{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"Scaling up reproducible research for single cell transcriptomics using MetaNeighbor (Protocol 1)\"\n",
    "output:\n",
    "  pdf_document:\n",
    "    fig_caption: yes\n",
    "    number_sections: yes\n",
    "graphics: yes\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Protocol 1: assessment of cell type replicability with unsupervised MetaNeighbor\n",
    "\n",
    "Protocol 1 demonstrates how to compute and visualize cluster replicability across 4 human pancreas datasets. We will show steps detailing how to install MetaNeighbor, how to compute and interpret MetaNeighbor AUROCs. All code blocks can be run in a Jupyter notebook, python or ipython interpretor or in a script run at the command line.\n",
    "\n",
    "## Step 0: Installation of MetaNeighbor and packages used in the protocol\n",
    "\n",
    "1. We start by installing the latest MetaNeighbor package from the Gillis lab GitHub page if you have not done so already. \n",
    "\n",
    "Uncomment the line in the cell below and run it, if you run into errors with the installation, refer to the installation information in the [README](https://github.com/gillislab/pymn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:10.464445Z",
     "start_time": "2020-10-16T12:51:10.462739Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#!pip install git+https://github.com/gillislab/pyMN#egg=pymn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Next we need to import other packages needed to run this analysis, all of these are required by pyMN anyway so you should have them if it successfully installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:12.218735Z",
     "start_time": "2020-10-16T12:51:10.487249Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pymn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:12.231240Z",
     "start_time": "2020-10-16T12:51:12.220208Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Here we set up our plotting settings, these are our preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:12.237517Z",
     "start_time": "2020-10-16T12:51:12.232729Z"
    }
   },
   "outputs": [],
   "source": [
    "#These save characters as text in PDFs\n",
    "import matplotlib\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "#These change plot aesthetics\n",
    "\n",
    "sns.set(style='white', font_scale=1.25)\n",
    "plt.rc(\"axes.spines\", top=False, right=False)\n",
    "plt.rc('xtick', bottom=True)\n",
    "plt.rc('ytick', left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Downloading the Hemberg scRNAseq data\n",
    "\n",
    "We consider 4 pancreatic datasets along with their independent annotation (from the original publication). MetaNeighbor expects a gene x cell matrix encapsulated in a anndata format. In Python we recommend using the anndata object. It works well with the popular scRNAseq analysis package Scanpy. The 4 datasets are best acquired using the R package scRNAseq. In the equivalent protocol using the R version of MetaNeighbor we outline how to acquire and preprocess each individual dataset. \n",
    "\n",
    "Merging the R SingleCellExperiment objects requires multiple steps shown in the R version of the protocol. In comparison, merging Python anndata objects is relatively straightforward, but if you have difficulties the [anndata documentation](https://anndata.readthedocs.io/en/stable/concatenation.html) has a section dedicated to it. For the BICCN data used in Protocols 2 and 3, when processing the data we merged 7 Anndata objects. To follow how the data was processed you can refer to this [R script](https://github.com/gillislab/pyMN/blob/master/data/hemberg.R) and this [Python script](https://github.com/gillislab/pyMN/blob/master/data/hemberg.py). Running the R script requires installing the R version of [MetaNeigbhor](https://github.com/gillislab/MetaNeighbor) and the Bioconductor package [scRNAseq](https://bioconductor.org/packages/3.12/scRNAseq).\n",
    "\n",
    "4. First we download the data using `curl`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:39.759246Z",
     "start_time": "2020-10-16T12:51:12.238902Z"
    },
    "eval": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "100 97.2M  100 97.2M    0     0  3634k      0  0:00:27  0:00:27 --:--:-- 3461k\n"
     ]
    }
   ],
   "source": [
    "!curl -L -o hemberg.h5ad https://ndownloader.figshare.com/files/24928574"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Hierarchical cell type replicability analysis\n",
    "\n",
    "5. We load the previously created pancreas dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:41.528688Z",
     "start_time": "2020-10-16T12:51:39.760510Z"
    }
   },
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad('hemberg.h5ad') #Temporary until hosted file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:41.532503Z",
     "start_time": "2020-10-16T12:51:41.529862Z"
    }
   },
   "outputs": [],
   "source": [
    "adata.obs.columns = adata.obs.columns.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see that we have an AnnData object with 15,793 cells and 15,295 genes\n",
    "\n",
    "It has two observation features: `cell.type` and `study_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:41.538046Z",
     "start_time": "2020-10-16T12:51:41.533688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 15793 × 15295\n",
       "    obs: 'cell.type', 'study_id'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Study and Cell Type Data must be passed as a string or integer to MetaNeigbhor fast version to improve computation times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:41.551681Z",
     "start_time": "2020-10-16T12:51:41.539178Z"
    }
   },
   "outputs": [],
   "source": [
    "adata.obs['cell.type'] = adata.obs['cell.type'].astype(str)\n",
    "adata.obs['study_id'] = adata.obs['study_id'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. To perform neighbor voting, MetaNeighbor builds a cell-cell similarity network, which we defined as the Spearman correlation over a user-defined set of genes. We found that we obtained best results by picking genes that are highly variable across datasets, which can be picked using the \"variableGenes\" function.\n",
    "\n",
    "\n",
    ">**NOTE**: This is the one function that differs slightly from the R version. The quantile function in R defaults to a behavoir that is not native to any standard python packages, thus the variable gene list is slightly different between the python and R versions. Overall this does not appear to affect the results significantly. While the AUROCs will not be identical they are extremely similar. In Section 1.4 there are a few differences in connectivity between individual clusters, but the neighborhood structure of the cell-type graph is consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:46.515043Z",
     "start_time": "2020-10-16T12:51:41.554048Z"
    },
    "eval": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    }
   ],
   "source": [
    "pymn.variableGenes(adata, study_col='study_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function returns a list of 736 (600 in the R version) genes that were detected as highly variable in each of the 4 datasets. By default the `variableGenes` functions stores the highly variable genes under the `highly_variable` column in `.var` of the passed AnnData object\n",
    "\n",
    "8. The data and a set of biological meaningful genes is all we need to run MetaNeighbor and obtain cluster similarities.\n",
    "\n",
    ">**NOTE**: Most functions do not return anything by default. Rather they save the output of the function in the `.uns` part of the anndata object. Additionally, by default the functions search for input data from the `.uns` data. This functionality is highly customizable, you can pass objects directly if you want and return the results instead of saving them from the object. Additionally, you can define your own keys to save results as instead of the defaults. __EX__: The output of the below `MetaNeighborUS` run is save in `adata.uns['MetaNeighborUS']` and the parameters uses are saved under `adata.uns['MetaNeighborUS_params']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:47.663104Z",
     "start_time": "2020-10-16T12:51:46.517380Z"
    },
    "eval": true
   },
   "outputs": [],
   "source": [
    "pymn.MetaNeighborUS(adata,\n",
    "                    study_col='study_id',\n",
    "                    ct_col='cell.type',\n",
    "                    fast_version=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster similarities are defined as an Area Under the ROC curve (AUROC), which range between 0 and 1, where 0 indicates low similarity and 1 high similarity.\n",
    "\n",
    "9. For ease of interpretation, we visualize AUROCs as a heatmap, where rows and columns are cell types from all the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:48.368420Z",
     "start_time": "2020-10-16T12:51:47.664613Z"
    },
    "eval": true,
    "fig.height": 6.5,
    "fig.width": 7,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/seaborn/matrix.py:1215: UserWarning: ``square=True`` ignored in clustermap\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.plotMetaNeighborUS(adata, figsize=(10, 10), cmap='coolwarm', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the heatmap, the color of each square indicates the proximity of a pair of cluster, ranging from blue (low similarity) to red (high similarity). For example, \"serger|gamma cell\" (bottom row) is highly similar to \"muraro|pp\" (2nd column from the right) but very dissimilar from \"muraro|ductal\" (middle column). To group similar clusters together, \"plotHeatmap\" applies hierarchical clustering on the AUROC matrix. On the heatmap, we see two red blocks that indicate clear hierarchical structure in the data, with endocrine cell types clustering together (e.g., alpha, beta, gamma) and mesenchymal cells on the other side (e.g., amacrine, ductal, endothelial). Note that each red block is composed of smaller red blocks, indicating that clusters can be matched at an even higher resolution. We also see some off-diagonal patterns (e.g., lawlor|Gamma/PP, lawlor|Delta), which generally indicate the presence of doublets or contamination (presence of cells from other cell types), but what matters here is the clear presence of red blocks, which is a strong indicator of replicability.\n",
    "\n",
    "10. To identify pairs of replicable clusters, we rely on a simple heuristics: a pair of cluster is replicable if they are reciprocal top hits (they preferentially vote for each other) and the AUROC exceeds a given threshold value (in our experience, 0.9 is a good heuristic value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:48.462222Z",
     "start_time": "2020-10-16T12:51:48.369880Z"
    },
    "eval": true,
    "scrolled": false
   },
   "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>Study_ID|Celltype_1</th>\n",
       "      <th>Study_ID|Celltype_2</th>\n",
       "      <th>Mean_AUROC</th>\n",
       "      <th>Match_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>seger|epsilon cell</td>\n",
       "      <td>muraro|epsilon</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>baron|epsilon</td>\n",
       "      <td>seger|epsilon cell</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seger|PSC cell</td>\n",
       "      <td>lawlor|Stellate</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seger|endothelial cell</td>\n",
       "      <td>muraro|endothelial</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>muraro|mesenchymal</td>\n",
       "      <td>seger|PSC cell</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>seger|mast cell</td>\n",
       "      <td>baron|mast</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>baron|activated_stellate</td>\n",
       "      <td>seger|PSC cell</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>baron|endothelial</td>\n",
       "      <td>muraro|endothelial</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lawlor|Gamma/PP</td>\n",
       "      <td>baron|gamma</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lawlor|Acinar</td>\n",
       "      <td>baron|acinar</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lawlor|Alpha</td>\n",
       "      <td>baron|alpha</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>seger|acinar cell</td>\n",
       "      <td>lawlor|Acinar</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>seger|beta cell</td>\n",
       "      <td>lawlor|Beta</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>seger|unclassified cell</td>\n",
       "      <td>baron|schwann</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>seger|MHC class II cell</td>\n",
       "      <td>baron|macrophage</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>baron|beta</td>\n",
       "      <td>lawlor|Beta</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>muraro|acinar</td>\n",
       "      <td>seger|acinar cell</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>seger|ductal cell</td>\n",
       "      <td>lawlor|Ductal</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>baron|t_cell</td>\n",
       "      <td>baron|mast</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>baron|ductal</td>\n",
       "      <td>lawlor|Ductal</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>seger|gamma cell</td>\n",
       "      <td>lawlor|Gamma/PP</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>muraro|beta</td>\n",
       "      <td>lawlor|Beta</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>muraro|duct</td>\n",
       "      <td>seger|ductal cell</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>seger|delta cell</td>\n",
       "      <td>muraro|delta</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>muraro|alpha</td>\n",
       "      <td>lawlor|Alpha</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>muraro|pp</td>\n",
       "      <td>lawlor|Gamma/PP</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>baron|quiescent_stellate</td>\n",
       "      <td>seger|PSC cell</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>seger|alpha cell</td>\n",
       "      <td>muraro|alpha</td>\n",
       "      <td>0.98</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>baron|delta</td>\n",
       "      <td>muraro|delta</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>seger|co-expression cell</td>\n",
       "      <td>lawlor|Beta</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>muraro|unclear</td>\n",
       "      <td>baron|ductal</td>\n",
       "      <td>0.93</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>lawlor|Delta</td>\n",
       "      <td>baron|delta</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>lawlor|None/Other</td>\n",
       "      <td>lawlor|Ductal</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Above_0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>seger|nan</td>\n",
       "      <td>muraro|nan</td>\n",
       "      <td>0.91</td>\n",
       "      <td>Reciprocal_top_hit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Study_ID|Celltype_1 Study_ID|Celltype_2  Mean_AUROC  \\\n",
       "0         seger|epsilon cell      muraro|epsilon        1.00   \n",
       "1              baron|epsilon  seger|epsilon cell        1.00   \n",
       "2             seger|PSC cell     lawlor|Stellate        1.00   \n",
       "3     seger|endothelial cell  muraro|endothelial        1.00   \n",
       "4         muraro|mesenchymal      seger|PSC cell        1.00   \n",
       "5            seger|mast cell          baron|mast        1.00   \n",
       "6   baron|activated_stellate      seger|PSC cell        1.00   \n",
       "7          baron|endothelial  muraro|endothelial        1.00   \n",
       "8            lawlor|Gamma/PP         baron|gamma        1.00   \n",
       "9              lawlor|Acinar        baron|acinar        1.00   \n",
       "10              lawlor|Alpha         baron|alpha        1.00   \n",
       "11         seger|acinar cell       lawlor|Acinar        1.00   \n",
       "12           seger|beta cell         lawlor|Beta        0.99   \n",
       "13   seger|unclassified cell       baron|schwann        0.99   \n",
       "14   seger|MHC class II cell    baron|macrophage        0.99   \n",
       "15                baron|beta         lawlor|Beta        0.99   \n",
       "16             muraro|acinar   seger|acinar cell        0.99   \n",
       "17         seger|ductal cell       lawlor|Ductal        0.99   \n",
       "18              baron|t_cell          baron|mast        0.99   \n",
       "19              baron|ductal       lawlor|Ductal        0.99   \n",
       "20          seger|gamma cell     lawlor|Gamma/PP        0.99   \n",
       "21               muraro|beta         lawlor|Beta        0.98   \n",
       "22               muraro|duct   seger|ductal cell        0.98   \n",
       "23          seger|delta cell        muraro|delta        0.98   \n",
       "24              muraro|alpha        lawlor|Alpha        0.98   \n",
       "25                 muraro|pp     lawlor|Gamma/PP        0.98   \n",
       "26  baron|quiescent_stellate      seger|PSC cell        0.98   \n",
       "27          seger|alpha cell        muraro|alpha        0.98   \n",
       "28               baron|delta        muraro|delta        0.97   \n",
       "29  seger|co-expression cell         lawlor|Beta        0.95   \n",
       "30            muraro|unclear        baron|ductal        0.93   \n",
       "31              lawlor|Delta         baron|delta        0.92   \n",
       "32         lawlor|None/Other       lawlor|Ductal        0.92   \n",
       "33                 seger|nan          muraro|nan        0.91   \n",
       "\n",
       "            Match_type  \n",
       "0   Reciprocal_top_hit  \n",
       "1            Above_0.9  \n",
       "2   Reciprocal_top_hit  \n",
       "3   Reciprocal_top_hit  \n",
       "4            Above_0.9  \n",
       "5   Reciprocal_top_hit  \n",
       "6            Above_0.9  \n",
       "7            Above_0.9  \n",
       "8   Reciprocal_top_hit  \n",
       "9   Reciprocal_top_hit  \n",
       "10  Reciprocal_top_hit  \n",
       "11           Above_0.9  \n",
       "12  Reciprocal_top_hit  \n",
       "13  Reciprocal_top_hit  \n",
       "14  Reciprocal_top_hit  \n",
       "15           Above_0.9  \n",
       "16           Above_0.9  \n",
       "17  Reciprocal_top_hit  \n",
       "18           Above_0.9  \n",
       "19           Above_0.9  \n",
       "20           Above_0.9  \n",
       "21           Above_0.9  \n",
       "22           Above_0.9  \n",
       "23  Reciprocal_top_hit  \n",
       "24           Above_0.9  \n",
       "25           Above_0.9  \n",
       "26           Above_0.9  \n",
       "27           Above_0.9  \n",
       "28           Above_0.9  \n",
       "29           Above_0.9  \n",
       "30           Above_0.9  \n",
       "31           Above_0.9  \n",
       "32           Above_0.9  \n",
       "33  Reciprocal_top_hit  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pymn.topHits(adata, threshold=0.9)\n",
    "\n",
    "adata.uns['MetaNeighborUS_topHits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find a long list of replicable endocrine cell types (e.g., epsilon, alpha and beta cells) and non-endocrine cell types (e.g. mast, endothelial or acinar cells). This list provides strong evidence that these cell types are robust, as they are identified across all datasets with high AUROC.\n",
    "\n",
    "11. In the case where there is a clear structure in the data (endocrine vs non-endocrine here), we can refine AUROCs by splitting the data. AUROCs have a simple interpretation: an AUROC of 0.6 indicates that cells from a given cell type are ranked in front of 60% of other test cells. However, this interpretation is outgroup dependent: because endocrine cells represent ~65% of cells, even an unrelated pair of non-endocrine cell types will have an AUROC > 0.65, because non-endocrine cells will always be ranked in front of endocrine cells.\n",
    "\n",
    "By starting with the full datasets, we uncovered the global structure in the data (endocrine vs non-endocrine). However, to evaluate replicability of endocrine cell types and reduce dataset composition effects, we can make the assessment more stringent by restricting the outgroup to close cell types, i.e. by keeping only endocrine subtypes. We split cell types in two by using the splitClusters function and retain only endocrine cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:48.476607Z",
     "start_time": "2020-10-16T12:51:48.463929Z"
    },
    "eval": true,
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['baron|acinar', 'baron|activated_stellate', 'baron|ductal',\n",
       "        'baron|endothelial', 'baron|macrophage', 'baron|mast',\n",
       "        'baron|quiescent_stellate', 'baron|schwann', 'baron|t_cell',\n",
       "        'lawlor|Acinar', 'lawlor|Ductal', 'lawlor|None/Other',\n",
       "        'lawlor|Stellate', 'muraro|acinar', 'muraro|duct',\n",
       "        'muraro|endothelial', 'muraro|mesenchymal', 'muraro|nan',\n",
       "        'muraro|unclear', 'seger|MHC class II cell', 'seger|PSC cell',\n",
       "        'seger|acinar cell', 'seger|ductal cell', 'seger|endothelial cell',\n",
       "        'seger|mast cell', 'seger|nan', 'seger|unclassified cell',\n",
       "        'seger|unclassified endocrine cell'], dtype=object),\n",
       " array(['baron|alpha', 'baron|beta', 'baron|delta', 'baron|epsilon',\n",
       "        'baron|gamma', 'lawlor|Alpha', 'lawlor|Beta', 'lawlor|Delta',\n",
       "        'lawlor|Gamma/PP', 'muraro|alpha', 'muraro|beta', 'muraro|delta',\n",
       "        'muraro|epsilon', 'muraro|pp', 'seger|alpha cell',\n",
       "        'seger|beta cell', 'seger|co-expression cell', 'seger|delta cell',\n",
       "        'seger|epsilon cell', 'seger|gamma cell'], dtype=object)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level1_split = pymn.splitClusters(\n",
    "    adata, k=2,\n",
    "    save_uns=False)  #Returning the splitClusters instead of saving them\n",
    "level1_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:48.480145Z",
     "start_time": "2020-10-16T12:51:48.477811Z"
    }
   },
   "outputs": [],
   "source": [
    "first_split = level1_split[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By outputting “level1_split”, we found that the cell types were nicely split between non-endocrine and endocrine, and that endocrine cell types where in the second element of the list. Note that splitClusters applies a simple hierarchical clustering algorithm to separate cell types, cell types can be selected manually in more complex scenarios.\n",
    "\n",
    "12. We repeat the MetaNeighbor analysis on endocrine cells only. First, we subset the data to the endocrine cell types (stored in \"first_split\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:48.502101Z",
     "start_time": "2020-10-16T12:51:48.481291Z"
    },
    "eval": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(9341, 15295)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_keep = np.in1d(\n",
    "    pymn.join_labels(adata.obs['study_id'].values,\n",
    "                     adata.obs['cell.type'].values), first_split)\n",
    "subdata = adata[to_keep, :]\n",
    "subdata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new dataset contains the 9341 putative endocrine cells.\n",
    "\n",
    "13. To focus on variability that is specific to endocrine cells, we re-pick highly variable genes:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:51.876299Z",
     "start_time": "2020-10-16T12:51:48.503846Z"
    },
    "eval": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.var` of view, copying.\n"
     ]
    }
   ],
   "source": [
    "pymn.variableGenes(subdata, study_col='study_id') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14. Finally we recompute cluster similarities and visualize AUROCs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:52.292900Z",
     "start_time": "2020-10-16T12:51:51.878106Z"
    },
    "eval": true,
    "fig.height": 6.5,
    "fig.width": 7
   },
   "outputs": [],
   "source": [
    "res = pymn.MetaNeighborUS(subdata,\n",
    "                          study_col='study_id',\n",
    "                          ct_col='cell.type',\n",
    "                          fast_version=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:52.649519Z",
     "start_time": "2020-10-16T12:51:52.294651Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/seaborn/matrix.py:1215: UserWarning: ``square=True`` ignored in clustermap\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.plotMetaNeighborUS(subdata,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting heatmap illustrates an example of a strong set of replicating cell types: when the assessment becomes more stringent (restriction to closely related cell types), the similarity of replicating cell types remains strong (AUROC~1 for alpha, beta, gamma, delta and epsilon cells) while the cross-cell-type similarity decreases (shift from red to blue, e.g. similarity of alpha and beta cell types has shifted from orange/red in the global heatmap to dark blue in the endocrine heatmap) by virtue of zooming in on a subpart of the dataset.\n",
    "\n",
    "15. We can continue to zoom in as long as there are at least two cell types per dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:52.801245Z",
     "start_time": "2020-10-16T12:51:52.650963Z"
    },
    "eval": true,
    "fig.height": 6.5,
    "fig.width": 7
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  if not is_categorical(df_full[k]):\n",
      "Trying to set attribute `.uns` of view, copying.\n"
     ]
    }
   ],
   "source": [
    "level2_split = pymn.splitClusters(subdata, k=3, save_uns=False)\n",
    "my_split = level2_split[2]\n",
    "keep_cell = np.in1d(\n",
    "    pymn.join_labels(adata.obs['study_id'], adata.obs['cell.type']), my_split)\n",
    "subdata = adata[keep_cell, ]\n",
    "pymn.MetaNeighborUS(subdata,\n",
    "                          study_col='study_id',\n",
    "                          ct_col='cell.type',\n",
    "                          fast_version=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:53.166499Z",
     "start_time": "2020-10-16T12:51:52.803042Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/miniconda3/lib/python3.7/site-packages/seaborn/matrix.py:1215: UserWarning: ``square=True`` ignored in clustermap\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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mDGAiIpkwgImIZMIAJiKSCW/EICKTam1Los6IM2AiMqno6GhMmjSpyUd0dHSj5+3atQupqakt1srNzUVUVFSLzwkPDwcABAcHY8uWLVizZo2hE0Zzz2v4pxw4AyYikwoLC8PMmTObPN7c7Dc2NhYJCQmorKzEqFGjUFJSgt9++w1+fn5ITEzEs88+CwCIj49HUlISysvL8fLLL2PlypXQaDQYN+4/B92r1WqEhobixo0biIyMxIQJE3D06FFUV1fjueeeAwBcvnwZmZmZSEhIgEqlwrlz55CXl4e3337bRD+NxjgDJiKTsrW1hbOzc5OP5gJYoVAYesENHz4cycnJ6NOnD86dO9eo/dDBgwcxf/58+Pv7IzExEXZ2dggMDIS9vX2zY1AoFIiJiYGzszP69euHrKwsAMCgQYPg6uqK0aNH48aNG+jatSvS09NN84NoBgOYiMxGcnIyrKysUF9fD5VKhcLCQnh5eaGgoADe3t6G52k0GmzYsAGxsbEYOXKkoWfc7VJTU7FlyxZERkZi7ty5ePLJJ5GTkwOlUgkXFxfD8yoqKvDrr79Cq9WiS5cuhj50bYE94e4Q03WYsFqd5TS0vwV8KaxWZzkNbfIzYvuk9bQVt5r44gMxwmoBQI9R04TWu5f21BeOM2Ai6lDaU184BjARdSjtqS8cA5iISCYMYCIimTCAiYhkwhsx7tBZdi6I1Fl2Logk+kemE1hPb9F0SxeZBmfAREQyYQATEcmEAUxEJBMGMBGRTBjAREQyYQATEcmEAUxEJBMGMBGRTBjARGRSZWVlyM3NbfLRXJ84KRISEnDw4EHD53drXySiFVF4eHir2iPdC++EIyKTio6Oxrp165o8vnDhQixatAgAsG3bNtTV1WHgwIEYOHAgdu7cCSsrK/j7+yMrKwslJSVISUnB/PnzkZKSgvLyckProJCQEISEhMDR0REAkJaWhu+//x42NjawtbVFUlKS0a2Iqqur8emnn6J///4YO3YsEhISGr2mKAxgIjKp1vSE8/LywokTJ1BeXo7Dhw/Dzs4OdnZ2yM7ORnJyMlauXInNmzcDuNU3TqPRoLKyEoWFhXB3d8e0adOQkJAAADh69Cjmzp2L0tJSnDhxAjExMRg2bBiqqqqabUV05MiRJq2ILly4AA8PD0yfPh0AEBER0eg1ReESBBGZVGt6wpWUlKBLly5IT0/HhAkTUFhYiJqaGri7u8PX1xebN2/G6dOnoVKp4Ofnh8LCQjg4OMDe3r5JO6Lx48fjhx9+wLFjxwDgvloRubu74/z58/jmm29w+fLlJq8pClsS3eEnS3e5h9DurJ66Qe4htDuTZottSWQrsCXRywMO3vtJRrB9eLKkr8/KykJCQgLS09OxZMmSZvu/ATAsE2g0Gkmv15a4BEFEZq1hXfhe+vXrh+rq6jYYkTgMYCLqEJydneUegtG4BkxEJBMGMBGRTBjAREQy4RrwHcz1ir5CKfZ3pcg2Qu/8PF9YLdEUlgphtUS2qxo/JVBYLQBQlIv7Ps+4LxdWCwAeFVqtY+EMmIhIJgxgIiKZMICJiGTCACYikgkDmIhIJgxgIiKZMICJiGTCACYikglvxCAikyorK2u2/ZCtrW2jM4FNYdeuXVCr1VCr1SZ9nfvFACYik2pNSyIAWLt2LaysrHDp0iWo1WpotVoUFxfj888/R1RUFDQaDVasWIGgoCB4enoiLi4OWq0WS5YswfLly6HRaPDggw/iyJEjAICgoKBGtW1tbVFUVIRZs2bh73//OyZPnozz589j8eLFsLCQJwoZwERkUq1pSdRg9uzZOHz4MNzc3ODm5obly2/dFt3QrcLZ2RmBgYHIyMgAAFhbW0Or1cLOzg6BgYHYuHEjZs+eDQA4cOBAo+4Vjz/+OEpLSxEXFwcbGxtMnz4d+fn5yMnJadQpoy0xgInIpIxZarCwsIBSqYSlpSWUSiWCg4OxdetWJCUlYcqUKYZuGBkZGejSpQsAQKfTGR739/fHt99+CwCYMWMGUlJSDLX37t2L69evIzQ0FHFxcdi+fTtycnLQv39/kd+uURjARGQWGpYjnn76acNjo0ePBgCEhIQAAN59910AwNSpUxt9bcPz3Nzc8NZbbxkevz2An332WcMvgj59+uDZZ58V/S0YjQFMRB2WWq2Go6Njo7Vm4D9BLjcGMBF1WOa6+6EB9wETEcmEAUxEJBMGMBGRTLgG3E6IbCHUmYhsIySSsou10Hqqnr2E1dLpVcJqUcs4AyYikgkDmIhIJgxgIiKZMICJiGTCACYikgkDmIhIJgxgIiKZtNt9wFFRUaiurjZB5WEmqElE1FS7DeDq6mq88sorwutu/TFWeE2izkzOlkTmjksQRGRS0dHRmDRpUpOP6OjoRs9bu3Zts0FtjPDwcElf39ba7QyYiNoHY1oS7dixA9euXYOLiwtqa2tRWVmJwMBAhIeHY86cOcjOzoZCoUBeXh6WLFmC4OBgzJgxA8XFxZg/fz4A4ObNm4iIiMDQoUNx+vRpLFu2DN9//z0uXbqEefPmYdOmTRg8eDDS0tLg4eGB06dP4/3338d7770HX19fw+NnzpxBREQEfvrpJ1y9ehU2NjZ4/vnnhf5sOAMmIpOytbWFs7Nzk4/mAnjq1KkICgpCcXExunfvjrS0NADA8OHDMXHiRGRkZCA0NBSWlpYoLi6Gu7s7Zs+ejfz8fEMNrVYLT09PzJ07F3q9HlVVVaivr4eDgwOSk5OhUqkQGhqKBx54ANOmTcOYMWOQm5uLXr16ISwsDNbW1ggODka/fv1w48YN3LhxA3Z2djh79qzwnw1nwERkNhr6tpWWlmLAgAGGZpwNPd+GDh2KLVu2oLa2FnZ2dlCpmh4c5Obmht27d6O2thYqlQq///67oU59fT1UKhUUCgWUSqWhB51erzd0Rra0tDT8eX19PS5dugQPDw/odDrh369Cr9dLPi4qMjLSJBfE5HjNcdN5Ec5Y7/w8X+4htDsTPp8ttJ7I09BOeL0qrBYAjPPoJrReS8LDw/Huu+8iJiYG169fR+/evTFt2rQ2e31jcQZMRB2Gn58fACAgIEDmkbQO14CJqMMYP3683EMwCgOYiEgmDGAiIpkwgImIZMKLcHdQKMX9TuosfdwUlgphtcy1h5todSWlQuuJ/O+2Vsd5WVvhT5qISCYMYCIimTCAiYhkwgAmIpIJA5iISCYMYCIimTCAiYhkwgAmIpIJb8QgIpNqbU+4Xbt2Qa1WQ61W37VWbm4uDh48iHnz5t31OQ1HUm7evBl6vR61tbXw9PTEuHHjJH0fd/r888/xwAMPICMjAxUVFQgODsb69evh4eGB3NxcrFix4p41GMBEZFLR0dFYt25dk8cXLlyIRYsWNXosNjYWCQkJqKysxKhRo1BSUoLffvsNfn5+SExMxLPPPgsAiI+PR1JSEsrLy/Hyyy9j5cqV0Gg0hpDNzMyEtbU15s6da6idlpaGxMREpKenY8WKFXj11VcxZswYpKenw8PDA5cvX8Z7772HuXPnYsKECcjNzYWLiwvKy8uxcOFCbNmyBdeuXcPkyZPh6ekJa2trAMD06dNRU1ODU6dOwd7eHi+++CLef/991NTUGA6SvxsuQRCRSYWFheHQoUNNPsLCwpo8V6FQGFoRDR8+HMnJyejTpw/OnTvXqPvFwYMHMX/+fPj7+yMxMRF2dnYIDAyEvb19o1oAEBUVhe3bt6OiogIWFhaoqKjA9evX0a9fP/zxj3+ElZUVQkJCDM93d3fH/PnzoVAo8MILL6CsrAw3b96EXq+Hs7Mz4uPjERsbazh7+IcffsCRI0cwefJkFBUV4auvvoJarb5n+AIMYCIyMWN6wiUnJ8PKysrQOqiwsBBeXl4oKCiAt7e34XkajQYbNmxAbGwsRo4c2STsXF1dUV1djc2bN8PKygoqlQppaWno3r07AECn0xlaEDV8bUMAN/d4UVERysvLDW2KtFot3NzcANyaAf/pT39Cr169DDPgZ555plU/G7YkuoNf0P8TVquzHMaz7JC4v4fOchjPuFVThNaztLcTVith1DvCagHARG8bofXupWEN2JQSEhIwevRoyXU4AyaiDqVhacCURIQvwAAmog6mPbUlYgATEcmEAUxEJBMGMBGRTHgjxh06y84FkTrLzgWRVN26Ca2nsO4irJZOz3lZW+FPmohIJgxgIiKZMICJiGTCACYikgkDmIhIJkJ2QVhbWyMyMlJEqVa7cuVKm74eEZFoQgK4pcORTaWtA5+ISDQuQRARyYQ3YhCRSbW2JZFUCQkJKC8vh0ajEVbzfjS0Vtq1a9c9j8VkABORSbWmJdG2bdtQV1eHgQMHYuDAgdi5cyesrKzg7++PrKwslJSUICUlBfPnz0dKSgrKy8uRl5eHt99+GyEhIQgJCYGjoyOAW62Hvv/+e3Tt2hU9evTAmDFjhLUievjhhwEA+/btQ05ODvr06YNx48Zh/fr1sLOzg6urq1E/Gy5BEJFJtaYlkZeXF+rq6lBeXo7Dhw/Dzs4Ozs7OyM7ORnJyMl544QX4+PgAuNU3zt7eHra2tigsLIS7uzumTZtmqHX06FHMnTsXjz/+OAAIbUXUICkpCS+99BICAwNx/PhxqFQqODk5IS8vz6ifDWfARGRSrVlqKCkpQZcuXZCeno6goCDs3LkT3bp1g5eXF+rr67F582acPn0ajz32GPz8/FBYWAgHBwfY29s3aUc0fvx4fP3117CxsYG9vT3S0tIMr3+/rYh69+6N+tvOiXnkkUfw5Zdfom/fvnj00Udx4sQJ3Lx5Ex4eHsjOzm71z0ZISyI5mKol0bjpscJrdnTv/Dxf7iG0O/7/mCm0nkrgWuoxn8XCagGAxsda0tdnZWUhISEB6enpWLJkyV2bXTasAT/22GPYs2cPfv/9dwQGBmLIkCGSXt+UOAMmIrPWsC58L/369UN1dTVsbGxa3RRTbgxgIuoQnJ2d5R6C0XgRjohIJgxgIiKZMICJiGTCNeA7PDH3MWG1VEqFsFq1tWJbJYnsvDR+SqCwWsou0q6Y36mupFRYLZFthGL/tFtYLQBQ2YibSykstwmrBQAoPCe2XgfCGTARkUwYwEREMmEAExHJhAFMRCQTBjARkUwYwEREMmEAExHJhAFMRCQT3ohBRCbVVi2J2iPOgG8TFRUl9xCIOpzo6GhMmjSpyUd0dHSj561du7bZoDZGeHi4Uc8/efIkrl69avTXicIZ8G2qq6sBsXfCEnV6YWFhmDmz6QH0zc1+d+zYgWvXrsHFxQW1tbWorKxEYGAgwsPDMWfOHGRnZ0OhUCAvLw9LlixBcHAwZsyYgeLiYsyff6sxQE1NDdasWQNHR0fY2trC0tISWVlZsLGxwaOPPoq4uDj06NED7u7uyMnJgY2NDQBAr9cjIiICDz74IADAzc0NMTExcHR0xCOPPIKxY8cK/9lwBkxEJmVrawtnZ+cmH80F8NSpUxEUFITi4mJ0794daWlpAIDhw4dj4sSJyMjIQGhoKCwtLVFcXAx3d3fMnj0b+fn5hhoXLlxAWVkZHBwcUFxcDAB49NFH8cwzz+DQoUPw9vZGTU0NKioqGr12UVGRoU9cenq64etCQ0Nx8uRJk/xsOAMmIrOxd+9eXL9+HaWlpRgwYIChD1tDG6KhQ4diy5YtqK2thZ2dHVQqVZMabm5u6N69OyoqKjBs2DAUFRUhNjYWv/76KyZNmoSrV6/CysoKWq3W0EkZAOzt7VFTU4MtW7bAzc0NwK0+cQqFAqbq3MaecHfUvG77orB6neU0tLdKlwmrxdPQ7o/Y09DEvjF+og1PQwsPD8e7777b6LFdu3ZBrVZDrVa32ThaizNgIuow/Pz8mjz29NNPyzCS1uEaMBF1GOPHj5d7CEZhABMRyYQBTEQkEwYwEZFMeBHuDkqFuJ0Ltj3E/X6rqxP7u1IncBeEolzcz0zVs5ewWgCgUArcHWDdRVgtkbsWAEB3U+BfqMha1CLOgImIZMIAJiKSCQOYiEgmDGAiIpm024tw1tbWiIyMFFrzypUrsOoptCQR0V212wCeN2+e8JqRkZEoEF6ViKh5XIIgIpIJA5iISCYMYCIyqbKyMuTm5jb5kNp+qMGAuSsAABfUSURBVDV27dqF1NRUo74mJiYGgPHtje5Hu10DJqL2ITo6GuvWrWvy+MKFC7Fo0SLD52vXroWVlRUuXboEtVoNrVaL4uJifP7554iKioJGo8GKFSsQFBQET09PxMXFQavVYsmSJVi+fDk0Gg0efPBBHDlyBAAQFBRkqJ2UlISjR4+iuroazz33HNasWYPJkyfj/PnzeO211xAREQG1Wo0pU6YgOTkZAQEBAACtVos9e/YY6m3duhXDhw/H8ePHsXLlSlhaWkr62TCAicikjOkJN3v2bBw+fBhubm5wc3PD8uXLAcDQGcPZ2RmBgYHIyMgAcGs3lFarhZ2dHQIDA7Fx40bMnj0bAHDgwAHY29sDuDWrHTZsGKqqqgz94aZPn478/HwUFBTA1dUVJSUlTcYTGxvbqJ6lpSVmzpyJ0tJS5OfnG/rH3S8GMBGZlDHt5y0sLKBUKmFpaQmlUong4GBs3boVSUlJmDJliqE1UUZGBrp0uXU2h06nMzzu7++Pb7/9FgAwY8YMpKSkAACefPJJHD16FE5OTnBxcUFVVRW2b9+OnJwc9OzZEyqVChUVFcjLy2s0njvr7dixw/Bn9QLayrTblkSmEBkZiYKeLwmr19NW5GE8wkoBEHsYzyu/vy2slkXvPsJqAUB9ubh1RpGH8cS+tlVYLUDwYTyCTau9INtr360dUXOti+TAi3BE1GGp1epGjTcbmEP4AlyCIKIOzBwbcd6OM2AiIpkwgImIZMIAJiKSCdeAb2NtbY0FXglyD6NZVpXFQuvpLayE1TrjvlxYLZ1eJawWANTqxM0xdHqB7Y0stwmrBYBthNopzoBvY4oT1oiI7oYBTEQkEwYwEZFMGMBERDJhABMRyYQBTEQkEwYwEZFMGMBERDJhABORSbVVS6KEhAQcPHjQqK8R3X6ooU5r6/FOOCIyqda0JNq2bRvq6uowcOBADBw4EDt37oSVlRX8/f2RlZWFkpISpKSkYP78+UhJSUF5eTny8vLw9ttvIyQkBCEhIYZjJy9dutTo67/77js89NBDyMjIwKuvvoqPP/4Yw4YNw2OPPdao/VB+fj42btwIW1tbjBgxAomJibCzs8OFCxfwxhtvGLprbNiwAV27dsXQoUNhYWHRqNWRsRjARGRSrWlJ5OXlhRMnTqC8vByHDx+GnZ0d7OzskJ2djeTkZKxcuRKbN28GcKtNkEajQWVlJQoLC+Hu7o5p06YhIeHWMQJ3fr1Op0NgYCAOHz6MlJQUeHt7o7CwEDqdrtF4EhMTMWHCBIwYMQIRERHo2bMnAgIC4OTkhMzMTEMAFxQUYNmyZQCAv/71r41aHRmLSxBEZFK2trZwdnZu8nF7AJeUlKBLly5IT0/HhAkTUFhYiJqaGri7u8PX1xebN2/G6dOnoVKp4Ofnh8LCQjg4OMDe3t7QjqjBnV+vUqmwY8cOxMXFwdvbG5WVldDpdLh8+XKjrxs5ciSOHDmCDRs2QKPRALjVIkmhUDRqP9S7d29s27YNx48fx5NPPomcnBwolUq4uLgY/bNhS6I7FJ6Nk3sIzTLnw3jO2YwWVquzHMajm/iwsFoAUFcmuGeVQFJbEmVlZSEhIQHp6elYsmRJk8BtkJCQgPLyckN4NjCX9kPN4RIEEZm1hnXhe+nXrx+qq6ubPG6u4QswgImog3B2dpZ7CEbjGjARkUwYwEREMmEAExHJhAFMRCQXPRmttLRU/9lnn+lLS0vNqpboeuZaS3Q9c60lup651urMGMD3IScnR+/m5qbPyckxq1qi65lrLdH1zLWW6HrmWqsz4xIEEZFMGMBERDJhABMRyYQBTEQkE9X777//vtyDaI+sra0xevRoWFtbm1Ut0fXMtZboeuZaS3Q9c63VWfE0NCIimXAJgohIJgxgIiKZMICJiGTC84CpiejoaCgUCuj1eigUCoSGhkquWV9fj5ycHPTv3x9Kpfn83q+rq4OFhfT/DVatWgWFQmH4/J133pFc0xw1fJ8N/2101O+zrfAinJG++OILXLhwAfX19fjss88k17tx44ah3siRIyXV+uijj5CZmQmdToevvvrqvuv8/vvvjcLkwQcflDSuhrH16dMHubm5eO+99yTV2rZtG86dO4f6+nr87W9/u+86K1euBACo1WrMnj1b0pjS0tIafT5s2DBJ9QAgPj4eJ0+ehF6vx8KFCyXV0ul0yM/PR319vZC/TxKDM2Aj2djYwM/PT9jWm4iICHTp0gV6vV5yAPft2xeDBw+GjY2NpDp3zoBFzHK8vLwwbdo07NixQ3KtqqoqjBgx4q69wVqrf//+eOGFFwzddqVITU1t9EtLRAAnJSUhLCxMch0AeO+99+Dm5galUinpHU1nmem3FQawkVxcXHDjxg1cvHhRSD03NzcAQJcuXSTX8vDwQEVFBdLT0yXVWbZsGerq6lBYWGhoxS3Vvn378Ouvv+LmzZu4ePGipP9x+/btixs3biA9PR1PPfXUfdc5d+4cVq9ejcLCQqxevVrSmGbOnIkDBw4gLy8PHh4e913ndgUFBYiPj4dKpcKkSZMk1Ro1ahRmzJgheUzLli1DdnY2srKyMGjQIMn1OjsGsJEcHR3h7++PlJQUIfUmTpyIqqoqZGZmSq6l1+vh7+8PS0tLybUiIiJga2sLe3t7zJ07V3K9JUuWID4+Ho8++ij69+8vqVZAQAAqKiogdfVswYIFSE5Oxuuvv96oRfr9unjxIpycnHDhwgU88sgjkusFBAQ0mm1KkZycjLNnz0KlUkmetX7zzTdwdnZGbm4unnvuOSHj66zM52pIOxAdHY3Nmzdjy5Yt2Llzp5Cap0+fhqurKyoqKiTVWbVqFdavX49Vq1YhJiZG8ricnJzg7OyMmzdvSq4FAP/+97/h6emJLVu2SK715z//GZs2bUJUVJSkOtu2bYO7uzuio6MljwkAioqKoNVqcenSJSH1tFotfvjhB2i1Wsm1QkJC0LdvX8yZM0dyLWtra1hbWyMvL09yrc6OM2AjaDQaDB06FIMGDcKsWbMk11u1ahUyMzNx+vRp9OjRQ1KtZcuWISsrq1Xtu1ujR48eOHDgAMaPHy+knr29PXr16oU+ffrgxo0b6N69+33X8vLyQnBwsOQxde3aFVZWVqiqqkJaWprkddsFCxbg+PHjGDVqlOSxAYBCocDKlSvxr3/9S3KtPXv2YNasWfjf//1fvPnmm5JqjR8/HjExMXjyySclj6uzYwAb4faZkoiLU08//bTkMGog+uLImDFjMGfOHGRlZUkdGgCgrKwMu3fvBgBERUVJuqp//vx5rFy5EpaWlpK+zyFDhiAtLc3wT6kBfOzYMTz11FOIiYlBQECApFoAkJubi3/84x+orq6WXKvhgqqITU86nQ7Lly/HiRMnJNfq7BjARli2bBnKysqQm5uLvn37Sq4n8sq56IsjcXFxGDBgAH777TfJs+q0tDRoNJpGjxUWFsLBweG+6qnVaoSEhEgaEwDk5eXB2toa48ePx5AhQyTXu379OgDg6tWrkmsBwNtvv42ioiIhF0IDAwOxf/9+BAUFSa6l1WoxcuRIaLVajBgxQnK9zowBbKS1a9fCyckJ3bp1k3wBQvSVc5EXR7KysvDTTz8JmQHf/oumYSYG4L4DODk5GZcvX4aFhYWkGfCrr76KmpoaxMbGYt++fRg1apSkrYDOzs5YvXq1kFAS+Y5m1apVhn//9ttvJb87qqysxOrVq9GnTx9JdYg3YhgtMjISTk5OyMvLw4IFC4TVq6yslLzb4NNPP4WzszNycnLwxhtvSKpVU1ODCxcuwN3dXfJ+WwDIyMhAbGwsJkyYAFdXV0m1KioqkJubC71eL3nZ4NSpU4iLi0N9fb3kmx1EysvLQ0lJieFzEfuKyfxwBmykwYMHY//+/Zg8ebKQekVFRSgtLUVtba3kWiIvjlhZWcHb21tynQYiLwJ98MEHGDNmDJRKpeRg2r9/v9ndHg3cWsa4/d2HlO8zJiYGp0+fNnzOmyfMBwPYCIcOHYJSqcQTTzwhbH/mggULkJiYKPkuuGvXruHIkSPo1q0bnJ2dhYxNJJEXgXx8fDBx4kQh+52dnJxQXV2NGzduSK516tQpxMfHQ6fTSZ5NDx8+HDqdDhUVFZK3oQUEBGD06NEAIGTrWHFxMTIzM4XcvdnZMYCN0KNHD+zbtw+jR4/GyZMnJd+dtGrVKpSXl6O+vh6HDh3CRx99dN+1vv76a7zyyisAgA0bNkheghB1rkQDf39/7Nu3D4GBgZJrnTt3DsXFxVAoFJKDLiwsDDdv3kRRUZHkcYmeTV+4cAE1NTXo2rWr5FoHDhxASUkJKioq4OnpKanW//zP/wjbntjZMYCNMGrUKBw/fhyTJk3CtWvXJNdbtmwZvvnmGzz33HP44YcfJNXq3bs3unXrZvh3qUSdK9EgLy8PL730kpBaHh4eQnZBAMC6detQWloKpVIp+a25yNk0AJSXl8PHxwelpaWSa50/fx6urq5Ctjy6u7tjxIgRQk6R6+z4EzTSww8/jNWrV2PixIlC6qWnp2P37t1IT0/H9OnT77vOvn37kJOTA71ej9TUVMk3Kog6V6LB/v37kZSUBCsrK8lBl5mZiQ8//FDIbbXdu3cXdvFN5GwaAMaOHYtDhw7h8ccfl1xr6dKlKCwsFHLLdXFxseHmEHO6cNkecReEEe48chC4Ndu83+1UwK2TvbRaLdzd3c2quWHDunRcXBzGjh0ruV55eTkuXLggZN0wOzsbycnJ0Ov1kg+YWbFiBZRKpZBfDCJn08CtpaQ5c+Zg06ZNki9cRkREoHfv3kKOA01ISMCBAwcwZcoUYXf9dVacARvhzv2sDe43gG8/3PrHH3+EQqGAn58fxo0bd9+1bielVmZmJg4cOIDKykohAfz3v/9d2LGb0dHRkn7p3a5///7ClkZEzqYBGC7A1dXVSb5Tz9PTU9hxoCdOnMDSpUuxceNGBrBEDGAjzJw5U+h+1mXLlgka2a1aou6aEn2uBCD22M1Bgwahvr5eyI6KX375BVevXpV8UwcAXLp0CR988IGQ2TRw6/u8cuUKhg4dKjmARR4HWlZWhqSkJJSVld13DbqFSxBG+uSTT4TtZwWAjRs3Ij8/X8jZEj/++KOk83EbmOLQ7YsXL0Kv1yMjIwNPPPGEpFrl5eWoqqrC77//Dl9fX8ljE+XLL78UNpsGgJ07d6KkpATu7u7w9/eXVOv229SlHgdaUFCAEydOYOTIkcLeiXRWnAEbSeR+VuDWKWEvv/yykFqiLnSZ4tDt8+fP46mnnpL8rgEAwsPDAQBKpRJXrlyRdPDNhx9+CKVSKeQXoMjZNHBrptnQxkkqUbep375slpyczJs6JGIAG0nkflYA2L17N1JTU4Vc0V+xYgUSExOFrMuJPnR79+7dwnZB+Pr64tlnn8X27duRn58vqdagQYOEHG0J3DpfWKSCggJkZmYK2bnQcIZvTk6OpDoil82IAWw0kftZAeBf//qXsLXbL7/8EuPHj8fGjRvx9ttvS6ol6n/YBg0NMEXcQajVarFr1y5cuHBB8i+b8+fPC1u3FTmbBoA333wTWq3WsH4uhajb1Hlbs1gMYCOJ3M8KAB9//DEsLCxQV1eHt956S1Ktbt26YcSIEUhKSpI8LtGHbh88eBD19fU4efIk1q5dK6nW0qVLodVqMW3aNMlb99zc3DBv3jxJNRqInE3f/lYfgKQdMmlpaejatauhiUBaWtp9b58MCAiAl5cXdDodLl++bPTXU2MMYCOFh4cb9rOK4OTkhJCQEGzdulVyrUceeQQfffRRk7N3jdXQFbl///44d+6ckP5mDd19pZ7fIHLrHnCr9XtOTo6QdVuRs2mRb/VTU1MBoMm1i/u9gBYXF4eysjLodDphNyR1VgxgI4nczwrcuqj3l7/8RfLbzIZuHf3794dWq8WYMWPuu5ZGo8Hu3bsxceJEHDt2TNK4Grz++uvQarV4/vnnJdURvQb5xRdf4Pr166ivr5dcS+RsGhC3Q0b09snU1FR4e3ujsrJSUh1iABtN5H5WABg4cCBCQ0Ml19FoNM3eJHI/+vXrh8rKSvTq1Qs6nU7y2ADAzs4Of/3rX4UcVCNy697KlSsNHZY//vhjSbVEzqYBsTtkRB4H+tZbb+HMmTNCjyvtrBjARho7dqxhP6sIO3bsQHp6OqysrCQFcb9+/bBmzRqoVCp4e3ujX79+ksYVFBSEvXv3CjmHALj1i6t79+5CDnARGUxDhgyBhYWF5KaogNjZNCB2h4zI7ZNffPEFfH198cknn2DFihWS63VmDGAjidzPCgDvvvuusLOFR44ciXPnziE+Pl7SUZkHDx6EQqHAoEGDkJubi9zcXAwePBguLi73XTM1NRXdu3cXcoi6yGAaMWIEKisrmz3nw1giZ9OA2B0yIrdPDh06FJMmTZK8BZAYwEYTuZ8VuPXW8MKFC6ivr8dnn30mqdbRo0fxwAMPSD6nuLl9p1J3G7i4uAg7RF1UMDUcNLRjxw4hByGJnE0DYnfIiNw+mZycjJMnT0KpVGL16tXciiYBA9hIIvezAoCNjQ38/PyEBMCYMWNw6tQp7NmzB48++uh91xk1apSw1uoNampqDDcqSD2wRlQwnT17FmPHjkVVVZWQn7/I2TQgdoeMyO2TM2fORFZWFoYNG8ZedRIxgI0kcj8rcKv32tWrV5GTkyP5HAd7e3ssXrxYyC+HrVu3IjExUdhMX+QpYaKCqaHZaFhYGKKioiSPKyEhAQqFAiqVCpmZmZKXqUTtkAHEbp+MjY1F//79kZyczACWiAFsJFH7WRtcvnwZgYGB+O677yTXSkhIwJYtW4QsZ2zduhWFhYVC1h9FExlM27dvF/Zupry8HI8//jgOHTqEPXv2SG4LJWqHDCB2+2RNTQ2Ki4tRWFgoZGydGQPYSKL2swLAjRs34OTkhF69euGBBx6QXE/kckZERARsbW1hb2+PuXPnSq4nkqhgmjdvHi5evAiFQiHpAmODyspK2Nra4ubNm0LqidohA4jdPjlnzhwcPnwYc+bMkVyrs2MAG0nkftaGt70iZr9paWmorq5GTU0NTp06JXk5w8nJCQ4ODmY5yxEVTLffVdcwC5ZyV11QUBB+/vlnBAUFCTlFTuQOGVHbJw8dOgQAGDBgAC5evIiLFy9K3iHTmTGAjSRyP6vIddHU1FT06dMHADB58mTJ9Xr06IEDBw6YZfdbUcEk8hD7hmASuXVP5A4ZUdsnm9vhYU6ttNobBrCRRO5nFWnmzJlC6/n4+MDHxwcWFhaora0VtuYtgshgiouLE3KIvSmCSeSSkqjtk6bYIdOZMYCNJHI/qznbsGEDfHx8kJKSgt69e5vVXk+RwSRqe5YpgknkDhmR2ydF75DpzBjARhK5n9Wc+fj4YN68eYiKikK3bt3kHk4jIoNJ5CH2ooNJ5A4ZkdsnzXmHTHvDADZSRw7d2/Xq1QsrVqzA8OHDhRxHKZLIYBJ5iL3IYBK9Q0bk9klz3iHT3jCAqVkNHZGHDRsmuYmjSKKDSeQh9iKDSeQOGUDs9klz3iHT3jCAqVnmereT6GASdYg9IDaYRL/TErl90px3yLQ3DGBqlrne7SQymEQeYg+YdzCJ3D5pzjtk2hsGMDWrM9ztJPIQe8C8g0nk9klz3iHT3kh/P0IdUlZWFvr06SPsrb456tevH7755hvs3LlTyAlmGzZswG+//YbPP/9cyHnAIjVsnxSx1NKwQ8bHxwdDhgwRMLrOiwFMzcrKysL169fh5OQk91BMauTIkbC2tkZ8fLzkWuYcTA3bJ0Wc+tawQ6ZXr16Sl206Oy5BULMsLCzg6+uLkpISuYdiUqIOsQfMe+ueyLVzc90h0x4xgKlZnWV/p6hD7IHOE0zmukOmPWIAU6cm8hD7zhJM5rpDpj3iGjB1agkJCXjjjTfwX//1X5JrNQRTdna2gJGZrzlz5kCpVHboHTJthQFMnVrDwT5TpkyRXKuzBFNn2CHTVhjA1Gk1HGJfWFiImJgYyfU6SzB1lh0ybYFrwNRpiT7EPisrC3V1dR0+mDrLDpm2oNCLuAWIiPD111/Dy8sLJSUlZnk7MpkfBjARkUy4BkxEJBMGMBGRTBjAREQyYQATEcmEAUxEJJP/D9Z6NRkodgcZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.plotMetaNeighborUS(subdata, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we removed the alpha and beta cells (representing close to 85% of endocrine cells) and validate that, even when restricting to neighboring cell types, there is still a clear distinction between delta, gamma and epsilon cells (AUROC ~ 1).\n",
    "\n",
    "## Step 3: stringent assessment of replicability with one-vs-best AUROCs\n",
    "\n",
    "In the previous section, we created progressively more stringent replicability assessments by selecting more and more specific subsets of related cell types. As an alternative, we provide the “one_vs_best” parameter, which offers similar results without having to restrict the dataset manually. In this scoring mode, MetaNeighbor will automatically identify the two closest matching cell types in each test dataset and compute an AUROC based on the voting result for cells from the closest match against cells from the second closest match. Essentially, we are asking how easily a cell type can be distinguished from its closest neighbor.\n",
    "\n",
    "16. To obtain one-vs-best AUROCs, we run the same command as before with two additional parameters: \"one_vs_best = TRUE\" and \"symmetric_output = FALSE\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:56.033306Z",
     "start_time": "2020-10-16T12:51:53.167628Z"
    },
    "eval": true,
    "fig.height": 6.5,
    "fig.width": 7
   },
   "outputs": [],
   "source": [
    "pymn.MetaNeighborUS(adata,\n",
    "                    study_col='study_id',\n",
    "                    ct_col='cell.type',\n",
    "                    fast_version=True,\n",
    "                    symmetric_output=False,\n",
    "                    one_vs_best=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:56.905063Z",
     "start_time": "2020-10-16T12:51:56.035033Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.plotMetaNeighborUS(adata,\n",
    "                        cmap='coolwarm',\n",
    "                        figsize=(10, 10),\n",
    "                        mn_key='MetaNeighborUS_1v1',\n",
    "                        xticklabels=True,\n",
    "                        yticklabels=True,\n",
    "                        fontsize=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation of the heatmap is slightly different compared to one-vs-all AUROCs. First, since we only compare the two closest cell types, most cell type combinations are not tested (NAs, shown in white on the heatmap). Second, by setting “symmetric_output=FALSE”, we broke the symmetry of the heatmap: train cell types are shown as columns and test cell types are shown as rows. Since each cell type is only tested against two cell types in each test dataset (closest and second closest match), we have 8 values per column (2 per dataset).\n",
    "\n",
    "This representation helps to rapidly identify a cell type’s closest hits as well as their closest outgroup. For example, ductal cells (1st red square from the top left) strongly match with each other (one-vs-best AUROC>0.8) and acinar cells are their closest outgroup (blue segments in the same column). The non-symmetric view makes it clear when best hits are not reciprocal. For example, mast cells (5th square from the left) heavily vote for “lawlor|Stellate” and “muraro|mesenchymal”, but this vote is not reciprocal. This pattern indicates that the mast cell type is missing in the Lawlor and Muraro datasets: because mast cells have no natural match in these datasets, they vote for the next closest cell type (stellate cells). The lack of reciprocity in voting is an important tool to detect imbalances in dataset composition.\n",
    "\n",
    "17.When using one-vs-best AUROCs, we recommend extracting replicating cell types as meta-clusters. Cell types are part of the same meta-cluster if they are reciprocal best hits. Note that if cell type A is the reciprocal best hit of B and C, all three cell types are part of the same meta-cluster, even if B and C are not reciprocal best hits. To further filter for strongly replicating cell types, we specify an AUROC threshold (in our experience, 0.7 is a strong one-vs-best AUROC threshold)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:56.972667Z",
     "start_time": "2020-10-16T12:51:56.906560Z"
    }
   },
   "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>clusters</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>metacluster_1</th>\n",
       "      <td>[seger|acinar cell, lawlor|Acinar, baron|acina...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.964499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_2</th>\n",
       "      <td>[baron|activated_stellate, lawlor|Stellate, mu...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.966342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_3</th>\n",
       "      <td>[muraro|alpha, baron|alpha, lawlor|Alpha, sege...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.929227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_4</th>\n",
       "      <td>[baron|beta, muraro|beta, seger|beta cell, law...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.890479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_5</th>\n",
       "      <td>[lawlor|Delta, seger|delta cell, baron|delta, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.873855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_6</th>\n",
       "      <td>[baron|ductal, lawlor|Ductal, muraro|duct, seg...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.895828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_7</th>\n",
       "      <td>[muraro|endothelial, baron|endothelial, seger|...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.962885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_8</th>\n",
       "      <td>[seger|epsilon cell, baron|epsilon, muraro|eps...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.997211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_9</th>\n",
       "      <td>[baron|gamma, seger|gamma cell, lawlor|Gamma/P...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.94572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_10</th>\n",
       "      <td>[seger|MHC class II cell, baron|macrophage]</td>\n",
       "      <td>2</td>\n",
       "      <td>0.940192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_11</th>\n",
       "      <td>[baron|mast, seger|mast cell]</td>\n",
       "      <td>2</td>\n",
       "      <td>0.933844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metacluster_12</th>\n",
       "      <td>[seger|unclassified cell, baron|schwann]</td>\n",
       "      <td>2</td>\n",
       "      <td>0.928088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         clusters n_studies  \\\n",
       "metacluster_1   [seger|acinar cell, lawlor|Acinar, baron|acina...         4   \n",
       "metacluster_2   [baron|activated_stellate, lawlor|Stellate, mu...         4   \n",
       "metacluster_3   [muraro|alpha, baron|alpha, lawlor|Alpha, sege...         4   \n",
       "metacluster_4   [baron|beta, muraro|beta, seger|beta cell, law...         4   \n",
       "metacluster_5   [lawlor|Delta, seger|delta cell, baron|delta, ...         4   \n",
       "metacluster_6   [baron|ductal, lawlor|Ductal, muraro|duct, seg...         4   \n",
       "metacluster_7   [muraro|endothelial, baron|endothelial, seger|...         3   \n",
       "metacluster_8   [seger|epsilon cell, baron|epsilon, muraro|eps...         3   \n",
       "metacluster_9   [baron|gamma, seger|gamma cell, lawlor|Gamma/P...         4   \n",
       "metacluster_10        [seger|MHC class II cell, baron|macrophage]         2   \n",
       "metacluster_11                      [baron|mast, seger|mast cell]         2   \n",
       "metacluster_12           [seger|unclassified cell, baron|schwann]         2   \n",
       "\n",
       "                   score  \n",
       "metacluster_1   0.964499  \n",
       "metacluster_2   0.966342  \n",
       "metacluster_3   0.929227  \n",
       "metacluster_4   0.890479  \n",
       "metacluster_5   0.873855  \n",
       "metacluster_6   0.895828  \n",
       "metacluster_7   0.962885  \n",
       "metacluster_8   0.997211  \n",
       "metacluster_9    0.94572  \n",
       "metacluster_10  0.940192  \n",
       "metacluster_11  0.933844  \n",
       "metacluster_12  0.928088  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pymn.extractMetaClusters(adata, threshold=.7)\n",
    "pymn.score_meta_clusters(adata)\n",
    "mcsummary = adata.uns['MetaNeighborUS_metacluster_scores']\n",
    "mcsummary[mcsummary.index != 'outliers']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *scoreMetaClusters* provides a good summary of meta-clusters, ordering cell types by the number of datasets in which they replicate, then by average AUROC. We find 12 cell types that have strong support across at least 2 datasets, with 7 cell types replicating across all 4 datasets. 8 cell types are tagged as \"outlier\", as they had no strong match. These cell types usually contain doublets, low quality cells or contaminated cell types. The replicability structure described here can be summarized as an Upset plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:57.217650Z",
     "start_time": "2020-10-16T12:51:56.974103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 224.75x320 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.plotUpset(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further investigate the robustness of meta-clusters, they can be visualized as heatmaps (called “cell-type badges”) with the plotMetaClusters function. Because the function generates one heatmap per meta-cluster, we save the output to a PDF file to facilitate investigation. Each badge shows an AUROC heatmap restricted to one specific meta-cluster. These badges help diagnose cases where AUROCs are lower in a specific train or test dataset. For example, the “muraro|duct” cell type has systematically lower AUROCs, suggesting the presence of contaminating cells in another cell type (probably in the “muraro|unclear” cell type).\n",
    "\n",
    "> **Note** This is not yet implemented "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:57.220978Z",
     "start_time": "2020-10-16T12:51:57.219095Z"
    },
    "eval": true
   },
   "outputs": [],
   "source": [
    "####### Not yet implemented #########\n",
    "\n",
    "# pdf(\"meta_clusters.pdf\")\n",
    "# plotMetaClusters(mclusters, best_hits)\n",
    "# dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "18. The last visualization is an alternative representation of the AUROC heatmap as a graph, which is particularly useful for large datasets. In this graph, top votes (AUROC > 0.5) are shown in gray, while outgroup votes (AUROC < 0.5) are shown in orange. To highlight close calls, we recommend keeping only strong outgroup votes, here with AUROC >= 0.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:59.168581Z",
     "start_time": "2020-10-16T12:51:57.222228Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bharris/pyMN/pymn/plotting.py:383: RuntimeWarning: invalid value encountered in greater\n",
      "  (best_hits.values < low_threshold)] = 0\n",
      "/home/bharris/pyMN/pymn/plotting.py:383: RuntimeWarning: invalid value encountered in less\n",
      "  (best_hits.values < low_threshold)] = 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymn.makeClusterGraph(adata, low_threshold=.3)\n",
    "pymn.plotClusterGraph(adata, font_size=9, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are several orange edges, indicating that some cell types had two close matches. To investigate the origin of these close calls, we can focus on a cluster of interest (coi). Here we take a closer look at “baron|epsilon”, query its closest neighbors in the graph with *extendClusterSet*, then zoom in on its subgraph by passing the list of clusters to the *plotClusterGraph* function in order to subset the graph and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T12:51:59.753835Z",
     "start_time": "2020-10-16T12:51:59.171376Z"
    },
    "eval": true,
    "fig.height": 6.5,
    "fig.width": 7
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coi = \"baron|epsilon\"\n",
    "coi = pymn.extendClusterSet(adata=adata, coi=coi, max_neighbor_distance=2)\n",
    "pymn.plotClusterGraph(adata, node_list=coi, font_size=10, figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the “baron|epsilon” case, we find that the epsilon cell type is missing in the Lawlor dataset, so there is no natural match for the Baron epsilon cell type. In such cases, votes are frequently non-reciprocal and equally split between two unrelated cell types, here “Lawlor|Gamma/PP” and “Lawlor|Delta”. In general, the cluster graph can be used to understand how meta-clusters are extracted, why some clusters are tagged as outliers and diagnose problems where the resolution of cell types differs across datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "fig.height,eval,fig.width,-all",
   "main_language": "R",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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