notebooks/hra-hierarchical-cell-neighborhoods_layers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HRA Hierarchical Tissue Unit Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will build on [an existing one on hierarchical tissue unit annotation](https://github.com/HickeyLab/Hierarchical-Tissue-Unit-Annotation) by [Dr. John Hickey](https://bme.duke.edu/people/john-hickey/). Outputs from this workbook are featured in Fig. 3 in the paper titled \"Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage\" (accepted for publication in Nature Methods, see preprint [on bioRxiv](https://doi.org/10.1101/2024.03.27.587041)). In this notebook, we will create a Vasculature Common Coordinate Framework (VCCF) distance visualization for the same donor and region (B006, proximal jejunum) as in the paper, see below.\n",
    "\n",
    "<img src=\"images/fig_3_original.jpg\" width=\"400\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concretely, we will take the original CSV file from Dryad with cell positions, types, donor IDs, and extraction sites, and then create a node-dist-vis widget plus a Cell Distance Explorer widget. For more information and documentation on hra-jupyter-widgets, please see [https://github.com/x-atlas-consortia/hra-jupyter-widgets/blob/main/usage.ipynb](https://github.com/x-atlas-consortia/hra-jupyter-widgets/blob/main/usage.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import native packages\n",
    "import os\n",
    "from pprint import pprint\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#Install and import external packages\n",
    "%pip install matplotlib pandas ipywidgets hra_jupyter_widgets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import ipywidgets as widgets\n",
    "\n",
    "# Import hra-jupyter-widgets. For documentation, please see https://github.com/x-atlas-consortia/hra-jupyter-widgets/blob/main/usage.ipynb\n",
    "from hra_jupyter_widgets import CdeVisualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data from Dryad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the data from Dryad. Note that this is a ~3GB file. We will use `curl` to dowload it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder 'data/' already exists.\n",
      "File already exists at data//23_09_CODEX_HuBMAP_alldata_Dryad_merged.csv\n"
     ]
    }
   ],
   "source": [
    "# Make sure the data folder is present\n",
    "folder_path = \"data/\"\n",
    "\n",
    "if not os.path.exists(folder_path):\n",
    "    os.makedirs(folder_path)\n",
    "    print(f\"Folder '{folder_path}' created.\")\n",
    "else:\n",
    "    print(f\"Folder '{folder_path}' already exists.\")\n",
    "\n",
    "# Define the path to the file. \n",
    "file_path = f'{folder_path}/23_09_CODEX_HuBMAP_alldata_Dryad_merged.csv'\n",
    "\n",
    "# Check if the file exists\n",
    "if not os.path.exists(file_path):\n",
    "    # If the file doesn't exist, run the curl command\n",
    "    !curl -L https://datadryad.org/api/v2/files/2572152/download -o {file_path}\n",
    "    print(f\"File downloaded and saved at {file_path}\")\n",
    "else:\n",
    "    print(f\"File already exists at {file_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data as DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file and convert it to a df\n",
    "df = pd.read_csv(file_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep cells from one dataset by selecting 1 donor and 1 region\n",
    "df_filtered = df[(df['donor'] == \"B012\") & (\n",
    "    df['unique_region'] == \"B012_Proximal jejunum\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x7/ttglg94d4g500ypbdkw3__nw0000gq/T/ipykernel_65983/1316168077.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Cell Type'] = df_filtered['Cell Type'].astype(str) + '_Cell Type'\n",
      "/var/folders/x7/ttglg94d4g500ypbdkw3__nw0000gq/T/ipykernel_65983/1316168077.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Neighborhood'] = df_filtered['Neighborhood'].astype(str) + '_Neighborhood'\n",
      "/var/folders/x7/ttglg94d4g500ypbdkw3__nw0000gq/T/ipykernel_65983/1316168077.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Community'] = df_filtered['Community'].astype(str) + '_Community'\n",
      "/var/folders/x7/ttglg94d4g500ypbdkw3__nw0000gq/T/ipykernel_65983/1316168077.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Tissue Unit'] = df_filtered['Tissue Unit'].astype(str) + '_Tissue Unit'\n"
     ]
    }
   ],
   "source": [
    "df_filtered['Cell Type'] = df_filtered['Cell Type'].astype(str) + '_Cell Type'\n",
    "df_filtered['Neighborhood'] = df_filtered['Neighborhood'].astype(str) + '_Neighborhood'\n",
    "df_filtered['Community'] = df_filtered['Community'].astype(str) + '_Community'\n",
    "df_filtered['Tissue Unit'] = df_filtered['Tissue Unit'].astype(str) + '_Tissue Unit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, let's define a function that turns a DataFrame into a node list that can then be passed into the CdeVisualization or NodeDistVis widget\n",
    "def make_node_list(df:pd.DataFrame, is_3d:bool = False):\n",
    "  \"\"\"Turn a DataFrame into a list of dicts for passing them into a HRA widget\n",
    "\n",
    "  Args:\n",
    "      df (pd.DataFrame): A DataFrame with cells\n",
    "  \"\"\"\n",
    "  \n",
    "  # If the df does not have a z-axis column, let's add one and set all cells to 0\n",
    "  if not is_3d:\n",
    "    df.loc[:, ('z')] = 0\n",
    "  \n",
    "  node_list = [{'x': row['x'], 'y': row['y'], 'z': row['z'], 'Cell Type': row['combined_info']}\n",
    "                 for index, row in df.iterrows()]\n",
    "\n",
    "  return node_list\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next, let's make four regions and visualize a 3D tissue stack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To showcase the 3D rendering capabilities of our `CDEVisualization` widget, we will display the same section as we showed with the `NodeDistVis` widget above, but we will show it three times with an added offset along the z-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new data frame with values from the NodeDistVis example\n",
    "df = df_filtered\n",
    "\n",
    "# indicate the number of layers you would like to show. In our case, let's show 3.\n",
    "n_layers = 4   # One layer for cell type, one for neighborhood, one for community, one for tissue unit\n",
    "\n",
    "# Create a list to hold all the data frames\n",
    "df_list = [df]\n",
    "\n",
    "for i in range(0, n_layers-1):\n",
    "\n",
    "  # Create a copy of this new DataFrame\n",
    "  df_copy = df.copy()\n",
    "\n",
    "  # Modify a column in the copied rows (e.g., change values in column 'B')\n",
    "  df_copy['unique_region'] = f'copy_{i}'  \n",
    "\n",
    "  # Add df_copy to list of df\n",
    "  df_list.append(df_copy)\n",
    "  \n",
    "# Concatenate the original DataFrame with the modified copies\n",
    "df_combined = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "df_filtered_3d = df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x7/ttglg94d4g500ypbdkw3__nw0000gq/T/ipykernel_65983/864382709.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_cells_3d['combined_info'] = combined_info\n"
     ]
    }
   ],
   "source": [
    "# Set a z-offset\n",
    "offset = 2500\n",
    "\n",
    "# Set z axis (or any other axis) by region\n",
    "df_filtered_3d['z'] = df_filtered_3d['unique_region'].apply(lambda v: 0 if v == 'B012_Proximal jejunum'\n",
    "                                                            else offset if v == 'copy_0'\n",
    "                                                            else offset * 2 if v == 'copy_1'\n",
    "                                                            else offset * 3\n",
    "                                                            )\n",
    "\n",
    "# Make new df with only x, y, z, Cell Type, and Neighborhood columns\n",
    "df_cells_3d = df_filtered_3d[['x', 'y', 'z', 'Cell Type', 'Neighborhood', 'Community', 'Tissue Unit']]\n",
    "\n",
    "cell_type_z0 = df_cells_3d[df_cells_3d['z'] == 0]['Cell Type']\n",
    "neighborhood_z1 = df_cells_3d[df_cells_3d['z'] == offset]['Neighborhood']\n",
    "community_z2 = df_cells_3d[df_cells_3d['z'] == offset *2]['Community']\n",
    "tissueunit_z3 = df_cells_3d[df_cells_3d['z'] == offset *3]['Tissue Unit']\n",
    "\n",
    "combined_info = pd.concat([cell_type_z0, neighborhood_z1, community_z2, tissueunit_z3])\n",
    "\n",
    "df_cells_3d['combined_info'] = combined_info\n",
    "\n",
    "df_cells_3d_filtered = df_cells_3d[['x', 'y', 'z', 'combined_info']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_list = make_node_list(df_cells_3d_filtered, True)\n",
    "edge_list = [[\"Cell ID\",\"Target ID\", \"X1\", \"Y1\", \"Z1\", \"X2\", \"Y2\", \"Z2\"]] # this makes an empty edges list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4839faa7ac248479ad3ce16f637f39a",
       "version_major": 2,
       "version_minor": 1
      },
      "text/plain": [
       "CdeVisualization(age=None, color_map=None, color_map_key=None, color_map_value_key=None, creation_timestamp=No…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, let's instantiate the CDEVisualization class with our node_list as parameter.\n",
    "cde = CdeVisualization(\n",
    "    nodes=node_list,\n",
    "    node_target_key = \"Cell Type\",\n",
    "    edges=edge_list,\n",
    ")\n",
    "\n",
    "# Display our new widget\n",
    "display(cde)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data for export and use in HRA Organ Gallery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>combined_info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7109.0</td>\n",
       "      <td>7160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cycling TA_Cell Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4803.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cycling TA_Cell Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7203.0</td>\n",
       "      <td>7413.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cycling TA_Cell Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cycling TA_Cell Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cycling TA_Cell Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130075</th>\n",
       "      <td>7604.0</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130076</th>\n",
       "      <td>664.0</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130077</th>\n",
       "      <td>5695.0</td>\n",
       "      <td>4301.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130078</th>\n",
       "      <td>61.0</td>\n",
       "      <td>3230.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130079</th>\n",
       "      <td>7063.0</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130080 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x       y     z                   combined_info\n",
       "0       7109.0  7160.0     0            Cycling TA_Cell Type\n",
       "1       4803.0    55.0     0            Cycling TA_Cell Type\n",
       "2       7203.0  7413.0     0            Cycling TA_Cell Type\n",
       "3        424.0  1437.0     0            Cycling TA_Cell Type\n",
       "4         93.0   344.0     0            Cycling TA_Cell Type\n",
       "...        ...     ...   ...                             ...\n",
       "130075  7604.0  6824.0  7500              Mucosa_Tissue Unit\n",
       "130076   664.0  4597.0  7500  Muscularis externa_Tissue Unit\n",
       "130077  5695.0  4301.0  7500              Mucosa_Tissue Unit\n",
       "130078    61.0  3230.0  7500  Muscularis externa_Tissue Unit\n",
       "130079  7063.0  2809.0  7500              Mucosa_Tissue Unit\n",
       "\n",
       "[130080 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cells_3d_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported df_Cell Type.csv\n",
      "Exported df_Neighborhood.csv\n",
      "Exported df_Community.csv\n",
      "Exported df_Tissue Unit.csv\n"
     ]
    }
   ],
   "source": [
    "# separate into 4 dfs\n",
    "df_list = []\n",
    "df_list.append(df_filtered_3d[['x', 'y', 'z', 'Cell Type']])\n",
    "df_list.append(df_filtered_3d[['x', 'y', 'z', 'Neighborhood']])\n",
    "df_list.append(df_filtered_3d[['x', 'y', 'z', 'Community']])\n",
    "df_list.append(df_filtered_3d[['x', 'y', 'z', 'Tissue Unit']])\n",
    "\n",
    "# export all\n",
    "for df in df_list:\n",
    "  last_column_name = df.columns[-1]  # Get the last column name\n",
    "  file_name = f\"df_{last_column_name}.csv\"  # Create a dynamic file name\n",
    "  df.to_csv(f\"data/output/{file_name}\", index=False)  # Export the DataFrame to CSV\n",
    "  print(f\"Exported {file_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>MUC2</th>\n",
       "      <th>SOX9</th>\n",
       "      <th>MUC1</th>\n",
       "      <th>CD31</th>\n",
       "      <th>Synapto</th>\n",
       "      <th>CD49f</th>\n",
       "      <th>CD15</th>\n",
       "      <th>CHGA</th>\n",
       "      <th>CDX2</th>\n",
       "      <th>ITLN1</th>\n",
       "      <th>...</th>\n",
       "      <th>Cell Type em</th>\n",
       "      <th>Cell subtype</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neigh_sub</th>\n",
       "      <th>Neighborhood_Ind</th>\n",
       "      <th>NeighInd_sub</th>\n",
       "      <th>Community</th>\n",
       "      <th>Major Community</th>\n",
       "      <th>Tissue Segment</th>\n",
       "      <th>Tissue Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1890593</th>\n",
       "      <td>0.216090</td>\n",
       "      <td>1.119012</td>\n",
       "      <td>0.567092</td>\n",
       "      <td>-0.242881</td>\n",
       "      <td>-0.274699</td>\n",
       "      <td>-0.191957</td>\n",
       "      <td>-0.204962</td>\n",
       "      <td>-0.109570</td>\n",
       "      <td>0.029645</td>\n",
       "      <td>0.117278</td>\n",
       "      <td>...</td>\n",
       "      <td>Cycling TA</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Transit Amplifying Zone_Neighborhood</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Transit Amplifying Zone</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Plasma Cell Enriched_Community</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890594</th>\n",
       "      <td>-0.221731</td>\n",
       "      <td>-0.223280</td>\n",
       "      <td>-0.283894</td>\n",
       "      <td>-0.256254</td>\n",
       "      <td>-0.273986</td>\n",
       "      <td>-0.373476</td>\n",
       "      <td>-0.215366</td>\n",
       "      <td>-0.113153</td>\n",
       "      <td>-0.262340</td>\n",
       "      <td>-0.354025</td>\n",
       "      <td>...</td>\n",
       "      <td>Cycling TA</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Plasma Cell Enriched_Neighborhood</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Plasma Cell Enriched</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Plasma Cell Enriched_Community</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890595</th>\n",
       "      <td>-0.221731</td>\n",
       "      <td>-0.026314</td>\n",
       "      <td>-0.442065</td>\n",
       "      <td>-0.256254</td>\n",
       "      <td>-0.274699</td>\n",
       "      <td>-0.211868</td>\n",
       "      <td>-0.173806</td>\n",
       "      <td>0.101372</td>\n",
       "      <td>-0.019355</td>\n",
       "      <td>-0.153058</td>\n",
       "      <td>...</td>\n",
       "      <td>Cycling TA</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Secretory Epithelial_Neighborhood</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adatpive Immune Enriched</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Secretory Epithelial_Community</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890596</th>\n",
       "      <td>-0.177174</td>\n",
       "      <td>-0.140475</td>\n",
       "      <td>-0.241711</td>\n",
       "      <td>-0.217204</td>\n",
       "      <td>-0.270773</td>\n",
       "      <td>-0.342078</td>\n",
       "      <td>-0.155135</td>\n",
       "      <td>-0.107398</td>\n",
       "      <td>-0.243269</td>\n",
       "      <td>-0.306894</td>\n",
       "      <td>...</td>\n",
       "      <td>Cycling TA</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Macrovasculature_Neighborhood</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Stroma &amp; Innate Immune</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Stroma_Community</td>\n",
       "      <td>Stroma</td>\n",
       "      <td>Subucosa</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890597</th>\n",
       "      <td>-0.166252</td>\n",
       "      <td>-0.155544</td>\n",
       "      <td>-0.225982</td>\n",
       "      <td>-0.227331</td>\n",
       "      <td>-0.255545</td>\n",
       "      <td>-0.349508</td>\n",
       "      <td>-0.211896</td>\n",
       "      <td>-0.107097</td>\n",
       "      <td>-0.221056</td>\n",
       "      <td>-0.295231</td>\n",
       "      <td>...</td>\n",
       "      <td>Cycling TA</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Innervated Smooth Muscle_Neighborhood</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Smooth Muscle</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Smooth Muscle_Community</td>\n",
       "      <td>Smooth Muscle</td>\n",
       "      <td>Muscle</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056157</th>\n",
       "      <td>-0.221627</td>\n",
       "      <td>-0.223280</td>\n",
       "      <td>-0.361725</td>\n",
       "      <td>-0.233062</td>\n",
       "      <td>-0.255201</td>\n",
       "      <td>-0.379466</td>\n",
       "      <td>-0.215366</td>\n",
       "      <td>-0.113153</td>\n",
       "      <td>-0.284214</td>\n",
       "      <td>-0.296658</td>\n",
       "      <td>...</td>\n",
       "      <td>DC</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Plasma Cell Enriched_Neighborhood</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Plasma Cell Enriched</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Plasma Cell Enriched_Community</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056158</th>\n",
       "      <td>-0.212460</td>\n",
       "      <td>-0.188303</td>\n",
       "      <td>-0.317383</td>\n",
       "      <td>-0.250673</td>\n",
       "      <td>-0.263856</td>\n",
       "      <td>-0.369020</td>\n",
       "      <td>-0.200340</td>\n",
       "      <td>-0.109447</td>\n",
       "      <td>-0.265941</td>\n",
       "      <td>-0.318743</td>\n",
       "      <td>...</td>\n",
       "      <td>DC</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Innervated Smooth Muscle_Neighborhood</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Smooth Muscle</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Smooth Muscle_Community</td>\n",
       "      <td>Smooth Muscle</td>\n",
       "      <td>Muscle</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056159</th>\n",
       "      <td>-0.218078</td>\n",
       "      <td>-0.207831</td>\n",
       "      <td>-0.338366</td>\n",
       "      <td>-0.256254</td>\n",
       "      <td>-0.249269</td>\n",
       "      <td>-0.379466</td>\n",
       "      <td>-0.212297</td>\n",
       "      <td>-0.106724</td>\n",
       "      <td>-0.266768</td>\n",
       "      <td>-0.295096</td>\n",
       "      <td>...</td>\n",
       "      <td>DC</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Innate Immune Enriched_Neighborhood</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Stroma &amp; Innate Immune</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Adaptive Immune Enriched_Community</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056160</th>\n",
       "      <td>-0.203579</td>\n",
       "      <td>-0.219397</td>\n",
       "      <td>-0.307815</td>\n",
       "      <td>0.040683</td>\n",
       "      <td>0.241911</td>\n",
       "      <td>-0.348804</td>\n",
       "      <td>-0.184639</td>\n",
       "      <td>-0.112245</td>\n",
       "      <td>-0.111207</td>\n",
       "      <td>-0.322564</td>\n",
       "      <td>...</td>\n",
       "      <td>DC</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Macrovasculature_Neighborhood</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Macrovasculature</td>\n",
       "      <td>Stromal</td>\n",
       "      <td>Smooth Muscle_Community</td>\n",
       "      <td>Smooth Muscle</td>\n",
       "      <td>Muscle</td>\n",
       "      <td>Muscularis externa_Tissue Unit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056161</th>\n",
       "      <td>-0.193161</td>\n",
       "      <td>-0.180678</td>\n",
       "      <td>-0.313999</td>\n",
       "      <td>-0.256254</td>\n",
       "      <td>-0.261860</td>\n",
       "      <td>-0.042046</td>\n",
       "      <td>-0.200856</td>\n",
       "      <td>-0.102556</td>\n",
       "      <td>-0.251901</td>\n",
       "      <td>-0.322239</td>\n",
       "      <td>...</td>\n",
       "      <td>DC</td>\n",
       "      <td>Immune</td>\n",
       "      <td>CD8+ T Enriched IEL_Neighborhood</td>\n",
       "      <td>Immune</td>\n",
       "      <td>CD8+ T Enriched IEL</td>\n",
       "      <td>Immune</td>\n",
       "      <td>Mature Epithelial_Community</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Mucosa</td>\n",
       "      <td>Mucosa_Tissue Unit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32520 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MUC2      SOX9      MUC1      CD31   Synapto     CD49f      CD15  \\\n",
       "1890593  0.216090  1.119012  0.567092 -0.242881 -0.274699 -0.191957 -0.204962   \n",
       "1890594 -0.221731 -0.223280 -0.283894 -0.256254 -0.273986 -0.373476 -0.215366   \n",
       "1890595 -0.221731 -0.026314 -0.442065 -0.256254 -0.274699 -0.211868 -0.173806   \n",
       "1890596 -0.177174 -0.140475 -0.241711 -0.217204 -0.270773 -0.342078 -0.155135   \n",
       "1890597 -0.166252 -0.155544 -0.225982 -0.227331 -0.255545 -0.349508 -0.211896   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "2056157 -0.221627 -0.223280 -0.361725 -0.233062 -0.255201 -0.379466 -0.215366   \n",
       "2056158 -0.212460 -0.188303 -0.317383 -0.250673 -0.263856 -0.369020 -0.200340   \n",
       "2056159 -0.218078 -0.207831 -0.338366 -0.256254 -0.249269 -0.379466 -0.212297   \n",
       "2056160 -0.203579 -0.219397 -0.307815  0.040683  0.241911 -0.348804 -0.184639   \n",
       "2056161 -0.193161 -0.180678 -0.313999 -0.256254 -0.261860 -0.042046 -0.200856   \n",
       "\n",
       "             CHGA      CDX2     ITLN1  ...  Cell Type em  Cell subtype  \\\n",
       "1890593 -0.109570  0.029645  0.117278  ...    Cycling TA    Epithelial   \n",
       "1890594 -0.113153 -0.262340 -0.354025  ...    Cycling TA    Epithelial   \n",
       "1890595  0.101372 -0.019355 -0.153058  ...    Cycling TA    Epithelial   \n",
       "1890596 -0.107398 -0.243269 -0.306894  ...    Cycling TA    Epithelial   \n",
       "1890597 -0.107097 -0.221056 -0.295231  ...    Cycling TA    Epithelial   \n",
       "...           ...       ...       ...  ...           ...           ...   \n",
       "2056157 -0.113153 -0.284214 -0.296658  ...            DC        Immune   \n",
       "2056158 -0.109447 -0.265941 -0.318743  ...            DC        Immune   \n",
       "2056159 -0.106724 -0.266768 -0.295096  ...            DC        Immune   \n",
       "2056160 -0.112245 -0.111207 -0.322564  ...            DC        Immune   \n",
       "2056161 -0.102556 -0.251901 -0.322239  ...            DC        Immune   \n",
       "\n",
       "                                  Neighborhood   Neigh_sub  \\\n",
       "1890593   Transit Amplifying Zone_Neighborhood  Epithelial   \n",
       "1890594      Plasma Cell Enriched_Neighborhood      Immune   \n",
       "1890595      Secretory Epithelial_Neighborhood  Epithelial   \n",
       "1890596          Macrovasculature_Neighborhood     Stromal   \n",
       "1890597  Innervated Smooth Muscle_Neighborhood     Stromal   \n",
       "...                                        ...         ...   \n",
       "2056157      Plasma Cell Enriched_Neighborhood      Immune   \n",
       "2056158  Innervated Smooth Muscle_Neighborhood     Stromal   \n",
       "2056159    Innate Immune Enriched_Neighborhood      Immune   \n",
       "2056160          Macrovasculature_Neighborhood     Stromal   \n",
       "2056161       CD8+ T Enriched IEL_Neighborhood      Immune   \n",
       "\n",
       "                 Neighborhood_Ind  NeighInd_sub  \\\n",
       "1890593   Transit Amplifying Zone    Epithelial   \n",
       "1890594      Plasma Cell Enriched        Immune   \n",
       "1890595  Adatpive Immune Enriched        Immune   \n",
       "1890596    Stroma & Innate Immune       Stromal   \n",
       "1890597             Smooth Muscle       Stromal   \n",
       "...                           ...           ...   \n",
       "2056157      Plasma Cell Enriched        Immune   \n",
       "2056158             Smooth Muscle       Stromal   \n",
       "2056159    Stroma & Innate Immune       Stromal   \n",
       "2056160          Macrovasculature       Stromal   \n",
       "2056161       CD8+ T Enriched IEL        Immune   \n",
       "\n",
       "                                  Community  Major Community  Tissue Segment  \\\n",
       "1890593      Plasma Cell Enriched_Community           Immune          Mucosa   \n",
       "1890594      Plasma Cell Enriched_Community           Immune          Mucosa   \n",
       "1890595      Secretory Epithelial_Community       Epithelial          Mucosa   \n",
       "1890596                    Stroma_Community           Stroma        Subucosa   \n",
       "1890597             Smooth Muscle_Community    Smooth Muscle          Muscle   \n",
       "...                                     ...              ...             ...   \n",
       "2056157      Plasma Cell Enriched_Community           Immune          Mucosa   \n",
       "2056158             Smooth Muscle_Community    Smooth Muscle          Muscle   \n",
       "2056159  Adaptive Immune Enriched_Community           Immune          Mucosa   \n",
       "2056160             Smooth Muscle_Community    Smooth Muscle          Muscle   \n",
       "2056161         Mature Epithelial_Community       Epithelial          Mucosa   \n",
       "\n",
       "                            Tissue Unit  \n",
       "1890593              Mucosa_Tissue Unit  \n",
       "1890594              Mucosa_Tissue Unit  \n",
       "1890595              Mucosa_Tissue Unit  \n",
       "1890596  Muscularis externa_Tissue Unit  \n",
       "1890597  Muscularis externa_Tissue Unit  \n",
       "...                                 ...  \n",
       "2056157              Mucosa_Tissue Unit  \n",
       "2056158  Muscularis externa_Tissue Unit  \n",
       "2056159              Mucosa_Tissue Unit  \n",
       "2056160  Muscularis externa_Tissue Unit  \n",
       "2056161              Mucosa_Tissue Unit  \n",
       "\n",
       "[32520 rows x 74 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered"
   ]
  }
 ],
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