba6-ac6a-e5b05c843c36",
   "metadata": {
    "id": "7bf2a32b-3365-4ba6-ac6a-e5b05c843c36"
   },
   "source": [
    "- **Pod**: careful wolfs ⚠️🐺\n",
    "- **Question**: is there a difference in neural activation at the cell level following stimulus presentation in the active vs passive conditions?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e20e4d4-3b15-4846-a491-23b83e744913",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b939b49e-a136-4635-8e9f-683ddcc27a0f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "b939b49e-a136-4635-8e9f-683ddcc27a0f",
    "outputId": "da88b801-9878-48ca-c83c-b7ebe296390f"
   },
   "outputs": [],
   "source": [
    "# Only for Colab\n",
    "# !python -m pip install --upgrade pip\n",
    "# !pip install allensdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f54b67f6-4d74-4581-bd9e-87f6ca1b65e5",
   "metadata": {
    "id": "f54b67f6-4d74-4581-bd9e-87f6ca1b65e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/seungjaeryanlee/anaconda3/envs/nma-cn/lib/python3.8/site-packages/allensdk/brain_observatory/session_api_utils.py:15: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from allensdk.brain_observatory.behavior.behavior_project_cache import VisualBehaviorOphysProjectCache"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4365eb5-3efb-47a3-ae4c-219fbbc0c93f",
   "metadata": {
    "id": "f4365eb5-3efb-47a3-ae4c-219fbbc0c93f",
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Setup AllenSDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "384272fc-6e1a-48fc-95c3-b8e8c82bb0ce",
   "metadata": {
    "id": "384272fc-6e1a-48fc-95c3-b8e8c82bb0ce"
   },
   "outputs": [],
   "source": [
    "!mkdir -p allensdk_storage\n",
    "DATA_STORAGE_DIRECTORY = Path(\"./allensdk_storage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e4dec19-a09f-42a9-acca-83783f51484e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e4dec19-a09f-42a9-acca-83783f51484e",
    "outputId": "d93b1b9a-10a2-40d2-ccba-8e45a583d078"
   },
   "outputs": [],
   "source": [
    "cache = VisualBehaviorOphysProjectCache.from_s3_cache(cache_dir=DATA_STORAGE_DIRECTORY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a278be53-4b29-42e6-9acd-564705fb6c5f",
   "metadata": {
    "id": "a278be53-4b29-42e6-9acd-564705fb6c5f"
   },
   "outputs": [],
   "source": [
    "experiments_df = cache.get_ophys_experiment_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a560169c-c2f1-4b54-9ac2-2487df23c52d",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## Filter `experiments_df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5667f215-6abe-494a-9f81-f7a8d479bf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_CODE = \"VisualBehavior\"\n",
    "FULL_GENOTYPE = \"Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69ba0d01-e3d4-424a-b8d1-3c4673327699",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_experiments_df = experiments_df.query(\"project_code == @PROJECT_CODE\") \\\n",
    "                                        .query(\"full_genotype == @FULL_GENOTYPE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e617b8e4-6762-476e-8a1c-38c8fa439147",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## General Helper Functions\n",
    "- `get_cell_specimen_ids(experiment)`\n",
    "- `get_indices_in_interval(experiment, start, end)`\n",
    "- `get_timestamps_in_interval(experiment, start, end)`\n",
    "- `get_cell_dff_in_interval(experiment, cell_specimen_id, start, end)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3643cf5-a362-4fd8-a7a7-0c38607d48c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cell_specimen_ids(experiment):\n",
    "    \"\"\"\n",
    "    Get all the cell specimen ids recorded in the experiment.\n",
    "    \"\"\"\n",
    "    return experiment.dff_traces.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b59d3c3-e981-4d92-bd5e-4d067a71bb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_indices_in_interval(experiment, start, end):\n",
    "    \"\"\"\n",
    "    Get all indices of timestamps where dF/F trace was recorded during a specified interval.\n",
    "    \"\"\"\n",
    "    indices = np.argwhere((experiment.ophys_timestamps >= start) & (experiment.ophys_timestamps <= end))[:, 0]\n",
    "    return indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46da2011-055f-4287-b45f-47123d96a2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_timestamps_in_interval(experiment, start, end):\n",
    "    \"\"\"\n",
    "    Get all timestamps when dF/F trace was recorded during a specified interval.\n",
    "    \"\"\"\n",
    "    indices = get_indices_in_interval(experiment, start, end)\n",
    "    return experiment.ophys_timestamps[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98015189-a555-43a5-97d7-36beb1e91988",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cell_dff_in_interval(experiment, cell_specimen_id, start, end):\n",
    "    \"\"\"\n",
    "    Get dF/F trace for one cell during a specified interval.\n",
    "    \"\"\"\n",
    "    indices = get_indices_in_interval(experiment, start, end)\n",
    "    return experiment.dff_traces.loc[cell_specimen_id][\"dff\"][indices[0]:indices[-1]+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc184d6-ac4a-459d-9c84-6538d5d6664c",
   "metadata": {
    "id": "adc184d6-ac4a-459d-9c84-6538d5d6664c",
    "tags": []
   },
   "source": [
    "## Cell Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d4a490-05fb-42f9-9d65-2a07a4e9c03c",
   "metadata": {
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    "toc-hr-collapsed": true
   },
   "source": [
    "### Cell Selection Helper Functions\n",
    "- `get_image_names(experiment)`\n",
    "- `get_image_start_times(experiment, image_name)`\n",
    "- `score_and_sort_cells(experiment, image_name, t_before, t_after, n_before, n_after)`\n",
    "- `plot_score_histogram(scores)`\n",
    "- `plot_dff(experiment, image_name, selected_cell_specimen_ids, t_before, t_after, n_before, n_after)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe8ddd5d-f8d1-4f78-8a02-8528db257236",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_names(experiment):\n",
    "    return experiment.stimulus_presentations[\"image_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e15a3669-b325-4db7-9d70-320510d70da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_start_times(experiment, image_name):\n",
    "    return active_experiment.stimulus_presentations.query(\"image_name == @image_name\")[\"start_time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1def93d4-1dee-4645-b90d-1779034c37d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_and_sort_cells(experiment, image_name, t_before, t_after, n_before, n_after):\n",
    "    cell_specimen_ids = get_cell_specimen_ids(active_experiment)\n",
    "    dff_matrix = np.vstack(experiment.dff_traces.dff.values)\n",
    "    stimulus_for_image_start_times = get_image_start_times(experiment, image_name)\n",
    "\n",
    "    # Compute score for each cell\n",
    "    zscored_for_image_lst = []\n",
    "    for stimulus_start_time in tqdm(stimulus_for_image_start_times):\n",
    "        before_indices = get_indices_in_interval(\n",
    "            active_experiment,\n",
    "            start=stimulus_start_time - t_before,\n",
    "            end=stimulus_start_time,\n",
    "        )[-n_before:]\n",
    "        after_indices = get_indices_in_interval(\n",
    "            active_experiment,\n",
    "            start=stimulus_start_time,\n",
    "            end=stimulus_start_time + t_after,\n",
    "        )[:n_after]\n",
    "\n",
    "        before_mean_dff = np.mean(dff_matrix[:, before_indices], axis=1)\n",
    "        before_std_dff = np.std(dff_matrix[:, before_indices], axis=1)\n",
    "\n",
    "        # zscored = ( after stimulus onset dff - mean(before stimulus onset dff) ) / std(before stimulus onset dff)\n",
    "        after_zscored = (dff_matrix[:, after_indices] - np.tile(before_mean_dff, (len(after_indices), 1)).T) / np.tile(before_std_dff, (len(after_indices), 1)).T\n",
    "        zscored_for_image_lst.append(after_zscored)\n",
    "    zscored_for_image = np.stack(zscored_for_image_lst, axis=1)\n",
    "    zscored_mean_over_presentations = np.max(zscored_for_image, axis=2)\n",
    "    max_zscored = np.mean(zscored_mean_over_presentations, axis=1)\n",
    "\n",
    "    sorted_cells_and_scores = sorted(zip(cell_specimen_ids, max_zscored), key=lambda x: x[1], reverse=True)\n",
    "    sorted_cell_specimen_ids = [cns[0] for cns in sorted_cells_and_scores]\n",
    "    sorted_scores = [cns[1] for cns in sorted_cells_and_scores]\n",
    "\n",
    "    return sorted_cell_specimen_ids, sorted_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3312683-bd8a-418a-8333-84d0eca9b36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_score_histogram(scores):\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "    ax = sns.histplot(scores)\n",
    "    ax.set_title(\"Distribution of scores across cells\")\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e3654153-2c59-43e3-a11b-827a2242ac69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dff(experiment, image_name, selected_cell_specimen_ids, t_before, t_after, n_before, n_after):\n",
    "    cell_specimen_ids = get_cell_specimen_ids(active_experiment)\n",
    "    stimulus_for_image_start_times = get_image_start_times(experiment, image_name)\n",
    "\n",
    "    # Average dff across all stimulus presentations\n",
    "    dff_matrix = np.vstack(experiment.dff_traces.dff.values)\n",
    "    dff_traces_lst = []\n",
    "    for stimulus_start_time in stimulus_for_image_start_times:\n",
    "        before_indices = get_indices_in_interval(\n",
    "            experiment,\n",
    "            start=stimulus_start_time - t_before,\n",
    "            end=stimulus_start_time,\n",
    "        )[-n_before:]\n",
    "        after_indices = get_indices_in_interval(\n",
    "            experiment,\n",
    "            start=stimulus_start_time,\n",
    "            end=stimulus_start_time + t_after,\n",
    "        )[:n_after]\n",
    "        dff_traces_lst.append(np.hstack((dff_matrix[:, before_indices], dff_matrix[:, after_indices])))\n",
    "    dff_traces = np.stack(dff_traces_lst, axis=1)\n",
    "    dff_traces_mean, dff_traces_std = np.mean(dff_traces, axis=1), np.std(dff_traces, axis=1)\n",
    "\n",
    "    # Choose dff traces for given cell specimen ids\n",
    "    indices = [np.argwhere(cell_specimen_ids == i)[0][0] for i in selected_cell_specimen_ids]\n",
    "    top_k_dff_traces = dff_traces_mean[indices]\n",
    "    top_k_dff_std = dff_traces_std[indices]\n",
    "\n",
    "    # Plot dff traces\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    xs = np.arange(top_k_dff_traces.shape[1])\n",
    "    ax.plot(xs, top_k_dff_traces.T)\n",
    "    for i in range(len(indices)):\n",
    "        ax.fill_between(\n",
    "            xs,\n",
    "            top_k_dff_traces[i].T - top_k_dff_std[i].T,\n",
    "            top_k_dff_traces[i].T + top_k_dff_std[i].T,\n",
    "            alpha=0.1,\n",
    "        )\n",
    "        ax.axvline(n_before-0.5, color=\"pink\")\n",
    "        ax.set_title(f\"Cell dFF traces for {image_name} in experiment {active_experiment.metadata['ophys_experiment_id']}\\nt=(-{t_before},{t_after}), n=(-{n_before},{n_after})\")\n",
    "        fig.legend(selected_cell_specimen_ids)\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ba9566-514c-4507-8bf0-e1bce213e65e",
   "metadata": {},
   "source": [
    "### Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55a8994d-7834-495e-982c-36333473af86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([450471, 459777, 459773, 447663, 445270, 403491, 456917, 456916,\n",
       "       425493, 431023, 436662, 440298, 431252, 453913, 412366, 425496,\n",
       "       445002])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_experiments_df[\"mouse_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3a4d7e4-ce0a-4f45-88e0-2807f0d2c18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MOUSE_ID = 450471"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0562af09-bcbb-4f69-b662-a53c3027bf5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ophys_experiment_id\n",
       "871155338            OPHYS_1_images_A\n",
       "872433717            OPHYS_1_images_A\n",
       "873156540            OPHYS_1_images_A\n",
       "873972085            OPHYS_1_images_A\n",
       "877022592    OPHYS_2_images_A_passive\n",
       "878363088            OPHYS_3_images_A\n",
       "879331157            OPHYS_4_images_B\n",
       "880374622    OPHYS_5_images_B_passive\n",
       "880961028            OPHYS_6_images_B\n",
       "Name: session_type, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_experiments_df.query(\"mouse_id == @MOUSE_ID\")[\"session_type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "DCi3b6RCdYzt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DCi3b6RCdYzt",
    "outputId": "cd9b52a9-5025-4305-b9c8-fe8a5766ff6b"
   },
   "outputs": [],
   "source": [
    "active_experiment = cache.get_behavior_ophys_experiment(871155338)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e19eb45f-70b3-481d-ab8a-e79aacff7f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['im065', 'omitted', 'im063', 'im061', 'im077', 'im085', 'im066',\n",
       "       'im062', 'im069'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_image_names(active_experiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0213c5-98ec-4a4d-a94e-eb59e5d29d17",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1f5bdc2f-d044-410b-934b-1d9d4fdc7621",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 644/644 [00:00<00:00, 941.62it/s] \n"
     ]
    }
   ],
   "source": [
    "sorted_cell_specimen_ids, sorted_scores = score_and_sort_cells(active_experiment, \"im085\", t_before=0.25, t_after=0.25, n_before=7, n_after=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "k-hMLvQQlnIr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "k-hMLvQQlnIr",
    "outputId": "0e63e40a-c685-4e53-be88-5aec45996c2f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_score_histogram(sorted_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "feb62900-bb5c-4514-bc80-a273ad645b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1086522347, 3.5926475626599217),\n",
       " (1086548183, 2.5300113049374775),\n",
       " (1086524443, 2.30302956655718),\n",
       " (1086527587, 2.0944942408973737),\n",
       " (1086526507, 1.9417895975918296)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(sorted_cell_specimen_ids, sorted_scores))[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "867955c1-05e2-4f90-bf51-354eea3e0cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dff(active_experiment, \"im085\", sorted_cell_specimen_ids[:5], t_before=0.25, t_after=0.75, n_before=7, n_after=21)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Single Cells.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python [conda env:nma-cn]",
   "language": "python",
   "name": "conda-env-nma-cn-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}