api/python/notebooks/api_demo/census_dataset_presence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Genes measured in each cell (dataset presence matrix)\n",
    "\n",
    "The Census is a compilation of cells from multiple datasets that may differ by the sets of genes they measure. This notebook describes the way to identify the genes measured per dataset.\n",
    "\n",
    "The presence matrix is a sparse boolean array, indicating which features (var) were present in each dataset.  The array has dimensions [n_datasets, n_var], and is stored in the SOMA Measurement `varp` collection. The first dimension is indexed by the `soma_joinid` in the `census_datasets` dataframe. The second is indexed by the `soma_joinid` in the `var` dataframe of the measurement.\n",
    "\n",
    "As a reminder the `obs` data frame has a column `dataset_id` that can be used to link any cell in the Census to the presence matrix.\n",
    "\n",
    "**Contents** \n",
    "\n",
    "1. Opening the Census.\n",
    "2. Fetching the IDs of the Census datasets.\n",
    "3. Fetching the dataset presence matrix.\n",
    "4. Identifying genes measured in a specific dataset.\n",
    "5. Identifying datasets that measured specific genes.\n",
    "6. Identifying all genes measured in a dataset.\n",
    "\n",
    "⚠️ Note that the Census RNA data includes duplicate cells present across multiple datasets. Duplicate cells can be filtered in or out using the cell metadata variable `is_primary_data` which is described in the [Census schema](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md#repeated-data).\n",
    "\n",
    "## Opening the Census\n",
    "\n",
    "The `cellxgene_census` python package contains a convenient API to open the latest version of the Census."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The \"stable\" release is currently 2023-07-25. Specify 'census_version=\"2023-07-25\"' in future calls to open_soma() to ensure data consistency.\n"
     ]
    }
   ],
   "source": [
    "import cellxgene_census\n",
    "\n",
    "census = cellxgene_census.open_soma()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching the IDs of the Census datasets\n",
    "\n",
    "Let's grab a table of all the datasets included in the Census and use this table in combination with the presence matrix below."
   ]
  },
  {
   "cell_type": "code",
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       "      <th></th>\n",
       "      <th>soma_joinid</th>\n",
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       "     soma_joinid                         collection_id  \\\n",
       "0              0  e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
       "1              1  e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
       "2              2  e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
       "3              3  f7cecffa-00b4-4560-a29a-8ad626b8ee08   \n",
       "4              4  3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c   \n",
       "..           ...                                   ...   \n",
       "588          588  180bff9c-c8a5-4539-b13b-ddbc00d643e6   \n",
       "589          589  a72afd53-ab92-4511-88da-252fb0e26b9a   \n",
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       "592          592  5d445965-6f1a-4b68-ba3a-b8f765155d3a   \n",
       "\n",
       "                                       collection_name  \\\n",
       "0    Spatial multiomics map of trophoblast developm...   \n",
       "1    Spatial multiomics map of trophoblast developm...   \n",
       "2    Spatial multiomics map of trophoblast developm...   \n",
       "3    Mapping single-cell transcriptomes in the intr...   \n",
       "4    Single-cell sequencing links multiregional imm...   \n",
       "..                                                 ...   \n",
       "588  Molecular characterization of selectively vuln...   \n",
       "589  Single-cell atlas of peripheral immune respons...   \n",
       "590  Construction of a human cell landscape at sing...   \n",
       "591  A molecular cell atlas of the human lung from ...   \n",
       "592  A molecular cell atlas of the human lung from ...   \n",
       "\n",
       "                  collection_doi                            dataset_id  \\\n",
       "0     10.1038/s41586-023-05869-0  f171db61-e57e-4535-a06a-35d8b6ef8f2b   \n",
       "1     10.1038/s41586-023-05869-0  ecf2e08e-2032-4a9e-b466-b65b395f4a02   \n",
       "2     10.1038/s41586-023-05869-0  74cff64f-9da9-4b2a-9b3b-8a04a1598040   \n",
       "3    10.1016/j.ccell.2022.11.001  5af90777-6760-4003-9dba-8f945fec6fdf   \n",
       "4    10.1016/j.ccell.2021.03.007  bd65a70f-b274-4133-b9dd-0d1431b6af34   \n",
       "..                           ...                                   ...   \n",
       "588   10.1038/s41593-020-00764-7  f9ad5649-f372-43e1-a3a8-423383e5a8a2   \n",
       "589    10.1038/s41591-020-0944-y  456e8b9b-f872-488b-871d-94534090a865   \n",
       "590    10.1038/s41586-020-2157-4  2adb1f8a-a6b1-4909-8ee8-484814e2d4bf   \n",
       "591    10.1038/s41586-020-2922-4  e04daea4-4412-45b5-989e-76a9be070a89   \n",
       "592    10.1038/s41586-020-2922-4  8c42cfd0-0b0a-46d5-910c-fc833d83c45e   \n",
       "\n",
       "                                         dataset_title  \\\n",
       "0                               donor_p13_trophoblasts   \n",
       "1                              All donors trophoblasts   \n",
       "2                 All donors all cell states (in vivo)   \n",
       "3    Single-cell transcriptomic datasets of Renal c...   \n",
       "4    Single-cell sequencing links multiregional imm...   \n",
       "..                                                 ...   \n",
       "588  Molecular characterization of selectively vuln...   \n",
       "589  Single-cell atlas of peripheral immune respons...   \n",
       "590  Construction of a human cell landscape at sing...   \n",
       "591      Krasnow Lab Human Lung Cell Atlas, Smart-seq2   \n",
       "592             Krasnow Lab Human Lung Cell Atlas, 10X   \n",
       "\n",
       "                             dataset_h5ad_path  dataset_total_cell_count  \n",
       "0    f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad                     31497  \n",
       "1    ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad                     67070  \n",
       "2    74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad                    286326  \n",
       "3    5af90777-6760-4003-9dba-8f945fec6fdf.h5ad                    270855  \n",
       "4    bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad                    167283  \n",
       "..                                         ...                       ...  \n",
       "588  f9ad5649-f372-43e1-a3a8-423383e5a8a2.h5ad                      8168  \n",
       "589  456e8b9b-f872-488b-871d-94534090a865.h5ad                     44721  \n",
       "590  2adb1f8a-a6b1-4909-8ee8-484814e2d4bf.h5ad                    598266  \n",
       "591  e04daea4-4412-45b5-989e-76a9be070a89.h5ad                      9409  \n",
       "592  8c42cfd0-0b0a-46d5-910c-fc833d83c45e.h5ad                     65662  \n",
       "\n",
       "[593 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab the experiment containing human data, and the measurement therein with RNA\n",
    "human = census[\"census_data\"][\"homo_sapiens\"]\n",
    "human_rna = human.ms[\"RNA\"]\n",
    "\n",
    "# The census-wide datasets\n",
    "datasets_df = census[\"census_info\"][\"datasets\"].read().concat().to_pandas()\n",
    "\n",
    "datasets_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching the dataset presence matrix\n",
    "\n",
    "Now let's fetch the dataset presence matrix. \n",
    "\n",
    "For convenience, read the entire presence matrix (for Homo sapiens) into a SciPy array. There is a convenience API providing this capability, returning the matrix in a `scipy.sparse.array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "execution": {
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     "shell.execute_reply": "2023-07-28T14:31:46.884057Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<593x60664 sparse matrix of type '<class 'numpy.uint8'>'\n",
       "\twith 16133717 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "presence_matrix = cellxgene_census.get_presence_matrix(census, organism=\"Homo sapiens\", measurement_name=\"RNA\")\n",
    "\n",
    "presence_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need the `var` dataframe, which is read into a Pandas DataFrame for convenient manipulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th>60660</th>\n",
       "      <td>60660</td>\n",
       "      <td>ENSG00000288720</td>\n",
       "      <td>RP11-852E15.3</td>\n",
       "      <td>7007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60661</th>\n",
       "      <td>60661</td>\n",
       "      <td>ENSG00000288721</td>\n",
       "      <td>RP5-973N23.5</td>\n",
       "      <td>7765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60662</th>\n",
       "      <td>60662</td>\n",
       "      <td>ENSG00000288723</td>\n",
       "      <td>RP11-553N16.6</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60663</th>\n",
       "      <td>60663</td>\n",
       "      <td>ENSG00000288724</td>\n",
       "      <td>RP13-546I2.2</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60664 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       soma_joinid       feature_id   feature_name  feature_length\n",
       "0                0  ENSG00000121410           A1BG            3999\n",
       "1                1  ENSG00000268895       A1BG-AS1            3374\n",
       "2                2  ENSG00000148584           A1CF            9603\n",
       "3                3  ENSG00000175899            A2M            6318\n",
       "4                4  ENSG00000245105        A2M-AS1            2948\n",
       "...            ...              ...            ...             ...\n",
       "60659        60659  ENSG00000288719  RP4-669P10.21            4252\n",
       "60660        60660  ENSG00000288720  RP11-852E15.3            7007\n",
       "60661        60661  ENSG00000288721   RP5-973N23.5            7765\n",
       "60662        60662  ENSG00000288723  RP11-553N16.6            1015\n",
       "60663        60663  ENSG00000288724   RP13-546I2.2             625\n",
       "\n",
       "[60664 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_df = human_rna.var.read().concat().to_pandas()\n",
    "\n",
    "var_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying genes measured in a specific dataset.\n",
    "\n",
    "Now that we have the dataset table, the genes metadata table, and the dataset presence matrix, we can check if a gene or set of genes were measured in a specific dataset.\n",
    "\n",
    "**Important:** the presence matrix is indexed by soma_joinid, and is *NOT* positionally indexed.  In other words:\n",
    "\n",
    "* the first dimension of the presence matrix is the dataset's `soma_joinid`, as stored in the `census_datasets` dataframe.\n",
    "* the second dimension of the presence matrix is the feature's `soma_joinid`, as stored in the `var` dataframe.\n",
    "\n",
    "Let's find out if the the gene `\"ENSG00000286096\"` was measured in the dataset with id `\"97a17473-e2b1-4f31-a544-44a60773e2dd\"`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-28T14:31:47.359369Z",
     "iopub.status.busy": "2023-07-28T14:31:47.359100Z",
     "iopub.status.idle": "2023-07-28T14:31:47.368533Z",
     "shell.execute_reply": "2023-07-28T14:31:47.368030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature is present.\n"
     ]
    }
   ],
   "source": [
    "var_joinid = var_df.loc[var_df.feature_id == \"ENSG00000286096\"].soma_joinid\n",
    "dataset_joinid = datasets_df.loc[datasets_df.dataset_id == \"97a17473-e2b1-4f31-a544-44a60773e2dd\"].soma_joinid\n",
    "is_present = presence_matrix[dataset_joinid, var_joinid][0, 0]\n",
    "print(f'Feature is {\"present\" if is_present else \"not present\"}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying datasets that measured specific genes\n",
    "\n",
    "Similarly, we can determine the datasets that measured a specific gene or set of genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-28T14:31:47.370759Z",
     "iopub.status.busy": "2023-07-28T14:31:47.370521Z",
     "iopub.status.idle": "2023-07-28T14:31:47.417709Z",
     "shell.execute_reply": "2023-07-28T14:31:47.417245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>soma_joinid</th>\n",
       "      <th>collection_id</th>\n",
       "      <th>collection_name</th>\n",
       "      <th>collection_doi</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>dataset_title</th>\n",
       "      <th>dataset_h5ad_path</th>\n",
       "      <th>dataset_total_cell_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>ff45e623-7f5f-46e3-b47d-56be0341f66b</td>\n",
       "      <td>Tabula Sapiens - Pancreas</td>\n",
       "      <td>ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad</td>\n",
       "      <td>13497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>f01bdd17-4902-40f5-86e3-240d66dd2587</td>\n",
       "      <td>Tabula Sapiens - Salivary_Gland</td>\n",
       "      <td>f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad</td>\n",
       "      <td>27199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>e6a11140-2545-46bc-929e-da243eed2cae</td>\n",
       "      <td>Tabula Sapiens - Heart</td>\n",
       "      <td>e6a11140-2545-46bc-929e-da243eed2cae.h5ad</td>\n",
       "      <td>11505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>e5c63d94-593c-4338-a489-e1048599e751</td>\n",
       "      <td>Tabula Sapiens - Bladder</td>\n",
       "      <td>e5c63d94-593c-4338-a489-e1048599e751.h5ad</td>\n",
       "      <td>24583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>d8732da6-8d1d-42d9-b625-f2416c30054b</td>\n",
       "      <td>Tabula Sapiens - Trachea</td>\n",
       "      <td>d8732da6-8d1d-42d9-b625-f2416c30054b.h5ad</td>\n",
       "      <td>9522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>cee11228-9f0b-4e57-afe2-cfe15ee56312</td>\n",
       "      <td>Tabula Sapiens - Spleen</td>\n",
       "      <td>cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad</td>\n",
       "      <td>34004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>a357414d-2042-4eb5-95f0-c58604a18bdd</td>\n",
       "      <td>Tabula Sapiens - Small_Intestine</td>\n",
       "      <td>a357414d-2042-4eb5-95f0-c58604a18bdd.h5ad</td>\n",
       "      <td>12467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>a0754256-f44b-4c4a-962c-a552e47d3fdc</td>\n",
       "      <td>Tabula Sapiens - Eye</td>\n",
       "      <td>a0754256-f44b-4c4a-962c-a552e47d3fdc.h5ad</td>\n",
       "      <td>10650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>983d5ec9-40e8-4512-9e65-a572a9c486cb</td>\n",
       "      <td>Tabula Sapiens - Blood</td>\n",
       "      <td>983d5ec9-40e8-4512-9e65-a572a9c486cb.h5ad</td>\n",
       "      <td>50115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c</td>\n",
       "      <td>Tabula Sapiens - Fat</td>\n",
       "      <td>5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c.h5ad</td>\n",
       "      <td>20263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>55cf0ea3-9d2b-4294-871e-bb4b49a79fc7</td>\n",
       "      <td>Tabula Sapiens - Tongue</td>\n",
       "      <td>55cf0ea3-9d2b-4294-871e-bb4b49a79fc7.h5ad</td>\n",
       "      <td>15020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>4f1555bc-4664-46c3-a606-78d34dd10d92</td>\n",
       "      <td>Tabula Sapiens - Bone_Marrow</td>\n",
       "      <td>4f1555bc-4664-46c3-a606-78d34dd10d92.h5ad</td>\n",
       "      <td>12297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>2423ce2c-3149-4cca-a2ff-cf682ea29b5f</td>\n",
       "      <td>Tabula Sapiens - Kidney</td>\n",
       "      <td>2423ce2c-3149-4cca-a2ff-cf682ea29b5f.h5ad</td>\n",
       "      <td>9641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>1c9eb291-6d31-47e1-96b2-129b5e1ae64f</td>\n",
       "      <td>Tabula Sapiens - Muscle</td>\n",
       "      <td>1c9eb291-6d31-47e1-96b2-129b5e1ae64f.h5ad</td>\n",
       "      <td>30746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>18eb630b-a754-4111-8cd4-c24ec80aa5ec</td>\n",
       "      <td>Tabula Sapiens - Lymph_Node</td>\n",
       "      <td>18eb630b-a754-4111-8cd4-c24ec80aa5ec.h5ad</td>\n",
       "      <td>53275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>0d2ee4ac-05ee-40b2-afb6-ebb584caa867</td>\n",
       "      <td>Tabula Sapiens - Lung</td>\n",
       "      <td>0d2ee4ac-05ee-40b2-afb6-ebb584caa867.h5ad</td>\n",
       "      <td>35682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>0ced5e76-6040-47ff-8a72-93847965afc0</td>\n",
       "      <td>Tabula Sapiens - Thymus</td>\n",
       "      <td>0ced5e76-6040-47ff-8a72-93847965afc0.h5ad</td>\n",
       "      <td>33664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>8e10f1c4-8e98-41e5-b65f-8cd89a887122</td>\n",
       "      <td>All neurons</td>\n",
       "      <td>8e10f1c4-8e98-41e5-b65f-8cd89a887122.h5ad</td>\n",
       "      <td>2480956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>fe1a73ab-a203-45fd-84e9-0f7fd19efcbd</td>\n",
       "      <td>Dissection: Amygdaloid complex (AMY) - basolat...</td>\n",
       "      <td>fe1a73ab-a203-45fd-84e9-0f7fd19efcbd.h5ad</td>\n",
       "      <td>35285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>f8dda921-5fb4-4c94-a654-c6fc346bfd6d</td>\n",
       "      <td>Dissection: Cerebral cortex (Cx) - Occipitotem...</td>\n",
       "      <td>f8dda921-5fb4-4c94-a654-c6fc346bfd6d.h5ad</td>\n",
       "      <td>31899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>dd03ce70-3243-4c96-9561-330cc461e4d7</td>\n",
       "      <td>Dissection: Cerebral cortex (Cx) - Perirhinal ...</td>\n",
       "      <td>dd03ce70-3243-4c96-9561-330cc461e4d7.h5ad</td>\n",
       "      <td>23732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>165</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>d2b5efc1-14c6-4b5f-bd98-40f9084872d7</td>\n",
       "      <td>Dissection: Tail of Hippocampus (HiT) - Caudal...</td>\n",
       "      <td>d2b5efc1-14c6-4b5f-bd98-40f9084872d7.h5ad</td>\n",
       "      <td>36886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>175</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>c4b03352-af8d-492a-8d6b-40f304e0a122</td>\n",
       "      <td>Supercluster: Medium spiny neuron</td>\n",
       "      <td>c4b03352-af8d-492a-8d6b-40f304e0a122.h5ad</td>\n",
       "      <td>152189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>176</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>c2aad8fc-b63b-4f9b-9cfd-baf7bc9c1771</td>\n",
       "      <td>Dissection: Cerebral cortex (Cx) - Temporal po...</td>\n",
       "      <td>c2aad8fc-b63b-4f9b-9cfd-baf7bc9c1771.h5ad</td>\n",
       "      <td>37642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>177</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>c202b243-1aa1-4b16-bc9a-b36241f3b1e3</td>\n",
       "      <td>Supercluster: Amygdala excitatory</td>\n",
       "      <td>c202b243-1aa1-4b16-bc9a-b36241f3b1e3.h5ad</td>\n",
       "      <td>109452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>178</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>bdb26abd-f4ba-4ea3-8862-c2340e7a4f55</td>\n",
       "      <td>Supercluster: CGE interneuron</td>\n",
       "      <td>bdb26abd-f4ba-4ea3-8862-c2340e7a4f55.h5ad</td>\n",
       "      <td>227671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>183</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>acae7679-d077-461c-b857-ee6ccfeb267f</td>\n",
       "      <td>Dissection: Head of hippocampus (HiH) - CA1</td>\n",
       "      <td>acae7679-d077-461c-b857-ee6ccfeb267f.h5ad</td>\n",
       "      <td>39147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>9372df2d-13d6-4fac-980b-919a5b7eb483</td>\n",
       "      <td>Dissection: Midbrain (M) - Periaqueductal gray...</td>\n",
       "      <td>9372df2d-13d6-4fac-980b-919a5b7eb483.h5ad</td>\n",
       "      <td>33794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>93131426-0124-4ab4-a013-9dfbcd99d467</td>\n",
       "      <td>Dissection: Epithalamus - ETH</td>\n",
       "      <td>93131426-0124-4ab4-a013-9dfbcd99d467.h5ad</td>\n",
       "      <td>24327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>206</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>7c1c3d47-3166-43e5-9a95-65ceb2d45f78</td>\n",
       "      <td>Dissection: Pons (Pn) - Pontine reticular form...</td>\n",
       "      <td>7c1c3d47-3166-43e5-9a95-65ceb2d45f78.h5ad</td>\n",
       "      <td>49512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>208</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>7a0a8891-9a22-4549-a55b-c2aca23c3a2a</td>\n",
       "      <td>Supercluster: Hippocampal CA1-3</td>\n",
       "      <td>7a0a8891-9a22-4549-a55b-c2aca23c3a2a.h5ad</td>\n",
       "      <td>74979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>220</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>5e5ab909-f73f-4b57-98a0-6d2c5662f6a4</td>\n",
       "      <td>Dissection: Midbrain (M) - Inferior colliculus...</td>\n",
       "      <td>5e5ab909-f73f-4b57-98a0-6d2c5662f6a4.h5ad</td>\n",
       "      <td>32306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>243</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>3f56901c-dd4a-47d6-b60b-7b0c0111cfb2</td>\n",
       "      <td>Dissection: Head of hippocampus (HiH) - CA1-3</td>\n",
       "      <td>3f56901c-dd4a-47d6-b60b-7b0c0111cfb2.h5ad</td>\n",
       "      <td>37911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>245</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>3a7f3ab4-a280-4b3b-b2c0-6dd05614a78c</td>\n",
       "      <td>Supercluster: Splatter</td>\n",
       "      <td>3a7f3ab4-a280-4b3b-b2c0-6dd05614a78c.h5ad</td>\n",
       "      <td>291833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>35c8a04c-8639-4d15-8228-765d8d93fc96</td>\n",
       "      <td>Dissection: Hypothalamus (HTH) - supraoptic re...</td>\n",
       "      <td>35c8a04c-8639-4d15-8228-765d8d93fc96.h5ad</td>\n",
       "      <td>16753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>270</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>07b1d7c8-5c2e-42f7-9246-26f746cd6013</td>\n",
       "      <td>Dissection: Myelencephalon (medulla oblongata)...</td>\n",
       "      <td>07b1d7c8-5c2e-42f7-9246-26f746cd6013.h5ad</td>\n",
       "      <td>27210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>273</td>\n",
       "      <td>283d65eb-dd53-496d-adb7-7570c7caa443</td>\n",
       "      <td>Transcriptomic diversity of cell types across ...</td>\n",
       "      <td>10.1101/2022.10.12.511898</td>\n",
       "      <td>0325478a-9b52-45b5-b40a-2e2ab0d72eb1</td>\n",
       "      <td>Supercluster: Upper-layer intratelencephalic</td>\n",
       "      <td>0325478a-9b52-45b5-b40a-2e2ab0d72eb1.h5ad</td>\n",
       "      <td>455006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>475</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>53d208b0-2cfd-4366-9866-c3c6114081bc</td>\n",
       "      <td>Tabula Sapiens - All Cells</td>\n",
       "      <td>53d208b0-2cfd-4366-9866-c3c6114081bc.h5ad</td>\n",
       "      <td>483152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>476</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>a68b64d8-aee3-4947-81b7-36b8fe5a44d2</td>\n",
       "      <td>Tabula Sapiens - Stromal</td>\n",
       "      <td>a68b64d8-aee3-4947-81b7-36b8fe5a44d2.h5ad</td>\n",
       "      <td>82478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>477</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>c5d88abe-f23a-45fa-a534-788985e93dad</td>\n",
       "      <td>Tabula Sapiens - Immune</td>\n",
       "      <td>c5d88abe-f23a-45fa-a534-788985e93dad.h5ad</td>\n",
       "      <td>264824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>478</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>5a11f879-d1ef-458a-910c-9b0bdfca5ebf</td>\n",
       "      <td>Tabula Sapiens - Endothelial</td>\n",
       "      <td>5a11f879-d1ef-458a-910c-9b0bdfca5ebf.h5ad</td>\n",
       "      <td>31691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>479</td>\n",
       "      <td>e5f58829-1a66-40b5-a624-9046778e74f5</td>\n",
       "      <td>Tabula Sapiens</td>\n",
       "      <td>10.1126/science.abl4896</td>\n",
       "      <td>97a17473-e2b1-4f31-a544-44a60773e2dd</td>\n",
       "      <td>Tabula Sapiens - Epithelial</td>\n",
       "      <td>97a17473-e2b1-4f31-a544-44a60773e2dd.h5ad</td>\n",
       "      <td>104148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     soma_joinid                         collection_id  \\\n",
       "5              5  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "6              6  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "7              7  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "8              8  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "9              9  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "11            11  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "12            12  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "14            14  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "15            15  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "19            19  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "20            20  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "21            21  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "23            23  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "24            24  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "25            25  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "26            26  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "27            27  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "43            43  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "139          139  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "143          143  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "160          160  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "165          165  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "175          175  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "176          176  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "177          177  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "178          178  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "183          183  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "196          196  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "197          197  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "206          206  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "208          208  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "220          220  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "243          243  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "245          245  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "249          249  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "270          270  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "273          273  283d65eb-dd53-496d-adb7-7570c7caa443   \n",
       "475          475  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "476          476  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "477          477  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "478          478  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "479          479  e5f58829-1a66-40b5-a624-9046778e74f5   \n",
       "\n",
       "                                       collection_name  \\\n",
       "5                                       Tabula Sapiens   \n",
       "6                                       Tabula Sapiens   \n",
       "7                                       Tabula Sapiens   \n",
       "8                                       Tabula Sapiens   \n",
       "9                                       Tabula Sapiens   \n",
       "11                                      Tabula Sapiens   \n",
       "12                                      Tabula Sapiens   \n",
       "14                                      Tabula Sapiens   \n",
       "15                                      Tabula Sapiens   \n",
       "19                                      Tabula Sapiens   \n",
       "20                                      Tabula Sapiens   \n",
       "21                                      Tabula Sapiens   \n",
       "23                                      Tabula Sapiens   \n",
       "24                                      Tabula Sapiens   \n",
       "25                                      Tabula Sapiens   \n",
       "26                                      Tabula Sapiens   \n",
       "27                                      Tabula Sapiens   \n",
       "43   Transcriptomic diversity of cell types across ...   \n",
       "139  Transcriptomic diversity of cell types across ...   \n",
       "143  Transcriptomic diversity of cell types across ...   \n",
       "160  Transcriptomic diversity of cell types across ...   \n",
       "165  Transcriptomic diversity of cell types across ...   \n",
       "175  Transcriptomic diversity of cell types across ...   \n",
       "176  Transcriptomic diversity of cell types across ...   \n",
       "177  Transcriptomic diversity of cell types across ...   \n",
       "178  Transcriptomic diversity of cell types across ...   \n",
       "183  Transcriptomic diversity of cell types across ...   \n",
       "196  Transcriptomic diversity of cell types across ...   \n",
       "197  Transcriptomic diversity of cell types across ...   \n",
       "206  Transcriptomic diversity of cell types across ...   \n",
       "208  Transcriptomic diversity of cell types across ...   \n",
       "220  Transcriptomic diversity of cell types across ...   \n",
       "243  Transcriptomic diversity of cell types across ...   \n",
       "245  Transcriptomic diversity of cell types across ...   \n",
       "249  Transcriptomic diversity of cell types across ...   \n",
       "270  Transcriptomic diversity of cell types across ...   \n",
       "273  Transcriptomic diversity of cell types across ...   \n",
       "475                                     Tabula Sapiens   \n",
       "476                                     Tabula Sapiens   \n",
       "477                                     Tabula Sapiens   \n",
       "478                                     Tabula Sapiens   \n",
       "479                                     Tabula Sapiens   \n",
       "\n",
       "                collection_doi                            dataset_id  \\\n",
       "5      10.1126/science.abl4896  ff45e623-7f5f-46e3-b47d-56be0341f66b   \n",
       "6      10.1126/science.abl4896  f01bdd17-4902-40f5-86e3-240d66dd2587   \n",
       "7      10.1126/science.abl4896  e6a11140-2545-46bc-929e-da243eed2cae   \n",
       "8      10.1126/science.abl4896  e5c63d94-593c-4338-a489-e1048599e751   \n",
       "9      10.1126/science.abl4896  d8732da6-8d1d-42d9-b625-f2416c30054b   \n",
       "11     10.1126/science.abl4896  cee11228-9f0b-4e57-afe2-cfe15ee56312   \n",
       "12     10.1126/science.abl4896  a357414d-2042-4eb5-95f0-c58604a18bdd   \n",
       "14     10.1126/science.abl4896  a0754256-f44b-4c4a-962c-a552e47d3fdc   \n",
       "15     10.1126/science.abl4896  983d5ec9-40e8-4512-9e65-a572a9c486cb   \n",
       "19     10.1126/science.abl4896  5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c   \n",
       "20     10.1126/science.abl4896  55cf0ea3-9d2b-4294-871e-bb4b49a79fc7   \n",
       "21     10.1126/science.abl4896  4f1555bc-4664-46c3-a606-78d34dd10d92   \n",
       "23     10.1126/science.abl4896  2423ce2c-3149-4cca-a2ff-cf682ea29b5f   \n",
       "24     10.1126/science.abl4896  1c9eb291-6d31-47e1-96b2-129b5e1ae64f   \n",
       "25     10.1126/science.abl4896  18eb630b-a754-4111-8cd4-c24ec80aa5ec   \n",
       "26     10.1126/science.abl4896  0d2ee4ac-05ee-40b2-afb6-ebb584caa867   \n",
       "27     10.1126/science.abl4896  0ced5e76-6040-47ff-8a72-93847965afc0   \n",
       "43   10.1101/2022.10.12.511898  8e10f1c4-8e98-41e5-b65f-8cd89a887122   \n",
       "139  10.1101/2022.10.12.511898  fe1a73ab-a203-45fd-84e9-0f7fd19efcbd   \n",
       "143  10.1101/2022.10.12.511898  f8dda921-5fb4-4c94-a654-c6fc346bfd6d   \n",
       "160  10.1101/2022.10.12.511898  dd03ce70-3243-4c96-9561-330cc461e4d7   \n",
       "165  10.1101/2022.10.12.511898  d2b5efc1-14c6-4b5f-bd98-40f9084872d7   \n",
       "175  10.1101/2022.10.12.511898  c4b03352-af8d-492a-8d6b-40f304e0a122   \n",
       "176  10.1101/2022.10.12.511898  c2aad8fc-b63b-4f9b-9cfd-baf7bc9c1771   \n",
       "177  10.1101/2022.10.12.511898  c202b243-1aa1-4b16-bc9a-b36241f3b1e3   \n",
       "178  10.1101/2022.10.12.511898  bdb26abd-f4ba-4ea3-8862-c2340e7a4f55   \n",
       "183  10.1101/2022.10.12.511898  acae7679-d077-461c-b857-ee6ccfeb267f   \n",
       "196  10.1101/2022.10.12.511898  9372df2d-13d6-4fac-980b-919a5b7eb483   \n",
       "197  10.1101/2022.10.12.511898  93131426-0124-4ab4-a013-9dfbcd99d467   \n",
       "206  10.1101/2022.10.12.511898  7c1c3d47-3166-43e5-9a95-65ceb2d45f78   \n",
       "208  10.1101/2022.10.12.511898  7a0a8891-9a22-4549-a55b-c2aca23c3a2a   \n",
       "220  10.1101/2022.10.12.511898  5e5ab909-f73f-4b57-98a0-6d2c5662f6a4   \n",
       "243  10.1101/2022.10.12.511898  3f56901c-dd4a-47d6-b60b-7b0c0111cfb2   \n",
       "245  10.1101/2022.10.12.511898  3a7f3ab4-a280-4b3b-b2c0-6dd05614a78c   \n",
       "249  10.1101/2022.10.12.511898  35c8a04c-8639-4d15-8228-765d8d93fc96   \n",
       "270  10.1101/2022.10.12.511898  07b1d7c8-5c2e-42f7-9246-26f746cd6013   \n",
       "273  10.1101/2022.10.12.511898  0325478a-9b52-45b5-b40a-2e2ab0d72eb1   \n",
       "475    10.1126/science.abl4896  53d208b0-2cfd-4366-9866-c3c6114081bc   \n",
       "476    10.1126/science.abl4896  a68b64d8-aee3-4947-81b7-36b8fe5a44d2   \n",
       "477    10.1126/science.abl4896  c5d88abe-f23a-45fa-a534-788985e93dad   \n",
       "478    10.1126/science.abl4896  5a11f879-d1ef-458a-910c-9b0bdfca5ebf   \n",
       "479    10.1126/science.abl4896  97a17473-e2b1-4f31-a544-44a60773e2dd   \n",
       "\n",
       "                                         dataset_title  \\\n",
       "5                            Tabula Sapiens - Pancreas   \n",
       "6                      Tabula Sapiens - Salivary_Gland   \n",
       "7                               Tabula Sapiens - Heart   \n",
       "8                             Tabula Sapiens - Bladder   \n",
       "9                             Tabula Sapiens - Trachea   \n",
       "11                             Tabula Sapiens - Spleen   \n",
       "12                    Tabula Sapiens - Small_Intestine   \n",
       "14                                Tabula Sapiens - Eye   \n",
       "15                              Tabula Sapiens - Blood   \n",
       "19                                Tabula Sapiens - Fat   \n",
       "20                             Tabula Sapiens - Tongue   \n",
       "21                        Tabula Sapiens - Bone_Marrow   \n",
       "23                             Tabula Sapiens - Kidney   \n",
       "24                             Tabula Sapiens - Muscle   \n",
       "25                         Tabula Sapiens - Lymph_Node   \n",
       "26                               Tabula Sapiens - Lung   \n",
       "27                             Tabula Sapiens - Thymus   \n",
       "43                                         All neurons   \n",
       "139  Dissection: Amygdaloid complex (AMY) - basolat...   \n",
       "143  Dissection: Cerebral cortex (Cx) - Occipitotem...   \n",
       "160  Dissection: Cerebral cortex (Cx) - Perirhinal ...   \n",
       "165  Dissection: Tail of Hippocampus (HiT) - Caudal...   \n",
       "175                  Supercluster: Medium spiny neuron   \n",
       "176  Dissection: Cerebral cortex (Cx) - Temporal po...   \n",
       "177                  Supercluster: Amygdala excitatory   \n",
       "178                      Supercluster: CGE interneuron   \n",
       "183        Dissection: Head of hippocampus (HiH) - CA1   \n",
       "196  Dissection: Midbrain (M) - Periaqueductal gray...   \n",
       "197                      Dissection: Epithalamus - ETH   \n",
       "206  Dissection: Pons (Pn) - Pontine reticular form...   \n",
       "208                    Supercluster: Hippocampal CA1-3   \n",
       "220  Dissection: Midbrain (M) - Inferior colliculus...   \n",
       "243      Dissection: Head of hippocampus (HiH) - CA1-3   \n",
       "245                             Supercluster: Splatter   \n",
       "249  Dissection: Hypothalamus (HTH) - supraoptic re...   \n",
       "270  Dissection: Myelencephalon (medulla oblongata)...   \n",
       "273       Supercluster: Upper-layer intratelencephalic   \n",
       "475                         Tabula Sapiens - All Cells   \n",
       "476                           Tabula Sapiens - Stromal   \n",
       "477                            Tabula Sapiens - Immune   \n",
       "478                       Tabula Sapiens - Endothelial   \n",
       "479                        Tabula Sapiens - Epithelial   \n",
       "\n",
       "                             dataset_h5ad_path  dataset_total_cell_count  \n",
       "5    ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad                     13497  \n",
       "6    f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad                     27199  \n",
       "7    e6a11140-2545-46bc-929e-da243eed2cae.h5ad                     11505  \n",
       "8    e5c63d94-593c-4338-a489-e1048599e751.h5ad                     24583  \n",
       "9    d8732da6-8d1d-42d9-b625-f2416c30054b.h5ad                      9522  \n",
       "11   cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad                     34004  \n",
       "12   a357414d-2042-4eb5-95f0-c58604a18bdd.h5ad                     12467  \n",
       "14   a0754256-f44b-4c4a-962c-a552e47d3fdc.h5ad                     10650  \n",
       "15   983d5ec9-40e8-4512-9e65-a572a9c486cb.h5ad                     50115  \n",
       "19   5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c.h5ad                     20263  \n",
       "20   55cf0ea3-9d2b-4294-871e-bb4b49a79fc7.h5ad                     15020  \n",
       "21   4f1555bc-4664-46c3-a606-78d34dd10d92.h5ad                     12297  \n",
       "23   2423ce2c-3149-4cca-a2ff-cf682ea29b5f.h5ad                      9641  \n",
       "24   1c9eb291-6d31-47e1-96b2-129b5e1ae64f.h5ad                     30746  \n",
       "25   18eb630b-a754-4111-8cd4-c24ec80aa5ec.h5ad                     53275  \n",
       "26   0d2ee4ac-05ee-40b2-afb6-ebb584caa867.h5ad                     35682  \n",
       "27   0ced5e76-6040-47ff-8a72-93847965afc0.h5ad                     33664  \n",
       "43   8e10f1c4-8e98-41e5-b65f-8cd89a887122.h5ad                   2480956  \n",
       "139  fe1a73ab-a203-45fd-84e9-0f7fd19efcbd.h5ad                     35285  \n",
       "143  f8dda921-5fb4-4c94-a654-c6fc346bfd6d.h5ad                     31899  \n",
       "160  dd03ce70-3243-4c96-9561-330cc461e4d7.h5ad                     23732  \n",
       "165  d2b5efc1-14c6-4b5f-bd98-40f9084872d7.h5ad                     36886  \n",
       "175  c4b03352-af8d-492a-8d6b-40f304e0a122.h5ad                    152189  \n",
       "176  c2aad8fc-b63b-4f9b-9cfd-baf7bc9c1771.h5ad                     37642  \n",
       "177  c202b243-1aa1-4b16-bc9a-b36241f3b1e3.h5ad                    109452  \n",
       "178  bdb26abd-f4ba-4ea3-8862-c2340e7a4f55.h5ad                    227671  \n",
       "183  acae7679-d077-461c-b857-ee6ccfeb267f.h5ad                     39147  \n",
       "196  9372df2d-13d6-4fac-980b-919a5b7eb483.h5ad                     33794  \n",
       "197  93131426-0124-4ab4-a013-9dfbcd99d467.h5ad                     24327  \n",
       "206  7c1c3d47-3166-43e5-9a95-65ceb2d45f78.h5ad                     49512  \n",
       "208  7a0a8891-9a22-4549-a55b-c2aca23c3a2a.h5ad                     74979  \n",
       "220  5e5ab909-f73f-4b57-98a0-6d2c5662f6a4.h5ad                     32306  \n",
       "243  3f56901c-dd4a-47d6-b60b-7b0c0111cfb2.h5ad                     37911  \n",
       "245  3a7f3ab4-a280-4b3b-b2c0-6dd05614a78c.h5ad                    291833  \n",
       "249  35c8a04c-8639-4d15-8228-765d8d93fc96.h5ad                     16753  \n",
       "270  07b1d7c8-5c2e-42f7-9246-26f746cd6013.h5ad                     27210  \n",
       "273  0325478a-9b52-45b5-b40a-2e2ab0d72eb1.h5ad                    455006  \n",
       "475  53d208b0-2cfd-4366-9866-c3c6114081bc.h5ad                    483152  \n",
       "476  a68b64d8-aee3-4947-81b7-36b8fe5a44d2.h5ad                     82478  \n",
       "477  c5d88abe-f23a-45fa-a534-788985e93dad.h5ad                    264824  \n",
       "478  5a11f879-d1ef-458a-910c-9b0bdfca5ebf.h5ad                     31691  \n",
       "479  97a17473-e2b1-4f31-a544-44a60773e2dd.h5ad                    104148  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab the feature's soma_joinid from the var dataframe\n",
    "var_joinid = var_df.loc[var_df.feature_id == \"ENSG00000286096\"].soma_joinid\n",
    "\n",
    "# The presence matrix is indexed by the joinids of the dataset and var dataframes,\n",
    "# so slice out the feature of interest by its joinid.\n",
    "dataset_joinids = presence_matrix[:, var_joinid].tocoo().row\n",
    "\n",
    "# From the datasets dataframe, slice out the datasets which have a joinid in the list\n",
    "datasets_df.loc[datasets_df.soma_joinid.isin(dataset_joinids)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying all genes measured in a dataset \n",
    "\n",
    "Finally, we can find the set of genes that were measured in the cells of a given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-28T14:31:47.419947Z",
     "iopub.status.busy": "2023-07-28T14:31:47.419713Z",
     "iopub.status.idle": "2023-07-28T14:31:47.431270Z",
     "shell.execute_reply": "2023-07-28T14:31:47.430798Z"
    }
   },
   "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>soma_joinid</th>\n",
       "      <th>feature_id</th>\n",
       "      <th>feature_name</th>\n",
       "      <th>feature_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ENSG00000121410</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>3999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ENSG00000268895</td>\n",
       "      <td>A1BG-AS1</td>\n",
       "      <td>3374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ENSG00000148584</td>\n",
       "      <td>A1CF</td>\n",
       "      <td>9603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ENSG00000175899</td>\n",
       "      <td>A2M</td>\n",
       "      <td>6318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ENSG00000245105</td>\n",
       "      <td>A2M-AS1</td>\n",
       "      <td>2948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58109</th>\n",
       "      <td>58109</td>\n",
       "      <td>ENSG00000277745</td>\n",
       "      <td>H2AB3</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58354</th>\n",
       "      <td>58354</td>\n",
       "      <td>ENSG00000233522</td>\n",
       "      <td>FAM224A</td>\n",
       "      <td>2031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58411</th>\n",
       "      <td>58411</td>\n",
       "      <td>ENSG00000183146</td>\n",
       "      <td>PRORY</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58523</th>\n",
       "      <td>58523</td>\n",
       "      <td>ENSG00000279274</td>\n",
       "      <td>RP11-533E23.2</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58632</th>\n",
       "      <td>58632</td>\n",
       "      <td>ENSG00000277836</td>\n",
       "      <td>ENSG00000277836.1</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27211 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       soma_joinid       feature_id       feature_name  feature_length\n",
       "0                0  ENSG00000121410               A1BG            3999\n",
       "1                1  ENSG00000268895           A1BG-AS1            3374\n",
       "2                2  ENSG00000148584               A1CF            9603\n",
       "3                3  ENSG00000175899                A2M            6318\n",
       "4                4  ENSG00000245105            A2M-AS1            2948\n",
       "...            ...              ...                ...             ...\n",
       "58109        58109  ENSG00000277745              H2AB3             591\n",
       "58354        58354  ENSG00000233522            FAM224A            2031\n",
       "58411        58411  ENSG00000183146              PRORY             878\n",
       "58523        58523  ENSG00000279274      RP11-533E23.2              75\n",
       "58632        58632  ENSG00000277836  ENSG00000277836.1             288\n",
       "\n",
       "[27211 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slice the dataset(s) of interest, and get the joinid(s)\n",
    "dataset_joinids = datasets_df.loc[datasets_df.collection_id == \"17481d16-ee44-49e5-bcf0-28c0780d8c4a\"].soma_joinid\n",
    "\n",
    "# Slice the presence matrix by the first dimension, i.e., by dataset\n",
    "var_joinids = presence_matrix[dataset_joinids, :].tocoo().col\n",
    "\n",
    "# From the feature (var) dataframe, slice out features which have a joinid in the list.\n",
    "var_df.loc[var_df.soma_joinid.isin(var_joinids)]"
   ]
  }
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