EXAMPLE_USAGE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47457844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting senepy\n",
      "  Downloading senepy-0.0.0-py3-none-any.whl (3.5 MB)\n",
      "\u001b[K     |████████████████████████████████| 3.5 MB 30.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas>=1.0 in /home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages (from senepy) (1.4.3)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages (from senepy) (1.22.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages (from pandas>=1.0->senepy) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages (from pandas>=1.0->senepy) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas>=1.0->senepy) (1.16.0)\n",
      "Installing collected packages: senepy\n",
      "Successfully installed senepy-0.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#installation\n",
    "%pip install senepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30c53698",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scanpy is recommended for handling single-cell data\n",
    "%pip install scanpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf986276",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b4eb870",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load modules\n",
    "import senepy as sp\n",
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9869ff8",
   "metadata": {},
   "source": [
    "### Import senescence gene hubs from study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a57cf91a",
   "metadata": {},
   "outputs": [],
   "source": [
    "hubs = sp.load_hubs(species = 'Mouse') #supports Human and Mouse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa0ea5b",
   "metadata": {},
   "source": [
    "hubs.metadata will contain a list of the hubs within the hubs object. It contains which tissue and cell type\n",
    "the hub was derived from as well as the hub_num if there were multiple senescence hubs within that cell type.\n",
    "n_sen: number of 180 prior \"known\" senescence markers curated from literature that were found in the hub.\n",
    "hyp: enrichment p-value of the \"known\" and novel hub genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e13b2979",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tissue</th>\n",
       "      <th>cell</th>\n",
       "      <th>hub_num</th>\n",
       "      <th>size</th>\n",
       "      <th>n_sen</th>\n",
       "      <th>hyp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bladder</td>\n",
       "      <td>bladder cell</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bladder</td>\n",
       "      <td>bladder cell</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bladder</td>\n",
       "      <td>bladder cell</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bladder</td>\n",
       "      <td>endothelial cell</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Diaphragm</td>\n",
       "      <td>mesenchymal stem cell</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Trachea</td>\n",
       "      <td>basal cell</td>\n",
       "      <td>1</td>\n",
       "      <td>495</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Trachea</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>0</td>\n",
       "      <td>680</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Trachea</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>0.147080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Trachea</td>\n",
       "      <td>macrophage</td>\n",
       "      <td>0</td>\n",
       "      <td>1654</td>\n",
       "      <td>23</td>\n",
       "      <td>0.028732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Trachea</td>\n",
       "      <td>macrophage</td>\n",
       "      <td>1</td>\n",
       "      <td>291</td>\n",
       "      <td>2</td>\n",
       "      <td>0.747975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tissue                   cell  hub_num  size  n_sen       hyp\n",
       "0     Bladder           bladder cell        0    20      0  1.000000\n",
       "1     Bladder           bladder cell        1    36      0  1.000000\n",
       "2     Bladder           bladder cell        2    61      2  0.107390\n",
       "3     Bladder       endothelial cell        1    29      0  1.000000\n",
       "4   Diaphragm  mesenchymal stem cell        0    88      0  1.000000\n",
       "..        ...                    ...      ...   ...    ...       ...\n",
       "70    Trachea            basal cell         1   495     16  0.000013\n",
       "71    Trachea             fibroblast        0   680     21  0.000001\n",
       "72    Trachea             fibroblast        1    74      2  0.147080\n",
       "73    Trachea             macrophage        0  1654     23  0.028732\n",
       "74    Trachea             macrophage        1   291      2  0.747975\n",
       "\n",
       "[75 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5d26e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tissue</th>\n",
       "      <th>cell</th>\n",
       "      <th>hub_num</th>\n",
       "      <th>size</th>\n",
       "      <th>n_sen</th>\n",
       "      <th>hyp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>1.534293e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>3.971457e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lung</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>3</td>\n",
       "      <td>4.110577e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Lung</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>1</td>\n",
       "      <td>547</td>\n",
       "      <td>23</td>\n",
       "      <td>1.201959e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tissue                 cell  hub_num  size  n_sen           hyp\n",
       "47   Lung  alveolar macrophage        0    76      2  1.534293e-01\n",
       "48   Lung  alveolar macrophage        1    33      0  1.000000e+00\n",
       "49   Lung  alveolar macrophage        2    55      1  3.971457e-01\n",
       "50   Lung           fibroblast        0   254      3  4.110577e-01\n",
       "51   Lung           fibroblast        1   547     23  1.201959e-09"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at hubs within Lung\n",
    "hubs.metadata[hubs.metadata.tissue == 'Lung'] #capitalization matters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e2aecd",
   "metadata": {},
   "source": [
    "hubs.hubs will contain a dictionary of the hub identifiers and the hub genes/importance values.\n",
    "To get the information for an individual hub we pass the identifiers to the dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bc975d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Arhgap30', 40),\n",
       " ('Serinc2', 30),\n",
       " ('Enc1', 51),\n",
       " ('Susd3', 50),\n",
       " ('Krt5', 201),\n",
       " ('Aldh3b2', 102),\n",
       " ('Csf2rb', 68),\n",
       " ('Acot10', 22),\n",
       " ('Ptprb', 8),\n",
       " ('4632427E13Rik', 9)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.hubs[('Lung', 'fibroblast', 1)][:10] #only showing first 10 with [:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85f49aa4-f84f-49a8-89f9-dcfd933a5cbb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4af829b9-a9f7-416e-b028-0b2c5a6dedc4",
   "metadata": {},
   "source": [
    "### Get gene list from a hub/signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8eea22e-3bf3-4785-a31e-d12fab62cb89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Arhgap30',\n",
       " 'Serinc2',\n",
       " 'Enc1',\n",
       " 'Susd3',\n",
       " 'Krt5',\n",
       " 'Aldh3b2',\n",
       " 'Csf2rb',\n",
       " 'Acot10',\n",
       " 'Ptprb',\n",
       " '4632427E13Rik']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.get_genes(('Lung', 'fibroblast', 1))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "404c404e-921b-4b92-a213-bdc9f94e420f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if you used 'cell_type' in load_hubs it would looke like this:\n",
    "#hubs.get_genes(('Lung', 'fibroblast'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05b3cac6-b152-4609-a9ae-f61aea2a9fea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0fbc38b5-dd70-4d97-8752-a31d38bd29c7",
   "metadata": {},
   "source": [
    "### Merge signatures\n",
    "Sometimes you may want to merge multiple signatures into one.\n",
    "For example, if there is no signature that represents your specific cell type, you can try merging all from that tissue. We can also merge all signatures to create a universal signature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "265f2746-66be-4b80-92f6-c98bfa44b0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tissue</th>\n",
       "      <th>cell</th>\n",
       "      <th>hub_num</th>\n",
       "      <th>size</th>\n",
       "      <th>n_sen</th>\n",
       "      <th>hyp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>1.534293e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lung</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>3.971457e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lung</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>3</td>\n",
       "      <td>4.110577e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Lung</td>\n",
       "      <td>fibroblast</td>\n",
       "      <td>1</td>\n",
       "      <td>547</td>\n",
       "      <td>23</td>\n",
       "      <td>1.201959e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tissue                 cell  hub_num  size  n_sen           hyp\n",
       "47   Lung  alveolar macrophage        0    76      2  1.534293e-01\n",
       "48   Lung  alveolar macrophage        1    33      0  1.000000e+00\n",
       "49   Lung  alveolar macrophage        2    55      1  3.971457e-01\n",
       "50   Lung           fibroblast        0   254      3  4.110577e-01\n",
       "51   Lung           fibroblast        1   547     23  1.201959e-09"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter the metadata to only include what you want to merge\n",
    "filt_meta = hubs.metadata[hubs.metadata.tissue == 'Lung']\n",
    "filt_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "858f7cd7-72eb-42c4-893c-eeb5858c65d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "21858f92-0071-4f99-895c-ffecc5f506e6",
   "metadata": {},
   "source": [
    "Below we merge the Lung signatures into one new signature. By default, the union of all genes is taken. The respective value for each gene is the number of times that gene is found in the input signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d74ca261-0bce-442c-8672-6bfb172dc089",
   "metadata": {},
   "outputs": [],
   "source": [
    "hubs.merge_hubs(filt_meta, new_name = 'Lung_merged')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6dd1e7e6-30aa-4481-ab20-c520744a9073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "937"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hubs.hubs['Lung_merged'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2096e4ab-1948-4418-8108-1c13fcc69c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Adora2a', 1),\n",
       " ('Srgap1', 1),\n",
       " ('Rgs18', 1),\n",
       " ('Rap1gap2', 1),\n",
       " ('Ms4a4c', 1),\n",
       " ('Eif4e3', 1),\n",
       " ('Susd3', 2),\n",
       " ('Padi4', 1),\n",
       " ('Elane', 2),\n",
       " ('Ms4a6b', 1)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.hubs['Lung_merged'][:10] #looking at the first 10 items in the new signature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bba0213-6870-472f-aeed-84875a2b331e",
   "metadata": {},
   "source": [
    "### Universal signature\n",
    "We can increase the threshold. For example, if we want to keep signatures that occur at least 2 times. This number will drastically reduce the number of genes in the resulting signature if only merging a handful of input signatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51259a9f-968d-4c16-8624-3f646c283c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "hubs.merge_hubs(filt_meta, new_name = 'Lung_merged_min2', overlap_threshold = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf50ff6f-9835-41a6-913e-db165cfca644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hubs.hubs['Lung_merged_min2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c12950-d212-47f2-a6d2-8c9932492c69",
   "metadata": {},
   "source": [
    "We can also cacluate a threshold automatically using a permutation-based method. In the example below, we can merge all signatures from all tissues and cells. We then use a 5% random chance threshold and remove all the genes that occur less than that number of times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c39c1e7-35f0-4a11-becc-dc5653377345",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████| 1000/1000 [00:55<00:00, 17.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A gene will occur 4 times at 4.65% chance\n",
      "Threfore 4 is the calculated_threshold\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hubs.merge_hubs(hubs.metadata, new_name = 'universal', calculate_thresh = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2002821-c09f-40f7-8f5c-c7eff30fd861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1166"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hubs.hubs['universal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8cafe0d9-3635-4792-9c9c-5df2c87f8e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Agtr1a', 4),\n",
       " ('Cadm4', 4),\n",
       " ('Fam89a', 4),\n",
       " ('Syt7', 5),\n",
       " ('Msln', 4),\n",
       " ('Slpi', 15),\n",
       " ('Pkhd1l1', 4),\n",
       " ('Podxl', 5),\n",
       " ('Prr15l', 4),\n",
       " ('Prlr', 4)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.hubs['universal'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b9934e-1064-4a1e-899e-c5548fde6fc1",
   "metadata": {},
   "source": [
    "You can adjust additional parameters of merge_hubs to change the chance required, etc. see hubs.merge_hubs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d315af6-a38e-4b53-a8b3-0fff3eac8c8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9fd3bffc-5f03-43df-af8c-2091ecaa611c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Hba-a1', 26),\n",
       " ('Hbb-b1', 19),\n",
       " ('Saa3', 18),\n",
       " ('Gm11428', 18),\n",
       " ('Vsig4', 17),\n",
       " ('Slpi', 15),\n",
       " ('Igj', 15),\n",
       " ('Cxcl13', 15),\n",
       " ('Elane', 15),\n",
       " ('Ccl5', 14)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if you want to see the top genes of any signature\n",
    "sorted(hubs.hubs['universal'], key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76bf7bc1-3eca-45c2-8465-c976ce028f86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c342fb-d74c-43e2-9ffa-694bb1859d76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eaf9ce03-db30-4f14-aacb-f44cfa3080de",
   "metadata": {},
   "source": [
    "### Prior knowledge gene set and senGPT gene set used in this study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644c22b8-f0f3-4e7f-8301-b6d065f21f59",
   "metadata": {},
   "source": [
    "To get the prior knowledge markers taken from the literature in this study you can use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b06559d-48f0-4491-9ed2-84eb0b3f8655",
   "metadata": {},
   "outputs": [],
   "source": [
    "hubs.literature_markers #this includes their function or source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5cf43475-120f-430e-8a30-c47a3a4e7332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cdkn2a',\n",
       " 'Cdkn2b',\n",
       " 'Cdkn1a',\n",
       " 'Cdkn1b',\n",
       " 'Trp53',\n",
       " 'Rb1',\n",
       " 'Glb1',\n",
       " 'Tfeb',\n",
       " 'Nfkb1',\n",
       " 'Cebpb']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(hubs.literature_markers)[:10] #to only get the gene names pass it to list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2343b82-f593-46b2-b80a-3c3484169597",
   "metadata": {},
   "source": [
    "To get senGPT markers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6ae2f51-f79a-4c3f-ae7e-f339d43e228f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cdkn2a',\n",
       " 'Cdkn1a',\n",
       " 'Cdkn2b',\n",
       " 'Il6',\n",
       " 'Il8',\n",
       " 'Cxcl1',\n",
       " 'Cxcl2',\n",
       " 'Cxcl3',\n",
       " 'Cxcl5',\n",
       " 'Cxcl6']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.senGPT[0:10] #only showing first 10 with [0:10]. Remove it to get all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b29f64cf-e263-42f1-ab6a-dd792a630955",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19b70fab-659a-4e44-9a0c-8a46af38ccc3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cbe23a0a-9030-45e3-a1f5-a4d56ae0648d",
   "metadata": {},
   "source": [
    "### Search the hubs/signatures by genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab83dfb0-c231-41a5-b96a-e0df4ae71754",
   "metadata": {},
   "source": [
    "Example of custom input gene search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2652cb5b-4a77-4531-899c-bec5f37cc215",
   "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>tissue</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>hub_num</th>\n",
       "      <th>size</th>\n",
       "      <th>num_hits</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "      <th>hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tongue</td>\n",
       "      <td>basal cell</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.003078</td>\n",
       "      <td>[Il6, Cxcl13]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lymphoid</td>\n",
       "      <td>T cell</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>[Cdkn2a, Cxcl13]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tongue</td>\n",
       "      <td>keratinocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>[Il6, Cxcl13]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Heart_and_Aorta</td>\n",
       "      <td>myocyte</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>[Il6, Cxcl13]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heart_and_Aorta</td>\n",
       "      <td>endocardial cell</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.012713</td>\n",
       "      <td>[Il6, Cxcl13]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tissue         cell_type  hub_num  size  num_hits   p_value  \\\n",
       "0           Tongue       basal cell         0    66         2  0.000041   \n",
       "1         Lymphoid            T cell        1    71         2  0.000048   \n",
       "2           Tongue      keratinocyte        0   133         2  0.000167   \n",
       "3  Heart_and_Aorta           myocyte        2   147         2  0.000204   \n",
       "4  Heart_and_Aorta  endocardial cell        1   300         2  0.000848   \n",
       "\n",
       "      p_adj              hits  \n",
       "0  0.003078     [Il6, Cxcl13]  \n",
       "1  0.001783  [Cdkn2a, Cxcl13]  \n",
       "2  0.004184     [Il6, Cxcl13]  \n",
       "3  0.003834     [Il6, Cxcl13]  \n",
       "4  0.012713     [Il6, Cxcl13]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.search_hubs_by_genes(['Cdkn2a', 'Cdkn1a', 'Il6', 'Cxcl13']).head() #remove .head() for all results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93da4fe3-16e6-4ac0-852c-cbc7a4e8cc8c",
   "metadata": {},
   "source": [
    "Example using senGPT markers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da4e5c2d-9293-4729-9917-873dbc338082",
   "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>tissue</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>hub_num</th>\n",
       "      <th>size</th>\n",
       "      <th>num_hits</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "      <th>hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Heart_and_Aorta</td>\n",
       "      <td>myocyte</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>8</td>\n",
       "      <td>1.390877e-07</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>[Il6, Mmp3, Il10, Tnf, Ccl2, Ccl5, Mmp25, E2f2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Liver</td>\n",
       "      <td>endothelial cell of hepatic sinusoid</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>8</td>\n",
       "      <td>1.583844e-05</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>[Cdkn2a, Cdkn2b, Edn1, Bmp4, Igfbp5, Mmp15, E2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tongue</td>\n",
       "      <td>keratinocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>1.984935e-04</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>[Il6, Cxcl3, Mmp3, Ccl5, Vcam1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Heart_and_Aorta</td>\n",
       "      <td>endocardial cell</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2.063324e-04</td>\n",
       "      <td>0.003869</td>\n",
       "      <td>[Cdkn2b, Il6, Cxcl2, Mmp9, Mmp12, Tnf, Ccl5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kidney</td>\n",
       "      <td>Henle thick ascending limb epithelial cell</td>\n",
       "      <td>0</td>\n",
       "      <td>1035</td>\n",
       "      <td>13</td>\n",
       "      <td>2.460405e-04</td>\n",
       "      <td>0.003691</td>\n",
       "      <td>[Cxcl1, Mmp14, Il1b, Ccl2, Ccl5, Icam1, Timp2,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tissue                                   cell_type  hub_num  size  \\\n",
       "0  Heart_and_Aorta                                     myocyte        2   147   \n",
       "1            Liver        endothelial cell of hepatic sinusoid        0   277   \n",
       "2           Tongue                                keratinocyte        0   133   \n",
       "3  Heart_and_Aorta                            endocardial cell        1   300   \n",
       "4           Kidney  Henle thick ascending limb epithelial cell        0  1035   \n",
       "\n",
       "   num_hits       p_value     p_adj  \\\n",
       "0         8  1.390877e-07  0.000010   \n",
       "1         8  1.583844e-05  0.000594   \n",
       "2         5  1.984935e-04  0.004962   \n",
       "3         7  2.063324e-04  0.003869   \n",
       "4        13  2.460405e-04  0.003691   \n",
       "\n",
       "                                                hits  \n",
       "0    [Il6, Mmp3, Il10, Tnf, Ccl2, Ccl5, Mmp25, E2f2]  \n",
       "1  [Cdkn2a, Cdkn2b, Edn1, Bmp4, Igfbp5, Mmp15, E2...  \n",
       "2                    [Il6, Cxcl3, Mmp3, Ccl5, Vcam1]  \n",
       "3       [Cdkn2b, Il6, Cxcl2, Mmp9, Mmp12, Tnf, Ccl5]  \n",
       "4  [Cxcl1, Mmp14, Il1b, Ccl2, Ccl5, Icam1, Timp2,...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubs.search_hubs_by_genes(hubs.senGPT).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317366da-0c50-428d-a544-87ae8d9cce43",
   "metadata": {},
   "source": [
    "Note that the the default background size is 25000 (bg_N = 25000) for this and the p_value will be more accurate if it reflects your study context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfcf224b-19fb-419b-963c-8de36c9082e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c4719d-cc2c-41fd-bb58-a70a8574a998",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "55047e92-afeb-42d5-ad73-260f398e42b4",
   "metadata": {},
   "source": [
    "# Cell scoring Example:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eca76ec",
   "metadata": {},
   "source": [
    "### Loading in single-cell adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7515cb9",
   "metadata": {},
   "source": [
    "This step is specific to your project -- for this example we will load in single cells from mouse lungs from Tabula Muris Senis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "568b4597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  648M  100  648M    0     0  19.8M      0  0:00:32  0:00:32 --:--:-- 22.1M\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#downloading file from TMS figshare https://figshare.com/projects/Tabula_Muris_Senis/64982\n",
    "!curl -o lung.h5ad \"https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/23873012/tabulamurissenisdropletprocessedofficialannotationsLung.h5ad\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "357e4066",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages/anndata/compat/__init__.py:232: FutureWarning: Moving element from .uns['neighbors']['distances'] to .obsp['distances'].\n",
      "\n",
      "This is where adjacency matrices should go now.\n",
      "  warn(\n",
      "/home/ubuntu/miniconda3/envs/sc/lib/python3.10/site-packages/anndata/compat/__init__.py:232: FutureWarning: Moving element from .uns['neighbors']['connectivities'] to .obsp['connectivities'].\n",
      "\n",
      "This is where adjacency matrices should go now.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "adata = sc.read_h5ad('lung.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b585e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain</th>\n",
       "      <th>leiden</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACGGGTCGCCCTTA-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>5774.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCAGACTG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG</td>\n",
       "      <td>non-classical monocyte</td>\n",
       "      <td>NA</td>\n",
       "      <td>Nonclassical Monocyte</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3878.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCCGTCGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAACATGGTAGG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Macrophage</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>3322.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>13346.0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAAGTCTAGTGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2608.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2803.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3041.0</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>7843.0</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>7172.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24540 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age                                 cell  \\\n",
       "index                                                                    \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT   \n",
       "...                           ...                                  ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1   3m           10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1   3m           10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1   3m           10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1   3m           10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1   3m           10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                            cell_ontology_class  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                                B cell   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                non-classical monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                               NK cell   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                   alveolar macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                                B cell   \n",
       "...                                                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                 fibroblast of lung   \n",
       "\n",
       "                             cell_ontology_id         free_annotation  \\\n",
       "index                                                                   \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                  NA                       B   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                  NA   Nonclassical Monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                  NA          Natural Killer   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                  NA     Alveolar Macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                  NA                       B   \n",
       "...                                       ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                               method mouse.id  n_genes     sex  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     droplet  18-F-50   1821.0  female   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     droplet  18-F-50   1333.0  female   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     droplet  18-F-50   1455.0  female   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     droplet  18-F-50   3322.0  female   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     droplet  18-F-50   1120.0  female   \n",
       "...                               ...      ...      ...     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  droplet  3-M-7/8      NaN    male   \n",
       "\n",
       "                                         subtissue tissue  \\\n",
       "index                                                       \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      nan   Lung   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      nan   Lung   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      nan   Lung   \n",
       "...                                            ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                             tissue_free_annotation  n_counts louvain leiden  \n",
       "index                                                                         \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      Lung    5774.0       4      3  \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      Lung    3878.0       2      1  \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      Lung    3990.0       7      5  \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      Lung   13346.0       9      8  \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      Lung    2608.0       4      3  \n",
       "...                                             ...       ...     ...    ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                   Lung    2803.0      10     12  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                   Lung    3049.0       7      5  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1                   Lung    3041.0      15     22  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   Lung    7843.0      15     20  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                   Lung    7172.0      19     17  \n",
       "\n",
       "[24540 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a2f8f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "919cb93c",
   "metadata": {},
   "source": [
    "### Translate hub genes - optional\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b8d77f",
   "metadata": {},
   "source": [
    "References may use different gene aliases or may practice different capitalization standards. We include\n",
    "a simple tool to translate the hub gene names to the corresponding gene names in your reference dataset.\n",
    "This is not a catch all for every situation. You can manually add a dictionary to the translator if your\n",
    "dataset is an exception or the transltor does not catch all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f08c0b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 of 18811 genes not initially present\n",
      "0 of 0 translated\n",
      "0 still not present\n"
     ]
    }
   ],
   "source": [
    "translator = sp.translator(hub = hubs.hubs, data = adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d74e43",
   "metadata": {},
   "source": [
    "With our input dataset we did not need to translate because all of the genes match. However, this is not always the case. Do not worry if 100% of the genes are not present. There are multiple reasons why this may be the case. For\n",
    "example, if you filtered the genes previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c5491c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2d982aa9",
   "metadata": {},
   "source": [
    "### Score cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5c2899",
   "metadata": {},
   "source": [
    "It is best to score only individual cell types at a time. Below we subset the trachea cells to only fibroblasts\n",
    "then apply the fibroblast hub to score it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bd9452f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#score_hub returns a list and we can save this directly to the adata.obs dataframe\n",
    "\n",
    "fib_subset = adata[adata.obs['cell_ontology_class'] == 'fibroblast of lung'].copy()\n",
    "\n",
    "fib_subset.obs['sen_score'] = sp.score_hub(fib_subset, hubs.hubs[('Lung', 'fibroblast', 0)])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4904c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain</th>\n",
       "      <th>leiden</th>\n",
       "      <th>sen_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CGATCGGGTACTTAGC-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_CGATCGGGTACTTAGC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.683934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGCTATCAGCGGATCA-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_CGCTATCAGCGGATCA</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1.217730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGTCACTAGGCATGTG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_CGTCACTAGGCATGTG</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1839.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.199682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCAGGTATCCAGGGCT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_TCAGGTATCCAGGGCT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>6051.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.453288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGGACGAAA-1-12-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_51_AAAGATGAGGACGAAA</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-51</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.494737</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTCCTCAGAGGGCTT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTCCTCAGAGGGCTT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>6419.0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0.165665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGCGCGTTGATTGC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGCGCGTTGATTGC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.314077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGCGCTCTCCTATA-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGCGCTCTCCTATA</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>0.200124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2803.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.733920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>7172.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.031185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1523 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age                                 cell  \\\n",
       "index                                                                    \n",
       "CGATCGGGTACTTAGC-1-11-0-0     18m  MACA_18m_F_LUNG_50_CGATCGGGTACTTAGC   \n",
       "CGCTATCAGCGGATCA-1-11-0-0     18m  MACA_18m_F_LUNG_50_CGCTATCAGCGGATCA   \n",
       "CGTCACTAGGCATGTG-1-11-0-0     18m  MACA_18m_F_LUNG_50_CGTCACTAGGCATGTG   \n",
       "TCAGGTATCCAGGGCT-1-11-0-0     18m  MACA_18m_F_LUNG_50_TCAGGTATCCAGGGCT   \n",
       "AAAGATGAGGACGAAA-1-12-0-0     18m  MACA_18m_F_LUNG_51_AAAGATGAGGACGAAA   \n",
       "...                           ...                                  ...   \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1   3m           10X_P8_13_TTTCCTCAGAGGGCTT   \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1   3m           10X_P8_13_TTTGCGCGTTGATTGC   \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1   3m           10X_P8_13_TTTGCGCTCTCCTATA   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1   3m           10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1   3m           10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                             cell_ontology_class cell_ontology_id  \\\n",
       "index                                                               \n",
       "CGATCGGGTACTTAGC-1-11-0-0     fibroblast of lung               NA   \n",
       "CGCTATCAGCGGATCA-1-11-0-0     fibroblast of lung               NA   \n",
       "CGTCACTAGGCATGTG-1-11-0-0     fibroblast of lung               NA   \n",
       "TCAGGTATCCAGGGCT-1-11-0-0     fibroblast of lung               NA   \n",
       "AAAGATGAGGACGAAA-1-12-0-0     fibroblast of lung               NA   \n",
       "...                                          ...              ...   \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1  fibroblast of lung       CL:0000499   \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1  fibroblast of lung       CL:0000499   \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1  fibroblast of lung       CL:0000499   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  fibroblast of lung       CL:0000499   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  fibroblast of lung       CL:0000499   \n",
       "\n",
       "                                  free_annotation   method mouse.id  n_genes  \\\n",
       "index                                                                          \n",
       "CGATCGGGTACTTAGC-1-11-0-0     Alveolar Fibroblast  droplet  18-F-50   1561.0   \n",
       "CGCTATCAGCGGATCA-1-11-0-0     Alveolar Fibroblast  droplet  18-F-50   1705.0   \n",
       "CGTCACTAGGCATGTG-1-11-0-0     Alveolar Fibroblast  droplet  18-F-50   1839.0   \n",
       "TCAGGTATCCAGGGCT-1-11-0-0     Alveolar Fibroblast  droplet  18-F-50   2535.0   \n",
       "AAAGATGAGGACGAAA-1-12-0-0     Alveolar Fibroblast  droplet  18-F-51   1524.0   \n",
       "...                                           ...      ...      ...      ...   \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1  Alveolar Fibroblast  droplet  3-M-7/8      NaN   \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1  Alveolar Fibroblast  droplet  3-M-7/8      NaN   \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1  Alveolar Fibroblast  droplet  3-M-7/8      NaN   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  Alveolar Fibroblast  droplet  3-M-7/8      NaN   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  Alveolar Fibroblast  droplet  3-M-7/8      NaN   \n",
       "\n",
       "                                 sex             subtissue tissue  \\\n",
       "index                                                               \n",
       "CGATCGGGTACTTAGC-1-11-0-0     female                   nan   Lung   \n",
       "CGCTATCAGCGGATCA-1-11-0-0     female                   nan   Lung   \n",
       "CGTCACTAGGCATGTG-1-11-0-0     female                   nan   Lung   \n",
       "TCAGGTATCCAGGGCT-1-11-0-0     female                   nan   Lung   \n",
       "AAAGATGAGGACGAAA-1-12-0-0     female                   nan   Lung   \n",
       "...                              ...                   ...    ...   \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1    male  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1    male  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1    male  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1    male  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1    male  immune-endo-depleted   Lung   \n",
       "\n",
       "                             tissue_free_annotation  n_counts louvain leiden  \\\n",
       "index                                                                          \n",
       "CGATCGGGTACTTAGC-1-11-0-0                      Lung    3383.0      10     12   \n",
       "CGCTATCAGCGGATCA-1-11-0-0                      Lung    3039.0      10     12   \n",
       "CGTCACTAGGCATGTG-1-11-0-0                      Lung    3827.0      10     12   \n",
       "TCAGGTATCCAGGGCT-1-11-0-0                      Lung    6051.0      10     12   \n",
       "AAAGATGAGGACGAAA-1-12-0-0                      Lung    3115.0      10     12   \n",
       "...                                             ...       ...     ...    ...   \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1                   Lung    6419.0      10     17   \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1                   Lung    2661.0      10     12   \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1                   Lung    3699.0      19     17   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                   Lung    2803.0      10     12   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                   Lung    7172.0      19     17   \n",
       "\n",
       "                              sen_score  \n",
       "index                                    \n",
       "CGATCGGGTACTTAGC-1-11-0-0      0.683934  \n",
       "CGCTATCAGCGGATCA-1-11-0-0      1.217730  \n",
       "CGTCACTAGGCATGTG-1-11-0-0      0.199682  \n",
       "TCAGGTATCCAGGGCT-1-11-0-0      0.453288  \n",
       "AAAGATGAGGACGAAA-1-12-0-0      0.494737  \n",
       "...                                 ...  \n",
       "10X_P8_13_TTTCCTCAGAGGGCTT-1   0.165665  \n",
       "10X_P8_13_TTTGCGCGTTGATTGC-1   0.314077  \n",
       "10X_P8_13_TTTGCGCTCTCCTATA-1   0.200124  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1   0.733920  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  -0.031185  \n",
       "\n",
       "[1523 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib_subset.obs #we saved the score in the sen_score column of .obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e38d89e",
   "metadata": {},
   "source": [
    "### Score multiple cell types at once"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5c2eb2",
   "metadata": {},
   "source": [
    "If you want to apply one hub to all cells, we have a function that subsets the data based on identifiers prior to scoreing and returns a score for every cell. In the below example we don't have to subset the data like above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40fe6ad5",
   "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>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain</th>\n",
       "      <th>leiden</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACGGGTCGCCCTTA-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>5774.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCAGACTG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG</td>\n",
       "      <td>non-classical monocyte</td>\n",
       "      <td>NA</td>\n",
       "      <td>Nonclassical Monocyte</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3878.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCCGTCGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAACATGGTAGG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Macrophage</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>3322.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>13346.0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAAGTCTAGTGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2608.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTAGGGATACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTAGGGATACC</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2803.0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGGTTTCCTGTACC-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGGTTTCCTGTACC</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAAGAGTCGGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAAGAGTCGGT</td>\n",
       "      <td>pulmonary interstitial fibroblast</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Myofibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3041.0</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCACATATGAGA-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCACATATGAGA</td>\n",
       "      <td>adventitial cell</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Adventitial Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>7843.0</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10X_P8_13_TTTGTCAGTGGTCCGT-1</th>\n",
       "      <td>3m</td>\n",
       "      <td>10X_P8_13_TTTGTCAGTGGTCCGT</td>\n",
       "      <td>fibroblast of lung</td>\n",
       "      <td>CL:0000499</td>\n",
       "      <td>Alveolar Fibroblast</td>\n",
       "      <td>droplet</td>\n",
       "      <td>3-M-7/8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>immune-endo-depleted</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>7172.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24540 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age                                 cell  \\\n",
       "index                                                                    \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     18m  MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT   \n",
       "...                           ...                                  ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1   3m           10X_P8_13_TTTGGTTAGGGATACC   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1   3m           10X_P8_13_TTTGGTTTCCTGTACC   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1   3m           10X_P8_13_TTTGTCAAGAGTCGGT   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1   3m           10X_P8_13_TTTGTCACATATGAGA   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1   3m           10X_P8_13_TTTGTCAGTGGTCCGT   \n",
       "\n",
       "                                            cell_ontology_class  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                                B cell   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                non-classical monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                               NK cell   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                   alveolar macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                                B cell   \n",
       "...                                                         ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                 fibroblast of lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                            NK cell   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  pulmonary interstitial fibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   adventitial cell   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                 fibroblast of lung   \n",
       "\n",
       "                             cell_ontology_id         free_annotation  \\\n",
       "index                                                                   \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                  NA                       B   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                  NA   Nonclassical Monocyte   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                  NA          Natural Killer   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                  NA     Alveolar Macrophage   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                  NA                       B   \n",
       "...                                       ...                     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1       CL:0000499     Alveolar Fibroblast   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1       CL:0000623          Natural Killer   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1       CL:0000499           Myofibroblast   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1       CL:0000499  Adventitial Fibroblast   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1       CL:0000499     Alveolar Fibroblast   \n",
       "\n",
       "                               method mouse.id  n_genes     sex  \\\n",
       "index                                                             \n",
       "AAACGGGTCGCCCTTA-1-11-0-0     droplet  18-F-50   1821.0  female   \n",
       "AAAGATGAGCAGACTG-1-11-0-0     droplet  18-F-50   1333.0  female   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0     droplet  18-F-50   1455.0  female   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     droplet  18-F-50   3322.0  female   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0     droplet  18-F-50   1120.0  female   \n",
       "...                               ...      ...      ...     ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  droplet  3-M-7/8      NaN    male   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  droplet  3-M-7/8      NaN    male   \n",
       "\n",
       "                                         subtissue tissue  \\\n",
       "index                                                       \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      nan   Lung   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      nan   Lung   \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      nan   Lung   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      nan   Lung   \n",
       "...                                            ...    ...   \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCACATATGAGA-1  immune-endo-depleted   Lung   \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1  immune-endo-depleted   Lung   \n",
       "\n",
       "                             tissue_free_annotation  n_counts louvain leiden  \n",
       "index                                                                         \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      Lung    5774.0       4      3  \n",
       "AAAGATGAGCAGACTG-1-11-0-0                      Lung    3878.0       2      1  \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                      Lung    3990.0       7      5  \n",
       "AAAGCAACATGGTAGG-1-11-0-0                      Lung   13346.0       9      8  \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      Lung    2608.0       4      3  \n",
       "...                                             ...       ...     ...    ...  \n",
       "10X_P8_13_TTTGGTTAGGGATACC-1                   Lung    2803.0      10     12  \n",
       "10X_P8_13_TTTGGTTTCCTGTACC-1                   Lung    3049.0       7      5  \n",
       "10X_P8_13_TTTGTCAAGAGTCGGT-1                   Lung    3041.0      15     22  \n",
       "10X_P8_13_TTTGTCACATATGAGA-1                   Lung    7843.0      15     20  \n",
       "10X_P8_13_TTTGTCAGTGGTCCGT-1                   Lung    7172.0      19     17  \n",
       "\n",
       "[24540 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91df479e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 30/30 [00:23<00:00,  1.26it/s]\n"
     ]
    }
   ],
   "source": [
    "adata.obs['lung_fib_0_score'] = sp.score_all_cells(adata, hubs.hubs[('Lung', 'fibroblast', 0)],\n",
    "                   identifiers = ['cell_ontology_class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbaee628",
   "metadata": {},
   "source": [
    "We can pass multiple identifier levels to split the data further. For example, if you had multiple tissues and cell types you could include tissue along with cell type. In this example, we will split by mouse sex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a183a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 59/59 [00:41<00:00,  1.43it/s]\n"
     ]
    }
   ],
   "source": [
    "adata.obs['lung_fib_0_score_sex'] = sp.score_all_cells(adata, hubs.hubs[('Lung', 'fibroblast', 0)],\n",
    "                   identifiers = ['cell_ontology_class', 'sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "289f267c",
   "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>age</th>\n",
       "      <th>cell</th>\n",
       "      <th>cell_ontology_class</th>\n",
       "      <th>cell_ontology_id</th>\n",
       "      <th>free_annotation</th>\n",
       "      <th>method</th>\n",
       "      <th>mouse.id</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>sex</th>\n",
       "      <th>subtissue</th>\n",
       "      <th>tissue</th>\n",
       "      <th>tissue_free_annotation</th>\n",
       "      <th>n_counts</th>\n",
       "      <th>louvain</th>\n",
       "      <th>leiden</th>\n",
       "      <th>lung_fib_0_score</th>\n",
       "      <th>lung_fib_0_score_sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACGGGTCGCCCTTA-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>5774.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>11.128093</td>\n",
       "      <td>11.107001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCAGACTG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG</td>\n",
       "      <td>non-classical monocyte</td>\n",
       "      <td>NA</td>\n",
       "      <td>Nonclassical Monocyte</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3878.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.384672</td>\n",
       "      <td>6.385467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGATGAGCCGTCGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT</td>\n",
       "      <td>NK cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>Natural Killer</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6.748975</td>\n",
       "      <td>6.756562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAACATGGTAGG-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG</td>\n",
       "      <td>alveolar macrophage</td>\n",
       "      <td>NA</td>\n",
       "      <td>Alveolar Macrophage</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>3322.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>13346.0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>11.930262</td>\n",
       "      <td>11.883487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAGCAAGTCTAGTGT-1-11-0-0</th>\n",
       "      <td>18m</td>\n",
       "      <td>MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT</td>\n",
       "      <td>B cell</td>\n",
       "      <td>NA</td>\n",
       "      <td>B</td>\n",
       "      <td>droplet</td>\n",
       "      <td>18-F-50</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>female</td>\n",
       "      <td>nan</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>2608.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7.567527</td>\n",
       "      <td>7.571177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age                                 cell  \\\n",
       "index                                                                 \n",
       "AAACGGGTCGCCCTTA-1-11-0-0  18m  MACA_18m_F_LUNG_50_AAACGGGTCGCCCTTA   \n",
       "AAAGATGAGCAGACTG-1-11-0-0  18m  MACA_18m_F_LUNG_50_AAAGATGAGCAGACTG   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0  18m  MACA_18m_F_LUNG_50_AAAGATGAGCCGTCGT   \n",
       "AAAGCAACATGGTAGG-1-11-0-0  18m  MACA_18m_F_LUNG_50_AAAGCAACATGGTAGG   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0  18m  MACA_18m_F_LUNG_50_AAAGCAAGTCTAGTGT   \n",
       "\n",
       "                              cell_ontology_class cell_ontology_id  \\\n",
       "index                                                                \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                  B cell               NA   \n",
       "AAAGATGAGCAGACTG-1-11-0-0  non-classical monocyte               NA   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0                 NK cell               NA   \n",
       "AAAGCAACATGGTAGG-1-11-0-0     alveolar macrophage               NA   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                  B cell               NA   \n",
       "\n",
       "                                 free_annotation   method mouse.id  n_genes  \\\n",
       "index                                                                         \n",
       "AAACGGGTCGCCCTTA-1-11-0-0                      B  droplet  18-F-50   1821.0   \n",
       "AAAGATGAGCAGACTG-1-11-0-0  Nonclassical Monocyte  droplet  18-F-50   1333.0   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0         Natural Killer  droplet  18-F-50   1455.0   \n",
       "AAAGCAACATGGTAGG-1-11-0-0    Alveolar Macrophage  droplet  18-F-50   3322.0   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0                      B  droplet  18-F-50   1120.0   \n",
       "\n",
       "                              sex subtissue tissue tissue_free_annotation  \\\n",
       "index                                                                       \n",
       "AAACGGGTCGCCCTTA-1-11-0-0  female       nan   Lung                   Lung   \n",
       "AAAGATGAGCAGACTG-1-11-0-0  female       nan   Lung                   Lung   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0  female       nan   Lung                   Lung   \n",
       "AAAGCAACATGGTAGG-1-11-0-0  female       nan   Lung                   Lung   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0  female       nan   Lung                   Lung   \n",
       "\n",
       "                           n_counts louvain leiden  lung_fib_0_score  \\\n",
       "index                                                                  \n",
       "AAACGGGTCGCCCTTA-1-11-0-0    5774.0       4      3         11.128093   \n",
       "AAAGATGAGCAGACTG-1-11-0-0    3878.0       2      1          6.384672   \n",
       "AAAGATGAGCCGTCGT-1-11-0-0    3990.0       7      5          6.748975   \n",
       "AAAGCAACATGGTAGG-1-11-0-0   13346.0       9      8         11.930262   \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0    2608.0       4      3          7.567527   \n",
       "\n",
       "                           lung_fib_0_score_sex  \n",
       "index                                            \n",
       "AAACGGGTCGCCCTTA-1-11-0-0             11.107001  \n",
       "AAAGATGAGCAGACTG-1-11-0-0              6.385467  \n",
       "AAAGATGAGCCGTCGT-1-11-0-0              6.756562  \n",
       "AAAGCAACATGGTAGG-1-11-0-0             11.883487  \n",
       "AAAGCAAGTCTAGTGT-1-11-0-0              7.571177  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb538ae3",
   "metadata": {},
   "source": [
    "### Example analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "17a231dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fib_subset.obs.sen_score.hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e6874fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(fib_subset, color='sen_score', vmax = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fa5706e6",
   "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>cell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1m</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3m</th>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18m</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21m</th>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30m</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cell\n",
       "age      \n",
       "1m     87\n",
       "3m   1143\n",
       "18m   166\n",
       "21m   101\n",
       "30m    26"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fib_subset.obs.groupby('age').count()[['cell']] #number of fibroblasts at each age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf6a804",
   "metadata": {},
   "source": [
    "Let's set a threshold for what we might consider putative senescent cells. The threshold will change depending\n",
    "on your testing conditions so we have left that up to the researcher. For example, if you enrich for senescent cells prior to sequencing you would expect more to be contributing to your distribution. In natural aging, since we still expect senescent cells to be in the minority, we have found that the mean plus 3 standard deviations to work well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "eb352080",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = fib_subset.obs.sen_score.mean() #distribution mean\n",
    "std = fib_subset.obs.sen_score.std() #distribution std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0ea56df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.884478992612143"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh = e + 3*std\n",
    "thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "dbacff5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to add senescent label\n",
    "def is_putative_sen(x):\n",
    "    if x >= thresh:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "    \n",
    "#map function to a new row in adata.obs\n",
    "fib_subset.obs['putative_sen'] = fib_subset.obs.sen_score.map(is_putative_sen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "37095721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(fib_subset, color='putative_sen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0f8b945a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare % of sen cells at 3m vs 21m\n",
    "m3_sub = fib_subset[fib_subset.obs.age == '3m']\n",
    "m21_sub = fib_subset[fib_subset.obs.age == '21m']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9a2f986d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6124234470691163"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3_sub.obs.putative_sen.sum() / len(m3_sub) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "43bd9248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.871287128712872"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m21_sub.obs.putative_sen.sum() / len(m21_sub) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb628ad",
   "metadata": {},
   "source": [
    "Note: Doing this direct percentage comparison is not advisable if the number of cells for a given group is small. E.g., if you have 20 cells in your data and none of them were senescent, you can't say that 0% of the cells in the actual population were senescent. However, you can still find senescent cells in smaller populations and explore their transcriptomes in different ways! Now that the senescent cells are labeled you can explore them further in great detail. DE analysis, TF analysis, regulatory networks, coexpression, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fba93dfc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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