notebooks/cluster_annotation_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# 10x RNA-seq clustering analysis and annotation (CCN20230722)\n",
    "\n",
    "The Mouse Whole Brain Atlas is a high-resolution transcriptomic and spatial cell-type atlas across the entire mouse brain, integrating several whole-brain single-cell RNA-sequencing (scRNA-seq) datasets. The datasets contain a total of ~4 million cells passing rigorous quality-control (QC) criteria. The integrated transcriptomic taxonomy contains 5,322 clusters that are organized in a hierarchical manner with nested groupings of 34 classes, 338 subclasses, 1,201 supertypes and 5,322 types/clusters. The scRNA-seq data reveal transcriptome-wide gene expression and co-expression patterns for each cell type. The anatomical location of each cell type has been annotated using a comprehensive brain-wide MERFISH dataset with a total of ~4 million segmented and QC-passed cells, probed with a 500-gene panel and registered to the Allen Mouse Brain Common Coordinate Framework (CCF v3). The MERFISH data not only provide accurate spatial annotation of cell types at subclass, supertype and cluster levels, but also reveal fine-resolution spatial distinctions or gradients for cell types. The combination of scRNA-seq and MERFISH data reveals a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type, as well as unique features of cell type organization in different brain regions.\n",
    "\n",
    "The purpose of this notebook is to provide an overview of how cluster and cluster annotation information is represented through example use cases.\n",
    "\n",
    "You need to be connected to the internet to run this notebook and that you have already downloaded the data via the getting started notebook.\n",
    "\n",
    "A detailed cell type [annotation table](https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/metadata/WMB-taxonomy/20230830/cl.df_CCN202307220.xlsx) is also provided along with data downloads detailing information about the hierarchical membership, anatomical annotation, neurotransmitter type, cell type marker genes, transcription factor and neuropeptide markers, and other metadata types for each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import requests\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prerequisite for running this notebook is that the data have been downloaded to local directory maintaining the organization from the manifest.json. **Change the download_base variable to where you have downloaded the data in your system.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "version = '20231215'\n",
    "download_base = '../../abc_download_root'\n",
    "\n",
    "use_local_cache = False\n",
    "manifest_path = 'releases/%s/manifest.json' % version\n",
    "\n",
    "if not use_local_cache :\n",
    "    url = 'https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/' + manifest_path\n",
    "    manifest = json.loads(requests.get(url).text)\n",
    "else :\n",
    "    file = os.path.join(download_base,manifest_path)\n",
    "    with open(file,'rb') as f:\n",
    "        manifest = json.load(f)\n",
    "    \n",
    "metadata = manifest['file_listing']['WMB-taxonomy']['metadata']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_directory = os.path.join( download_base, \n",
    "                               manifest['directory_listing']['WMB-taxonomy']['directories']['metadata']['relative_path'], \n",
    "                              'views')\n",
    "view_directory = pathlib.Path( view_directory )\n",
    "cache_views = False\n",
    "if cache_views :\n",
    "    os.makedirs( view_directory, exist_ok=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview\n",
    "\n",
    "### Clusters\n",
    "Each of the final set of 5196 cluster is associated with an alias and label. Each row of the dataframe represnts a ***cluster***. Each *cluster* has a *label* (human readable string that is unique in the database), *cluster alias* (in this case a simple integer) and the *number of cells* that has been grouped into the cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5322\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_alias</th>\n",
       "      <th>number_of_cells</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>727</td>\n",
       "      <td>CS20230722_0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>740</td>\n",
       "      <td>CS20230722_0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>1053</td>\n",
       "      <td>CS20230722_0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>59</td>\n",
       "      <td>CS20230722_1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>96</td>\n",
       "      <td>CS20230722_1001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_alias  number_of_cells            label\n",
       "0              1              727  CS20230722_0001\n",
       "1             10              740  CS20230722_0010\n",
       "2            100             1053  CS20230722_0100\n",
       "3           1000               59  CS20230722_1000\n",
       "4           1001               96  CS20230722_1001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpath = metadata['cluster']['files']['csv']['relative_path']\n",
    "file = os.path.join( download_base, rpath)\n",
    "cluster = pd.read_csv(file)\n",
    "print(len(cluster))\n",
    "cluster.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster annotation term sets\n",
    "Each level of classification is represented as a ***cluster annotation term set***. Each term set consists of a set of ordered terms. Each term set has a *label* (human readable string that is unique in the database), a *name*, *description* and *order* among the term sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>Clusters are assigned based on the average exp...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CCN20230722_CLAS</td>\n",
       "      <td>class</td>\n",
       "      <td>The top level of cell type definition in the m...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCN20230722_SUBC</td>\n",
       "      <td>subclass</td>\n",
       "      <td>The coarse level of cell type definition in th...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCN20230722_SUPT</td>\n",
       "      <td>supertype</td>\n",
       "      <td>The second finest level of cell type definitio...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>cluster</td>\n",
       "      <td>The finest level of cell type definition in th...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              label              name  \\\n",
       "0  CCN20230722_NEUR  neurotransmitter   \n",
       "1  CCN20230722_CLAS             class   \n",
       "2  CCN20230722_SUBC          subclass   \n",
       "3  CCN20230722_SUPT         supertype   \n",
       "4  CCN20230722_CLUS           cluster   \n",
       "\n",
       "                                         description  order  \n",
       "0  Clusters are assigned based on the average exp...      0  \n",
       "1  The top level of cell type definition in the m...      1  \n",
       "2  The coarse level of cell type definition in th...      2  \n",
       "3  The second finest level of cell type definitio...      3  \n",
       "4  The finest level of cell type definition in th...      4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpath = metadata['cluster_annotation_term_set']['files']['csv']['relative_path']\n",
    "file = os.path.join( download_base, rpath)\n",
    "term_set = pd.read_csv(file)\n",
    "term_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the pandas iterrows function to print out the description for each term set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neurotransmitter:\n",
      "\n",
      "Clusters are assigned based on the average expression of both neurotransmitter transporter genes and key neurotransmitter synthesizing enzyme genes.\n",
      "\n",
      "class:\n",
      "\n",
      "The top level of cell type definition in the mouse whole brain taxonomy. It is primarily determined by broad brain region and neurotransmitter type. All cells within a subclass belong to the same class. Class provides a broader categorization of cell types.\n",
      "\n",
      "subclass:\n",
      "\n",
      "The coarse level of cell type definition in the mouse whole brain taxonomy. All cells within a supertype belong to the same subclass. Subclass groups together related supertypes.\n",
      "\n",
      "supertype:\n",
      "\n",
      "The second finest level of cell type definition in the mouse whole brain taxonomy. All cells within a cluster belong to the same supertype. Supertype groups together similar clusters.\n",
      "\n",
      "cluster:\n",
      "\n",
      "The finest level of cell type definition in the mouse whole brain taxonomy. Cells within a cluster share similar characteristics and belong to the same supertype.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print out description for each term_set\n",
    "for tsindex, tsrow in term_set.iterrows() :\n",
    "    print(\"%s:\\n\" % tsrow['name'])\n",
    "    print(\"%s\\n\" % tsrow['description'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster annotation terms\n",
    "A ***cluster annotation term*** represents a group within a single level of classification. Each term object has a *label* (human readable string that is unique in the database), a *name*, which *cluster annotation term set* it belongs to and a *parent* or superclass if the term forms a heirarchy, an order within a *term set* and a *color* for visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>name</th>\n",
       "      <th>cluster_annotation_term_set_label</th>\n",
       "      <th>parent_term_label</th>\n",
       "      <th>parent_term_set_label</th>\n",
       "      <th>term_set_order</th>\n",
       "      <th>term_order</th>\n",
       "      <th>cluster_annotation_term_set_name</th>\n",
       "      <th>color_hex_triplet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CS20230722_NEUR_Glut</td>\n",
       "      <td>Glut</td>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>#2B93DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CS20230722_NEUR_NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>#666666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CS20230722_NEUR_GABA</td>\n",
       "      <td>GABA</td>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>#FF3358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CS20230722_NEUR_Dopa</td>\n",
       "      <td>Dopa</td>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>#fcf04b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CS20230722_NEUR_Glut-GABA</td>\n",
       "      <td>Glut-GABA</td>\n",
       "      <td>CCN20230722_NEUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>#0a9964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       label       name cluster_annotation_term_set_label  \\\n",
       "0       CS20230722_NEUR_Glut       Glut                  CCN20230722_NEUR   \n",
       "1         CS20230722_NEUR_NA         NA                  CCN20230722_NEUR   \n",
       "2       CS20230722_NEUR_GABA       GABA                  CCN20230722_NEUR   \n",
       "3       CS20230722_NEUR_Dopa       Dopa                  CCN20230722_NEUR   \n",
       "4  CS20230722_NEUR_Glut-GABA  Glut-GABA                  CCN20230722_NEUR   \n",
       "\n",
       "  parent_term_label parent_term_set_label  term_set_order  term_order  \\\n",
       "0                                                       0           0   \n",
       "1                                                       0           1   \n",
       "2                                                       0           2   \n",
       "3                                                       0           3   \n",
       "4                                                       0           4   \n",
       "\n",
       "  cluster_annotation_term_set_name color_hex_triplet  \n",
       "0                 neurotransmitter           #2B93DF  \n",
       "1                 neurotransmitter           #666666  \n",
       "2                 neurotransmitter           #FF3358  \n",
       "3                 neurotransmitter           #fcf04b  \n",
       "4                 neurotransmitter           #0a9964  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpath = metadata['cluster_annotation_term']['files']['csv']['relative_path']\n",
    "file = os.path.join( download_base, rpath)\n",
    "term = pd.read_csv(file, keep_default_na=False)\n",
    "term.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the groupby and count functions in pandas to tally the number of terms in each term set (classification level)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>label</th>\n",
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       "      <th>cluster_annotation_term_set_name</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>5322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neurotransmitter</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subclass</th>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supertype</th>\n",
       "      <td>1201</td>\n",
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      "text/plain": [
       "                                  label\n",
       "cluster_annotation_term_set_name       \n",
       "class                                34\n",
       "cluster                            5322\n",
       "neurotransmitter                     10\n",
       "subclass                            338\n",
       "supertype                          1201"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term[['label','cluster_annotation_term_set_name']].groupby('cluster_annotation_term_set_name').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that **term names are only unique within a term set and not unique across term sets**. In particular, the name \"0001 CLA-EPd-CTX Car3 Glut_1\" is used both as supertype and cluster term. These are distinct entities as supertype \"0001 CLA-EPd-CTX Car3 Glut_1\" has three other distinct cluster members. As such when doing aggregation operations, it is important to the use the term ***label*** instead of ***name***. For the name \"0001 CLA-EPd-CTX Car3 Glut_1\", the associated label terms are \"\tCS20230722_SUPT_0001\" and \"CS20230722_CLUS_000\" in the supertype and class level respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0001 CLA-EPd-CTX Car3 Glut_1</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "                              usage_count\n",
       "name                                     \n",
       "0001 CLA-EPd-CTX Car3 Glut_1            2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the groupby function to find the usage count for each term name\n",
    "term_name_count = term.groupby(['name'])[['cluster_annotation_term_set_name']].count()\n",
    "term_name_count.columns = ['usage_count']\n",
    "pred = term_name_count['usage_count'] > 1\n",
    "term_name_count = term_name_count[pred]\n",
    "term_name_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>label</th>\n",
       "      <th>name</th>\n",
       "      <th>cluster_annotation_term_set_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>CS20230722_SUPT_0001</td>\n",
       "      <td>0001 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>supertype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>CS20230722_CLUS_0001</td>\n",
       "      <td>0001 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>cluster</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     label                          name  \\\n",
       "382   CS20230722_SUPT_0001  0001 CLA-EPd-CTX Car3 Glut_1   \n",
       "1583  CS20230722_CLUS_0001  0001 CLA-EPd-CTX Car3 Glut_1   \n",
       "\n",
       "     cluster_annotation_term_set_name  \n",
       "382                         supertype  \n",
       "1583                          cluster  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataframe to terms where there was a name reuse\n",
    "pred = [x in term_name_count.index for x in term['name']]\n",
    "name_clash_term = term[pred][['label','name','cluster_annotation_term_set_name']]\n",
    "name_clash_term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\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>label</th>\n",
       "      <th>name</th>\n",
       "      <th>cluster_annotation_term_set_label</th>\n",
       "      <th>parent_term_label</th>\n",
       "      <th>parent_term_set_label</th>\n",
       "      <th>term_set_order</th>\n",
       "      <th>term_order</th>\n",
       "      <th>cluster_annotation_term_set_name</th>\n",
       "      <th>color_hex_triplet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>CS20230722_CLUS_0001</td>\n",
       "      <td>0001 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>CS20230722_SUPT_0001</td>\n",
       "      <td>CCN20230722_SUPT</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>cluster</td>\n",
       "      <td>#00664E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>CS20230722_CLUS_0002</td>\n",
       "      <td>0002 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>CS20230722_SUPT_0001</td>\n",
       "      <td>CCN20230722_SUPT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>#5C79CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>CS20230722_CLUS_0003</td>\n",
       "      <td>0003 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>CS20230722_SUPT_0001</td>\n",
       "      <td>CCN20230722_SUPT</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>cluster</td>\n",
       "      <td>#86FF4D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>CS20230722_CLUS_0004</td>\n",
       "      <td>0004 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>CS20230722_SUPT_0001</td>\n",
       "      <td>CCN20230722_SUPT</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>cluster</td>\n",
       "      <td>#CC563D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     label                          name  \\\n",
       "1583  CS20230722_CLUS_0001  0001 CLA-EPd-CTX Car3 Glut_1   \n",
       "1584  CS20230722_CLUS_0002  0002 CLA-EPd-CTX Car3 Glut_1   \n",
       "1585  CS20230722_CLUS_0003  0003 CLA-EPd-CTX Car3 Glut_1   \n",
       "1586  CS20230722_CLUS_0004  0004 CLA-EPd-CTX Car3 Glut_1   \n",
       "\n",
       "     cluster_annotation_term_set_label     parent_term_label  \\\n",
       "1583                  CCN20230722_CLUS  CS20230722_SUPT_0001   \n",
       "1584                  CCN20230722_CLUS  CS20230722_SUPT_0001   \n",
       "1585                  CCN20230722_CLUS  CS20230722_SUPT_0001   \n",
       "1586                  CCN20230722_CLUS  CS20230722_SUPT_0001   \n",
       "\n",
       "     parent_term_set_label  term_set_order  term_order  \\\n",
       "1583      CCN20230722_SUPT               4           0   \n",
       "1584      CCN20230722_SUPT               4           1   \n",
       "1585      CCN20230722_SUPT               4           2   \n",
       "1586      CCN20230722_SUPT               4           3   \n",
       "\n",
       "     cluster_annotation_term_set_name color_hex_triplet  \n",
       "1583                          cluster           #00664E  \n",
       "1584                          cluster           #5C79CC  \n",
       "1585                          cluster           #86FF4D  \n",
       "1586                          cluster           #CC563D  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List all clusters which have supertype \"0001 CLA-EPd-CTX Car3 Glut_1\" as parent\n",
    "pred = (term['parent_term_label'] == 'CS20230722_SUPT_0001')\n",
    "term[pred]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster to cluster annotation membership\n",
    "The association between a ***cluster*** and ***cluster annotation term*** is represented as a ***cluster to cluster annotation membership*** within the context of one ***cluster annotation term set***. It is expected that a cluster in only associated with one term within a specific term set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>cluster_annotation_term_label</th>\n",
       "      <th>cluster_annotation_term_set_label</th>\n",
       "      <th>cluster_alias</th>\n",
       "      <th>cluster_annotation_term_name</th>\n",
       "      <th>cluster_annotation_term_set_name</th>\n",
       "      <th>number_of_cells</th>\n",
       "      <th>color_hex_triplet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CS20230722_CLUS_0001</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>128</td>\n",
       "      <td>0001 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>4262</td>\n",
       "      <td>#00664E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CS20230722_CLUS_0002</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>129</td>\n",
       "      <td>0002 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>3222</td>\n",
       "      <td>#5C79CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CS20230722_CLUS_0003</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>130</td>\n",
       "      <td>0003 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>12216</td>\n",
       "      <td>#86FF4D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CS20230722_CLUS_0004</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>143</td>\n",
       "      <td>0004 CLA-EPd-CTX Car3 Glut_1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>9334</td>\n",
       "      <td>#CC563D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CS20230722_CLUS_0005</td>\n",
       "      <td>CCN20230722_CLUS</td>\n",
       "      <td>131</td>\n",
       "      <td>0005 CLA-EPd-CTX Car3 Glut_2</td>\n",
       "      <td>cluster</td>\n",
       "      <td>1056</td>\n",
       "      <td>#E7FF26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cluster_annotation_term_label cluster_annotation_term_set_label  \\\n",
       "0          CS20230722_CLUS_0001                  CCN20230722_CLUS   \n",
       "1          CS20230722_CLUS_0002                  CCN20230722_CLUS   \n",
       "2          CS20230722_CLUS_0003                  CCN20230722_CLUS   \n",
       "3          CS20230722_CLUS_0004                  CCN20230722_CLUS   \n",
       "4          CS20230722_CLUS_0005                  CCN20230722_CLUS   \n",
       "\n",
       "   cluster_alias  cluster_annotation_term_name  \\\n",
       "0            128  0001 CLA-EPd-CTX Car3 Glut_1   \n",
       "1            129  0002 CLA-EPd-CTX Car3 Glut_1   \n",
       "2            130  0003 CLA-EPd-CTX Car3 Glut_1   \n",
       "3            143  0004 CLA-EPd-CTX Car3 Glut_1   \n",
       "4            131  0005 CLA-EPd-CTX Car3 Glut_2   \n",
       "\n",
       "  cluster_annotation_term_set_name  number_of_cells color_hex_triplet  \n",
       "0                          cluster             4262           #00664E  \n",
       "1                          cluster             3222           #5C79CC  \n",
       "2                          cluster            12216           #86FF4D  \n",
       "3                          cluster             9334           #CC563D  \n",
       "4                          cluster             1056           #E7FF26  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpath = metadata['cluster_to_cluster_annotation_membership']['files']['csv']['relative_path']\n",
    "file = os.path.join( download_base, rpath)\n",
    "membership = pd.read_csv(file)\n",
    "membership.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example use cases\n",
    "### Aggregating cluster and cells counts per term\n",
    "\n",
    "We can obtain cluster and cell counts per cluster annotation term using the pandas groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>number_of_clusters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_annotation_term_label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_01</th>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_02</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_03</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_04</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_05</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               number_of_clusters\n",
       "cluster_annotation_term_label                    \n",
       "CS20230722_CLAS_01                            402\n",
       "CS20230722_CLAS_02                             83\n",
       "CS20230722_CLAS_03                             16\n",
       "CS20230722_CLAS_04                             16\n",
       "CS20230722_CLAS_05                            105"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of clusters associated with each cluster annotation term\n",
    "term_cluster_count = membership.groupby(['cluster_annotation_term_label'])[['cluster_alias']].count()\n",
    "term_cluster_count.columns = ['number_of_clusters']\n",
    "term_cluster_count.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>number_of_cells</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_annotation_term_label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_01</th>\n",
       "      <td>1095484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_02</th>\n",
       "      <td>310198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_03</th>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_04</th>\n",
       "      <td>84352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_CLAS_05</th>\n",
       "      <td>107502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               number_of_cells\n",
       "cluster_annotation_term_label                 \n",
       "CS20230722_CLAS_01                     1095484\n",
       "CS20230722_CLAS_02                      310198\n",
       "CS20230722_CLAS_03                        4767\n",
       "CS20230722_CLAS_04                       84352\n",
       "CS20230722_CLAS_05                      107502"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum up the number of cells associated with each cluster annotation term\n",
    "term_cell_count = membership.groupby(['cluster_annotation_term_label'])[['number_of_cells']].sum()\n",
    "term_cell_count.columns = ['number_of_cells']\n",
    "term_cell_count.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>name</th>\n",
       "      <th>cluster_annotation_term_set_name</th>\n",
       "      <th>number_of_clusters</th>\n",
       "      <th>number_of_cells</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CS20230722_NEUR_Glut</th>\n",
       "      <td>Glut</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>2561</td>\n",
       "      <td>2054137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_NEUR_NA</th>\n",
       "      <td>NA</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>127</td>\n",
       "      <td>1089152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_NEUR_GABA</th>\n",
       "      <td>GABA</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>1991</td>\n",
       "      <td>834601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_NEUR_Dopa</th>\n",
       "      <td>Dopa</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>67</td>\n",
       "      <td>9396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS20230722_NEUR_Glut-GABA</th>\n",
       "      <td>Glut-GABA</td>\n",
       "      <td>neurotransmitter</td>\n",
       "      <td>62</td>\n",
       "      <td>8989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name cluster_annotation_term_set_name  \\\n",
       "label                                                                   \n",
       "CS20230722_NEUR_Glut            Glut                 neurotransmitter   \n",
       "CS20230722_NEUR_NA                NA                 neurotransmitter   \n",
       "CS20230722_NEUR_GABA            GABA                 neurotransmitter   \n",
       "CS20230722_NEUR_Dopa            Dopa                 neurotransmitter   \n",
       "CS20230722_NEUR_Glut-GABA  Glut-GABA                 neurotransmitter   \n",
       "\n",
       "                           number_of_clusters  number_of_cells  \n",
       "label                                                           \n",
       "CS20230722_NEUR_Glut                     2561          2054137  \n",
       "CS20230722_NEUR_NA                        127          1089152  \n",
       "CS20230722_NEUR_GABA                     1991           834601  \n",
       "CS20230722_NEUR_Dopa                       67             9396  \n",
       "CS20230722_NEUR_Glut-GABA                  62             8989  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join counts with the term dataframe\n",
    "term_by_label = term.set_index('label')\n",
    "term_with_counts = term_by_label.join( term_cluster_count )\n",
    "term_with_counts = term_with_counts.join( term_cell_count )\n",
    "term_with_counts[['name','cluster_annotation_term_set_name','number_of_clusters','number_of_cells']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we can cache this view for later reuse. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if cache_views :\n",
    "    file = os.path.join( view_directory, 'cluster_annotation_term_with_counts.csv')\n",
    "    term_with_counts.to_csv( file )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize cluster and cells counts for of the classification levels using bar plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot_by_level_and_type( df, level, fig_width = 8.5, fig_height = 4) :\n",
    "    \n",
    "    fig, ax = plt.subplots(1,2)\n",
    "    fig.set_size_inches(fig_width, fig_height)\n",
    "    \n",
    "    for idx, ctype in enumerate(['clusters','cells']) :\n",
    "\n",
    "        pred = (df['cluster_annotation_term_set_name'] == level )\n",
    "        names = df[pred]['name']\n",
    "        counts = df[pred]['number_of_%s' % ctype]\n",
    "        colors = df[pred]['color_hex_triplet']\n",
    "        \n",
    "        ax[idx].barh(names,counts,color=colors)\n",
    "        ax[idx].set_title('Number of %s by %s' % (ctype,level)),\n",
    "        ax[idx].set_xscale('log')\n",
    "        \n",
    "        if idx > 0 :\n",
    "            ax[idx].set_yticklabels([])\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neurotransmitter cluster and  cell counts\n",
    "The majority of clusters and cells are of glutamatergic, GABAergic or GABA-glycinergic neurotransmitter types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = term_with_counts\n",
    "level = 'neurotransmitter'\n",
    "pred = (df['cluster_annotation_term_set_name'] == level )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_by_level_and_type(term_with_counts,'neurotransmitter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class level cluster and cell counts\n",
    "Class \"18 MB Glut\" contains the largest number of clusters (>600), while class \"01 IT-ET Glut\" contains the largest number of cells (>1.0 million)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot_by_level_and_type(term_with_counts,'class',8.5,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing cluster and term distributions\n",
    "\n",
    "We can explore the relationship and distribution of clusters between term sets by creating a pivot table using pandas groupby fuunction. Each row of the resulting dataframe represents a cluster, each column represents a term set and the value in the table is the name of the term that has been associated with the cluster for that specific term set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>cluster_annotation_term_set_name</th>\n",
       "      <th>neurotransmitter</th>\n",
       "      <th>class</th>\n",
       "      <th>subclass</th>\n",
       "      <th>supertype</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_alias</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>1</th>\n",
       "      <td>Glut</td>\n",
       "      <td>01 IT-ET Glut</td>\n",
       "      <td>018 L2 IT PPP-APr Glut</td>\n",
       "      <td>0082 L2 IT PPP-APr Glut_3</td>\n",
       "      <td>0326 L2 IT PPP-APr Glut_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Glut</td>\n",
       "      <td>01 IT-ET Glut</td>\n",
       "      <td>018 L2 IT PPP-APr Glut</td>\n",
       "      <td>0082 L2 IT PPP-APr Glut_3</td>\n",
       "      <td>0327 L2 IT PPP-APr Glut_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Glut</td>\n",
       "      <td>01 IT-ET Glut</td>\n",
       "      <td>018 L2 IT PPP-APr Glut</td>\n",
       "      <td>0081 L2 IT PPP-APr Glut_2</td>\n",
       "      <td>0322 L2 IT PPP-APr Glut_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glut</td>\n",
       "      <td>01 IT-ET Glut</td>\n",
       "      <td>018 L2 IT PPP-APr Glut</td>\n",
       "      <td>0081 L2 IT PPP-APr Glut_2</td>\n",
       "      <td>0323 L2 IT PPP-APr Glut_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Glut</td>\n",
       "      <td>01 IT-ET Glut</td>\n",
       "      <td>018 L2 IT PPP-APr Glut</td>\n",
       "      <td>0081 L2 IT PPP-APr Glut_2</td>\n",
       "      <td>0325 L2 IT PPP-APr Glut_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34368</th>\n",
       "      <td>GABA-Glyc</td>\n",
       "      <td>27 MY GABA</td>\n",
       "      <td>288 MDRN Hoxb5 Ebf2 Gly-Gaba</td>\n",
       "      <td>1102 MDRN Hoxb5 Ebf2 Gly-Gaba_1</td>\n",
       "      <td>4955 MDRN Hoxb5 Ebf2 Gly-Gaba_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34372</th>\n",
       "      <td>GABA-Glyc</td>\n",
       "      <td>27 MY GABA</td>\n",
       "      <td>285 MY Lhx1 Gly-Gaba</td>\n",
       "      <td>1091 MY Lhx1 Gly-Gaba_3</td>\n",
       "      <td>4901 MY Lhx1 Gly-Gaba_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34374</th>\n",
       "      <td>GABA-Glyc</td>\n",
       "      <td>27 MY GABA</td>\n",
       "      <td>285 MY Lhx1 Gly-Gaba</td>\n",
       "      <td>1091 MY Lhx1 Gly-Gaba_3</td>\n",
       "      <td>4902 MY Lhx1 Gly-Gaba_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34376</th>\n",
       "      <td>GABA-Glyc</td>\n",
       "      <td>27 MY GABA</td>\n",
       "      <td>285 MY Lhx1 Gly-Gaba</td>\n",
       "      <td>1091 MY Lhx1 Gly-Gaba_3</td>\n",
       "      <td>4903 MY Lhx1 Gly-Gaba_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34380</th>\n",
       "      <td>GABA-Glyc</td>\n",
       "      <td>27 MY GABA</td>\n",
       "      <td>285 MY Lhx1 Gly-Gaba</td>\n",
       "      <td>1095 MY Lhx1 Gly-Gaba_7</td>\n",
       "      <td>4924 MY Lhx1 Gly-Gaba_7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5322 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster_annotation_term_set_name neurotransmitter          class  \\\n",
       "cluster_alias                                                      \n",
       "1                                            Glut  01 IT-ET Glut   \n",
       "2                                            Glut  01 IT-ET Glut   \n",
       "3                                            Glut  01 IT-ET Glut   \n",
       "4                                            Glut  01 IT-ET Glut   \n",
       "5                                            Glut  01 IT-ET Glut   \n",
       "...                                           ...            ...   \n",
       "34368                                   GABA-Glyc     27 MY GABA   \n",
       "34372                                   GABA-Glyc     27 MY GABA   \n",
       "34374                                   GABA-Glyc     27 MY GABA   \n",
       "34376                                   GABA-Glyc     27 MY GABA   \n",
       "34380                                   GABA-Glyc     27 MY GABA   \n",
       "\n",
       "cluster_annotation_term_set_name                      subclass  \\\n",
       "cluster_alias                                                    \n",
       "1                                       018 L2 IT PPP-APr Glut   \n",
       "2                                       018 L2 IT PPP-APr Glut   \n",
       "3                                       018 L2 IT PPP-APr Glut   \n",
       "4                                       018 L2 IT PPP-APr Glut   \n",
       "5                                       018 L2 IT PPP-APr Glut   \n",
       "...                                                        ...   \n",
       "34368                             288 MDRN Hoxb5 Ebf2 Gly-Gaba   \n",
       "34372                                     285 MY Lhx1 Gly-Gaba   \n",
       "34374                                     285 MY Lhx1 Gly-Gaba   \n",
       "34376                                     285 MY Lhx1 Gly-Gaba   \n",
       "34380                                     285 MY Lhx1 Gly-Gaba   \n",
       "\n",
       "cluster_annotation_term_set_name                        supertype  \\\n",
       "cluster_alias                                                       \n",
       "1                                       0082 L2 IT PPP-APr Glut_3   \n",
       "2                                       0082 L2 IT PPP-APr Glut_3   \n",
       "3                                       0081 L2 IT PPP-APr Glut_2   \n",
       "4                                       0081 L2 IT PPP-APr Glut_2   \n",
       "5                                       0081 L2 IT PPP-APr Glut_2   \n",
       "...                                                           ...   \n",
       "34368                             1102 MDRN Hoxb5 Ebf2 Gly-Gaba_1   \n",
       "34372                                     1091 MY Lhx1 Gly-Gaba_3   \n",
       "34374                                     1091 MY Lhx1 Gly-Gaba_3   \n",
       "34376                                     1091 MY Lhx1 Gly-Gaba_3   \n",
       "34380                                     1095 MY Lhx1 Gly-Gaba_7   \n",
       "\n",
       "cluster_annotation_term_set_name                          cluster  \n",
       "cluster_alias                                                      \n",
       "1                                       0326 L2 IT PPP-APr Glut_3  \n",
       "2                                       0327 L2 IT PPP-APr Glut_3  \n",
       "3                                       0322 L2 IT PPP-APr Glut_2  \n",
       "4                                       0323 L2 IT PPP-APr Glut_2  \n",
       "5                                       0325 L2 IT PPP-APr Glut_2  \n",
       "...                                                           ...  \n",
       "34368                             4955 MDRN Hoxb5 Ebf2 Gly-Gaba_1  \n",
       "34372                                     4901 MY Lhx1 Gly-Gaba_3  \n",
       "34374                                     4902 MY Lhx1 Gly-Gaba_3  \n",
       "34376                                     4903 MY Lhx1 Gly-Gaba_3  \n",
       "34380                                     4924 MY Lhx1 Gly-Gaba_7  \n",
       "\n",
       "[5322 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = membership.groupby(['cluster_alias','cluster_annotation_term_set_name'])['cluster_annotation_term_name'].first().unstack()\n",
    "pivot = pivot[term_set['name']] # order columns\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also obtain a cluster annotation color pivot table in the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>neurotransmitter_color</th>\n",
       "      <th>class_color</th>\n",
       "      <th>subclass_color</th>\n",
       "      <th>supertype_color</th>\n",
       "      <th>cluster_color</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_alias</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>1</th>\n",
       "      <td>#2B93DF</td>\n",
       "      <td>#FA0087</td>\n",
       "      <td>#0F6632</td>\n",
       "      <td>#266DFF</td>\n",
       "      <td>#64661F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#2B93DF</td>\n",
       "      <td>#FA0087</td>\n",
       "      <td>#0F6632</td>\n",
       "      <td>#266DFF</td>\n",
       "      <td>#CCA73D</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#2B93DF</td>\n",
       "      <td>#FA0087</td>\n",
       "      <td>#0F6632</td>\n",
       "      <td>#002BCC</td>\n",
       "      <td>#99000D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#2B93DF</td>\n",
       "      <td>#FA0087</td>\n",
       "      <td>#0F6632</td>\n",
       "      <td>#002BCC</td>\n",
       "      <td>#5C8899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#2B93DF</td>\n",
       "      <td>#FA0087</td>\n",
       "      <td>#0F6632</td>\n",
       "      <td>#002BCC</td>\n",
       "      <td>#473D66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34368</th>\n",
       "      <td>#820e57</td>\n",
       "      <td>#0096C7</td>\n",
       "      <td>#660038</td>\n",
       "      <td>#5CCCA4</td>\n",
       "      <td>#500099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34372</th>\n",
       "      <td>#820e57</td>\n",
       "      <td>#0096C7</td>\n",
       "      <td>#f20985</td>\n",
       "      <td>#976df9</td>\n",
       "      <td>#0F6627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34374</th>\n",
       "      <td>#820e57</td>\n",
       "      <td>#0096C7</td>\n",
       "      <td>#f20985</td>\n",
       "      <td>#976df9</td>\n",
       "      <td>#2E4799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34376</th>\n",
       "      <td>#820e57</td>\n",
       "      <td>#0096C7</td>\n",
       "      <td>#f20985</td>\n",
       "      <td>#976df9</td>\n",
       "      <td>#15FF00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34380</th>\n",
       "      <td>#820e57</td>\n",
       "      <td>#0096C7</td>\n",
       "      <td>#f20985</td>\n",
       "      <td>#FF2B26</td>\n",
       "      <td>#459988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5322 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              neurotransmitter_color class_color subclass_color  \\\n",
       "cluster_alias                                                     \n",
       "1                            #2B93DF     #FA0087        #0F6632   \n",
       "2                            #2B93DF     #FA0087        #0F6632   \n",
       "3                            #2B93DF     #FA0087        #0F6632   \n",
       "4                            #2B93DF     #FA0087        #0F6632   \n",
       "5                            #2B93DF     #FA0087        #0F6632   \n",
       "...                              ...         ...            ...   \n",
       "34368                        #820e57     #0096C7        #660038   \n",
       "34372                        #820e57     #0096C7        #f20985   \n",
       "34374                        #820e57     #0096C7        #f20985   \n",
       "34376                        #820e57     #0096C7        #f20985   \n",
       "34380                        #820e57     #0096C7        #f20985   \n",
       "\n",
       "              supertype_color cluster_color  \n",
       "cluster_alias                                \n",
       "1                     #266DFF       #64661F  \n",
       "2                     #266DFF       #CCA73D  \n",
       "3                     #002BCC       #99000D  \n",
       "4                     #002BCC       #5C8899  \n",
       "5                     #002BCC       #473D66  \n",
       "...                       ...           ...  \n",
       "34368                 #5CCCA4       #500099  \n",
       "34372                 #976df9       #0F6627  \n",
       "34374                 #976df9       #2E4799  \n",
       "34376                 #976df9       #15FF00  \n",
       "34380                 #FF2B26       #459988  \n",
       "\n",
       "[5322 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color = membership.groupby(['cluster_alias','cluster_annotation_term_set_name'])['color_hex_triplet'].first().unstack().fillna('#f9f9f9')\n",
    "color = color[term_set['name']] # order columns\n",
    "color.columns = ['%s_color' % x for x in color.columns]\n",
    "color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we can cache this view for later reuse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "if cache_views :\n",
    "    \n",
    "    file = os.path.join( view_directory, 'cluster_to_cluster_annotation_membership_pivoted.csv')\n",
    "    pivot.to_csv( file )\n",
    "    \n",
    "    file = os.path.join( view_directory, 'cluster_to_cluster_annotation_membership_color.csv')\n",
    "    color.to_csv( file )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given pair of term sets A and B, define a function *distribution* that creates a cluster count table where the rows are terms in term set A, columns are terms in term set B and the table values being the number of clusters that is shared between the terms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distribution( A, B ) :\n",
    "    \n",
    "    AxB = pivot.groupby([A,B])[['cluster']].count()\n",
    "    AxB.columns = ['number_of_clusters']\n",
    "    AxB = AxB.unstack().fillna(0)\n",
    "\n",
    "    B_names = [x[1] for x in list(AxB.columns)]\n",
    "    pred = (term['cluster_annotation_term_set_name'] == B)\n",
    "    term_by_name = term[pred].set_index('name')\n",
    "    B_colors = term_by_name.loc[B_names,'color_hex_triplet']\n",
    "    \n",
    "    return AxB, B_names, B_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function *stacked_bar_distribution* takes the results of *distribution* as input to create distribution stacked bar plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stacked_bar_distribution( AxB, B_names, B_colors, fig_width = 6, fig_height = 6 ) :\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(fig_width, fig_height)\n",
    "\n",
    "    bottom = np.zeros(len(AxB))\n",
    "\n",
    "    for i, col in enumerate(AxB.columns):\n",
    "        ax.barh(AxB.index, AxB[col], left=bottom, label=col[1], color=B_colors[i])\n",
    "        bottom += np.array(AxB[col])\n",
    "\n",
    "    ax.set_title('Distribution of %s in each %s' % (AxB.columns.names[1],AxB.index.name))\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of neurotransmitter clusters in each class\n",
    "\n",
    "At the next level of classification, around 50% of the classes are have a single neurotransmitter identity and remaining 50% are of mixed types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AxB, B_names, B_colors = distribution( 'class', 'neurotransmitter' )\n",
    "fig, ax = stacked_bar_distribution(AxB,B_names,B_colors,6,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the mouse whole brain taxonomy\n",
    "\n",
    "Term sets: class, subclass, supertype and cluster forms a four level mouse whole brain taxonomy. We can visualized the taxonomy as a sunburst diagram that shows the single inheritance hierarchy through a series of rings, that are sliced for each annotation term. Each ring corresponds to a level in the hierarchy. We have ordered the rings so that the class level is the outer most ring so that we can add in labels. Rings are sliced up and divided based on their hierarchical relationship to the parent slice. The angle of each slice is proportional to the number of clusters belonging to the term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = ['class','subclass','supertype']\n",
    "df = {}\n",
    "\n",
    "for lvl in levels :\n",
    "    pred = term_with_counts['cluster_annotation_term_set_name'] == lvl\n",
    "    df[lvl] = term_with_counts[pred]\n",
    "    df[lvl] = df[lvl].sort_values(['parent_term_label','label'])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,10)\n",
    "size = 0.15\n",
    "\n",
    "for i, lvl in enumerate(levels) :\n",
    "    \n",
    "    if lvl == 'class' :\n",
    "        ax.pie(df[lvl]['number_of_clusters'], colors=df[lvl]['color_hex_triplet'],\n",
    "               labels = df[lvl]['name'], rotatelabels=True, labeldistance=1.025,\n",
    "               radius=1, wedgeprops=dict(width=size, edgecolor=None),startangle=0)\n",
    "    else :\n",
    "        ax.pie(df[lvl]['number_of_clusters'], colors=df[lvl]['color_hex_triplet'],\n",
    "           radius=1-i*size, wedgeprops=dict(width=size, edgecolor=None),startangle=0)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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