Cycling_MG_Experiment_2.ipynb

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   "cell_type": "markdown",
   "id": "1769941c",
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   "source": [
    "# Analysis of cycling microglia from experiment 2\n",
    "\n",
    "This notebook generates major results in Figures S4 for the cycling microglia from Experiment 2 (with transcription and translation inhibitors).\n",
    "\n",
    "# Preparation\n",
    "\n",
    "This list includes the necessary packages to run the notebook, along with some that may not be required here but are commonly used for exploring adata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43085b2c",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "import os\n",
    "import re\n",
    "import time\n",
    "import glob\n",
    "import csv\n",
    "import seqc\n",
    "import h5py\n",
    "import tables\n",
    "import seqc.filter\n",
    "import warnings\n",
    "import itertools\n",
    "import harmony\n",
    "import palantir as pl\n",
    "import scvelo as scv\n",
    "import cellrank as cr\n",
    "import collections\n",
    "import statsmodels\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.gridspec as gridspec\n",
    "import doubletdetection\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scanpy as sc\n",
    "import seaborn as sns\n",
    "import scrublet as scr\n",
    "import anndata as ad\n",
    "import networkx as nx\n",
    "import logging\n",
    "import scipy.cluster.hierarchy as shc\n",
    "import scanpy.external as sce\n",
    "import logging\n",
    "\n",
    "from pathlib import Path\n",
    "from typing import Dict\n",
    "from scipy import sparse\n",
    "from scipy import io\n",
    "from scipy.sparse.csgraph import connected_components\n",
    "from sklearn.preprocessing import normalize\n",
    "from itertools import compress\n",
    "from kneed import KneeLocator\n",
    "from copy import deepcopy\n",
    "from seqc import plot\n",
    "from seqc.plot import FigureGrid\n",
    "from collections import OrderedDict\n",
    "from sklearn.utils import resample\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from seqc.sparse_frame import SparseFrame\n",
    "from scipy.io import mmwrite\n",
    "from adjustText import adjust_text\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bba23ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define and customize plot attributes\n",
    "%matplotlib inline\n",
    "sc.set_figure_params(dpi=80) \n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "mpl.rcParams['figure.figsize'] = [4,4]\n",
    "mpl.rcParams['figure.dpi'] = 80\n",
    "mpl.rcParams['savefig.dpi'] = 150\n",
    "mpl.rcParams['figure.facecolor'] = 'white'\n",
    "mpl.rcParams['image.cmap'] = 'Spectral_r'\n",
    "mpl.style.use(\"ggplot\")\n",
    "mpl.rcdefaults()\n",
    "warnings.filterwarnings(action=\"ignore\", module=\"matplotlib\", message=\"findfont\")\n",
    "\n",
    "logging.getLogger('matplotlib').setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625a5ded",
   "metadata": {},
   "source": [
    "# Initialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e2904be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change directory to the desired path:\n",
    "DATA_DIR = '/data/massague/gans/projects/Breast_tumor/GitHub/Data/'\n",
    "\n",
    "# Sub-directory for the data of cycling microglia from experiment 1 \n",
    "source = 'Cycling_MG_2/'\n",
    "\n",
    "# Processed adata\n",
    "for h5_file in Path(DATA_DIR+source).glob('*.h5'):    \n",
    "    adata = sc.read(h5_file) # Log-transformed, normalized data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e5f66a",
   "metadata": {},
   "source": [
    "## Load MILO results\n",
    "\n",
    "### Neighborhood indexes and cell counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91cc6b9d",
   "metadata": {},
   "outputs": [
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       "        1\n",
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   "source": [
    "# Directory for the Milo results\n",
    "MILO_DIR = DATA_DIR+source+'Milo/'\n",
    "\n",
    "# 'Added' in the file name indicates that transcription and translation inhibitors are added:\n",
    "nhoodIdx = (pd.read_csv(MILO_DIR+'Tr_tl_inhibitors_Added_Proliferating microglia_milo.nhoodIndex.csv', index_col=0)-1).T\n",
    "\n",
    "# Each row in column \"1\": the index cell per Milo neighborhood\n",
    "nhoodIdx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dacf2de3",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    HER2BC_Unlabeled_inhib_added  TNBC_Unlabeled_inhib_added  \\\n",
       "1                              1                           0   \n",
       "2                              0                           0   \n",
       "3                              0                           0   \n",
       "4                              2                           0   \n",
       "5                              1                           0   \n",
       "6                              1                           0   \n",
       "7                              0                           0   \n",
       "8                              2                           0   \n",
       "9                              0                           0   \n",
       "10                             0                           1   \n",
       "11                             0                           0   \n",
       "12                             1                           0   \n",
       "13                             0                           1   \n",
       "14                             0                           0   \n",
       "15                             0                           0   \n",
       "16                             0                           0   \n",
       "17                             3                           0   \n",
       "18                             0                           1   \n",
       "\n",
       "    HER2BC_Labeled_inhib_added  TNBC_Labeled_inhib_added  \n",
       "1                           20                         8  \n",
       "2                           31                         9  \n",
       "3                           53                        10  \n",
       "4                           60                        10  \n",
       "5                           37                         0  \n",
       "6                           33                         4  \n",
       "7                           54                         9  \n",
       "8                           35                         2  \n",
       "9                           68                        19  \n",
       "10                          63                        12  \n",
       "11                          20                        13  \n",
       "12                          23                        13  \n",
       "13                          61                        26  \n",
       "14                          61                         8  \n",
       "15                          52                         6  \n",
       "16                          39                         9  \n",
       "17                          35                         4  \n",
       "18                          24                        20  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhoodCounts = pd.read_csv(MILO_DIR+'Tr_tl_inhibitors_Added_Proliferating microglia_milo.nhoodCounts.csv', index_col=0)\n",
    "\n",
    "# Number of cells from each source per neighborhood\n",
    "nhoodCounts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab92b192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update row names in the above Milo results to match the corresponding obs_names of adata\n",
    "nhoodIdx.index = adata.obs_names[nhoodIdx][:,0]\n",
    "nhoodCounts.index = nhoodIdx.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb6f57d",
   "metadata": {},
   "source": [
    "Example code for creating an observation (obs.) in adata to assign neighborhood index status to a cell. (Note: the provided adata object already contains this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1d4ddd5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['nhoodIdx'] = [i in nhoodCounts.index for i in adata.obs_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da63a84",
   "metadata": {},
   "source": [
    "### Generalized linear model (GLM) fit results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7a8f797",
   "metadata": {},
   "outputs": [
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       "      <th>SG-602_d27_inh_mCh_170328415233844-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>4.536598</td>\n",
       "      <td>4.352456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_126820516542699-1-1</th>\n",
       "      <td>-1.208608</td>\n",
       "      <td>2.396463</td>\n",
       "      <td>2.073957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_240550874605990-1-1</th>\n",
       "      <td>-1.198345</td>\n",
       "      <td>3.355754</td>\n",
       "      <td>2.750383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134515756001013-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.644515</td>\n",
       "      <td>3.874963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120856552032182-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.485497</td>\n",
       "      <td>3.594175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_226829501058356-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.199196</td>\n",
       "      <td>3.990437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_157140462496684-1-1</th>\n",
       "      <td>-0.185083</td>\n",
       "      <td>1.789590</td>\n",
       "      <td>-0.099665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134034853938908-1-1</th>\n",
       "      <td>-1.201127</td>\n",
       "      <td>2.431153</td>\n",
       "      <td>2.492743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        (Intercept)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1    -1.208364   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1    -0.574420   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1    -1.208616   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1    -1.208621   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1    -0.569616   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1    -1.198076   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1    -1.208608   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1    -1.198345   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1    -0.185083   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1    -1.201127   \n",
       "\n",
       "                                        groupHER2BC_Labeled_inhib_added  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                         2.257855   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                         4.971045   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                         5.504467   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                         2.715359   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                         2.868360   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                         2.754660   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                         5.523083   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                         2.174123   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                         5.752785   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                         3.387633   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                         4.536598   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                         2.396463   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                         3.355754   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                         5.644515   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                         5.485497   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                         5.199196   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                         1.789590   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                         2.431153   \n",
       "\n",
       "                                        groupTNBC_Labeled_inhib_added  \n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                       1.596220  \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                       3.990437  \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                       4.093946  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                       1.181261  \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                      -2.278491  \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                       0.923873  \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                       3.990437  \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                      -0.368419  \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                       4.727871  \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                       1.984455  \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                       4.352456  \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                       2.073957  \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                       2.750383  \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                       3.874963  \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                       3.594175  \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                       3.990437  \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                      -0.099665  \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                       2.492743  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit = pd.read_csv(MILO_DIR+'Tr_tl_inhibitors_Added_Proliferating microglia_milo.nhoodCountsFit.csv', index_col=0)\n",
    "fit.index = nhoodIdx.index\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94779821",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(Intercept)</th>\n",
       "      <th>groupHER2BC_Labeled_inhib_added</th>\n",
       "      <th>groupTNBC_Labeled_inhib_added</th>\n",
       "      <th>UMAP_1</th>\n",
       "      <th>UMAP_2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>SG-602_d27_inh_mCh_165260070608243-1-1</th>\n",
       "      <td>-1.208364</td>\n",
       "      <td>2.257855</td>\n",
       "      <td>1.596220</td>\n",
       "      <td>6.784558</td>\n",
       "      <td>6.086753</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120726897682292-1-1</th>\n",
       "      <td>-3.487106</td>\n",
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       "      <td>3.990437</td>\n",
       "      <td>5.505533</td>\n",
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       "      <th>SG-602_d27_inh_mCh_230610192485676-1-1</th>\n",
       "      <td>-3.487106</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>-0.574420</td>\n",
       "      <td>2.715359</td>\n",
       "      <td>1.181261</td>\n",
       "      <td>5.871239</td>\n",
       "      <td>4.877238</td>\n",
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       "      <th>SG-602_d27_inh_mCh_135621978577187-1-1</th>\n",
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       "      <td>-2.278491</td>\n",
       "      <td>5.571806</td>\n",
       "      <td>5.048438</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_235686931623196-1-1</th>\n",
       "      <td>-1.208621</td>\n",
       "      <td>2.754660</td>\n",
       "      <td>0.923873</td>\n",
       "      <td>4.288418</td>\n",
       "      <td>6.644714</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170677547482357-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.523083</td>\n",
       "      <td>3.990437</td>\n",
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       "      <th>SG-602_d27_inh_mCh_121812464823195-1-1</th>\n",
       "      <td>-0.569616</td>\n",
       "      <td>2.174123</td>\n",
       "      <td>-0.368419</td>\n",
       "      <td>5.251798</td>\n",
       "      <td>6.231791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_195617061460406-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.752785</td>\n",
       "      <td>4.727871</td>\n",
       "      <td>6.515573</td>\n",
       "      <td>3.002038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_206068851521894-1-1</th>\n",
       "      <td>-1.198076</td>\n",
       "      <td>3.387633</td>\n",
       "      <td>1.984455</td>\n",
       "      <td>3.653918</td>\n",
       "      <td>0.246762</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170328415233844-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>4.536598</td>\n",
       "      <td>4.352456</td>\n",
       "      <td>7.716782</td>\n",
       "      <td>-1.929050</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_126820516542699-1-1</th>\n",
       "      <td>-1.208608</td>\n",
       "      <td>2.396463</td>\n",
       "      <td>2.073957</td>\n",
       "      <td>6.178156</td>\n",
       "      <td>4.303478</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_240550874605990-1-1</th>\n",
       "      <td>-1.198345</td>\n",
       "      <td>3.355754</td>\n",
       "      <td>2.750383</td>\n",
       "      <td>6.885855</td>\n",
       "      <td>3.034356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134515756001013-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.644515</td>\n",
       "      <td>3.874963</td>\n",
       "      <td>4.874705</td>\n",
       "      <td>0.786061</td>\n",
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       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120856552032182-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.485497</td>\n",
       "      <td>3.594175</td>\n",
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       "      <td>-3.487106</td>\n",
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       "      <td>3.990437</td>\n",
       "      <td>3.987141</td>\n",
       "      <td>5.834962</td>\n",
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       "      <th>SG-602_d27_inh_mCh_157140462496684-1-1</th>\n",
       "      <td>-0.185083</td>\n",
       "      <td>1.789590</td>\n",
       "      <td>-0.099665</td>\n",
       "      <td>5.975085</td>\n",
       "      <td>6.063810</td>\n",
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       "      <th>SG-602_d27_inh_mCh_134034853938908-1-1</th>\n",
       "      <td>-1.201127</td>\n",
       "      <td>2.431153</td>\n",
       "      <td>2.492743</td>\n",
       "      <td>7.599732</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                        (Intercept)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1    -1.208364   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1    -0.574420   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1    -1.208616   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1    -1.208621   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1    -0.569616   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1    -1.198076   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1    -1.208608   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1    -1.198345   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1    -0.185083   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1    -1.201127   \n",
       "\n",
       "                                        groupHER2BC_Labeled_inhib_added  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                         2.257855   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                         4.971045   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                         5.504467   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                         2.715359   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                         2.868360   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                         2.754660   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                         5.523083   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                         2.174123   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                         5.752785   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                         3.387633   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                         4.536598   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                         2.396463   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                         3.355754   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                         5.644515   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                         5.485497   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                         5.199196   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                         1.789590   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                         2.431153   \n",
       "\n",
       "                                        groupTNBC_Labeled_inhib_added  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                       1.596220   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                       4.093946   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                       1.181261   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                      -2.278491   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                       0.923873   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                      -0.368419   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                       4.727871   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                       1.984455   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                       4.352456   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                       2.073957   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                       2.750383   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                       3.874963   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                       3.594175   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                      -0.099665   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                       2.492743   \n",
       "\n",
       "                                          UMAP_1    UMAP_2  \n",
       "SG-602_d27_inh_mCh_165260070608243-1-1  6.784558  6.086753  \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1  5.505533 -1.606483  \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1  4.317109  0.651766  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  5.871239  4.877238  \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1  5.571806  5.048438  \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1  4.288418  6.644714  \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1  6.333059  0.030782  \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1  5.251798  6.231791  \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1  6.515573  3.002038  \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1  3.653918  0.246762  \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1  7.716782 -1.929050  \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1  6.178156  4.303478  \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1  6.885855  3.034356  \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1  4.874705  0.786061  \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1  5.626551  1.015566  \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1  3.987141  5.834962  \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1  5.975085  6.063810  \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1  7.599732  3.952429  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add UMAP embedding results to assist downstream visualization\n",
    "fit.loc[:,['UMAP_1','UMAP_2']] = adata[fit.index].obsm['X_umap']\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51cd27df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the fit results have also been added to the adata provided, using: \n",
    "adata.obs['GLM_coeff_HER2BC_Labeled'] = np.nan\n",
    "adata.obs['GLM_coeff_TNBC_Labeled'] = np.nan\n",
    "\n",
    "adata.obs['GLM_coeff_HER2BC_Labeled'][fit.index] = fit.loc[:,'groupHER2BC_Labeled_inhib_added']\n",
    "adata.obs['GLM_coeff_TNBC_Labeled'][fit.index] = fit.loc[:,'groupTNBC_Labeled_inhib_added']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313d9d73",
   "metadata": {},
   "source": [
    "### Differential abundance results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "94cc7402",
   "metadata": {},
   "outputs": [],
   "source": [
    "# da: \"differential abundance\", saved as a dictionary, with the keys set to the groups compared\n",
    "da = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d7d39d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>(Intercept)</th>\n",
       "      <th>groupHER2BC_Labeled_inhib_added</th>\n",
       "      <th>groupTNBC_Labeled_inhib_added</th>\n",
       "      <th>UMAP_1</th>\n",
       "      <th>UMAP_2</th>\n",
       "      <th>TNvsHER2_neg_log10(SpatialFDR)</th>\n",
       "      <th>TNvsHER2_logFC</th>\n",
       "      <th>TNvsCtrl_neg_log10(SpatialFDR)</th>\n",
       "      <th>TNvsCtrl_logFC</th>\n",
       "      <th>HER2vsCtrl_neg_log10(SpatialFDR)</th>\n",
       "      <th>HER2vsCtrl_logFC</th>\n",
       "      <th>LabeledvsCtrl_neg_log10(SpatialFDR)</th>\n",
       "      <th>nhood_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_165260070608243-1-1</th>\n",
       "      <td>-1.208364</td>\n",
       "      <td>2.257855</td>\n",
       "      <td>1.596220</td>\n",
       "      <td>6.784558</td>\n",
       "      <td>6.086753</td>\n",
       "      <td>0.849080</td>\n",
       "      <td>-0.954538</td>\n",
       "      <td>1.108281</td>\n",
       "      <td>2.302859</td>\n",
       "      <td>2.890810</td>\n",
       "      <td>3.257397</td>\n",
       "      <td>2.334188</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120726897682292-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>4.971045</td>\n",
       "      <td>3.990437</td>\n",
       "      <td>5.505533</td>\n",
       "      <td>-1.606483</td>\n",
       "      <td>1.780537</td>\n",
       "      <td>-1.414719</td>\n",
       "      <td>2.117366</td>\n",
       "      <td>5.756983</td>\n",
       "      <td>5.752000</td>\n",
       "      <td>7.171702</td>\n",
       "      <td>5.414358</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_230610192485676-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.504467</td>\n",
       "      <td>4.093946</td>\n",
       "      <td>4.317109</td>\n",
       "      <td>0.651766</td>\n",
       "      <td>2.952230</td>\n",
       "      <td>-2.034951</td>\n",
       "      <td>2.123225</td>\n",
       "      <td>5.906316</td>\n",
       "      <td>7.518279</td>\n",
       "      <td>7.941267</td>\n",
       "      <td>7.735831</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>-0.574420</td>\n",
       "      <td>2.715359</td>\n",
       "      <td>1.181261</td>\n",
       "      <td>5.871239</td>\n",
       "      <td>4.877238</td>\n",
       "      <td>3.372030</td>\n",
       "      <td>-2.213236</td>\n",
       "      <td>0.910424</td>\n",
       "      <td>1.704199</td>\n",
       "      <td>6.105733</td>\n",
       "      <td>3.917434</td>\n",
       "      <td>6.632005</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_135621978577187-1-1</th>\n",
       "      <td>-1.208616</td>\n",
       "      <td>2.868360</td>\n",
       "      <td>-2.278491</td>\n",
       "      <td>5.571806</td>\n",
       "      <td>5.048438</td>\n",
       "      <td>7.650844</td>\n",
       "      <td>-7.425336</td>\n",
       "      <td>0.672091</td>\n",
       "      <td>-3.287167</td>\n",
       "      <td>5.257264</td>\n",
       "      <td>4.138168</td>\n",
       "      <td>9.313412</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_235686931623196-1-1</th>\n",
       "      <td>-1.208621</td>\n",
       "      <td>2.754660</td>\n",
       "      <td>0.923873</td>\n",
       "      <td>4.288418</td>\n",
       "      <td>6.644714</td>\n",
       "      <td>3.730161</td>\n",
       "      <td>-2.641267</td>\n",
       "      <td>0.416770</td>\n",
       "      <td>1.332867</td>\n",
       "      <td>4.692660</td>\n",
       "      <td>3.974135</td>\n",
       "      <td>5.885967</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170677547482357-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.523083</td>\n",
       "      <td>3.990437</td>\n",
       "      <td>6.333059</td>\n",
       "      <td>0.030782</td>\n",
       "      <td>3.243834</td>\n",
       "      <td>-2.211142</td>\n",
       "      <td>2.117366</td>\n",
       "      <td>5.756983</td>\n",
       "      <td>7.518279</td>\n",
       "      <td>7.968125</td>\n",
       "      <td>8.011858</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_121812464823195-1-1</th>\n",
       "      <td>-0.569616</td>\n",
       "      <td>2.174123</td>\n",
       "      <td>-0.368419</td>\n",
       "      <td>5.251798</td>\n",
       "      <td>6.231791</td>\n",
       "      <td>4.969519</td>\n",
       "      <td>-3.668111</td>\n",
       "      <td>0.130056</td>\n",
       "      <td>-0.531516</td>\n",
       "      <td>4.108428</td>\n",
       "      <td>3.136596</td>\n",
       "      <td>6.632005</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_195617061460406-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.752785</td>\n",
       "      <td>4.727871</td>\n",
       "      <td>6.515573</td>\n",
       "      <td>3.002038</td>\n",
       "      <td>2.173543</td>\n",
       "      <td>-1.478638</td>\n",
       "      <td>3.278711</td>\n",
       "      <td>6.820876</td>\n",
       "      <td>8.608748</td>\n",
       "      <td>8.299514</td>\n",
       "      <td>8.467735</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_206068851521894-1-1</th>\n",
       "      <td>-1.198076</td>\n",
       "      <td>3.387633</td>\n",
       "      <td>1.984455</td>\n",
       "      <td>3.653918</td>\n",
       "      <td>0.246762</td>\n",
       "      <td>3.158579</td>\n",
       "      <td>-2.024358</td>\n",
       "      <td>1.677719</td>\n",
       "      <td>2.862964</td>\n",
       "      <td>7.147466</td>\n",
       "      <td>4.887322</td>\n",
       "      <td>7.387751</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170328415233844-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>4.536598</td>\n",
       "      <td>4.352456</td>\n",
       "      <td>7.716782</td>\n",
       "      <td>-1.929050</td>\n",
       "      <td>0.175013</td>\n",
       "      <td>-0.265661</td>\n",
       "      <td>2.753976</td>\n",
       "      <td>6.279266</td>\n",
       "      <td>4.099064</td>\n",
       "      <td>6.544927</td>\n",
       "      <td>3.515692</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_126820516542699-1-1</th>\n",
       "      <td>-1.208608</td>\n",
       "      <td>2.396463</td>\n",
       "      <td>2.073957</td>\n",
       "      <td>6.178156</td>\n",
       "      <td>4.303478</td>\n",
       "      <td>0.358634</td>\n",
       "      <td>-0.465278</td>\n",
       "      <td>1.965558</td>\n",
       "      <td>2.992087</td>\n",
       "      <td>3.299535</td>\n",
       "      <td>3.457365</td>\n",
       "      <td>2.652890</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_240550874605990-1-1</th>\n",
       "      <td>-1.198345</td>\n",
       "      <td>3.355754</td>\n",
       "      <td>2.750383</td>\n",
       "      <td>6.885855</td>\n",
       "      <td>3.034356</td>\n",
       "      <td>0.983527</td>\n",
       "      <td>-0.873365</td>\n",
       "      <td>3.278711</td>\n",
       "      <td>3.967964</td>\n",
       "      <td>7.069361</td>\n",
       "      <td>4.841329</td>\n",
       "      <td>6.373341</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134515756001013-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.644515</td>\n",
       "      <td>3.874963</td>\n",
       "      <td>4.874705</td>\n",
       "      <td>0.786061</td>\n",
       "      <td>3.870483</td>\n",
       "      <td>-2.552924</td>\n",
       "      <td>1.965558</td>\n",
       "      <td>5.590389</td>\n",
       "      <td>8.080907</td>\n",
       "      <td>8.143314</td>\n",
       "      <td>8.912225</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120856552032182-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.485497</td>\n",
       "      <td>3.594175</td>\n",
       "      <td>5.626551</td>\n",
       "      <td>1.015566</td>\n",
       "      <td>3.870483</td>\n",
       "      <td>-2.728600</td>\n",
       "      <td>1.632009</td>\n",
       "      <td>5.185298</td>\n",
       "      <td>7.501858</td>\n",
       "      <td>7.913899</td>\n",
       "      <td>8.406671</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_226829501058356-1-1</th>\n",
       "      <td>-3.487106</td>\n",
       "      <td>5.199196</td>\n",
       "      <td>3.990437</td>\n",
       "      <td>3.987141</td>\n",
       "      <td>5.834962</td>\n",
       "      <td>2.287333</td>\n",
       "      <td>-1.743871</td>\n",
       "      <td>2.117366</td>\n",
       "      <td>5.756983</td>\n",
       "      <td>6.445610</td>\n",
       "      <td>7.500854</td>\n",
       "      <td>6.365992</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_157140462496684-1-1</th>\n",
       "      <td>-0.185083</td>\n",
       "      <td>1.789590</td>\n",
       "      <td>-0.099665</td>\n",
       "      <td>5.975085</td>\n",
       "      <td>6.063810</td>\n",
       "      <td>3.800295</td>\n",
       "      <td>-2.725619</td>\n",
       "      <td>0.048406</td>\n",
       "      <td>-0.143786</td>\n",
       "      <td>3.388511</td>\n",
       "      <td>2.581833</td>\n",
       "      <td>4.933244</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134034853938908-1-1</th>\n",
       "      <td>-1.201127</td>\n",
       "      <td>2.431153</td>\n",
       "      <td>2.492743</td>\n",
       "      <td>7.599732</td>\n",
       "      <td>3.952429</td>\n",
       "      <td>0.061820</td>\n",
       "      <td>0.088855</td>\n",
       "      <td>2.753976</td>\n",
       "      <td>3.596267</td>\n",
       "      <td>3.299535</td>\n",
       "      <td>3.507413</td>\n",
       "      <td>3.050734</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        (Intercept)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1    -1.208364   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1    -0.574420   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1    -1.208616   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1    -1.208621   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1    -0.569616   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1    -1.198076   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1    -1.208608   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1    -1.198345   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1    -3.487106   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1    -0.185083   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1    -1.201127   \n",
       "\n",
       "                                        groupHER2BC_Labeled_inhib_added  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                         2.257855   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                         4.971045   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                         5.504467   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                         2.715359   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                         2.868360   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                         2.754660   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                         5.523083   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                         2.174123   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                         5.752785   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                         3.387633   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                         4.536598   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                         2.396463   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                         3.355754   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                         5.644515   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                         5.485497   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                         5.199196   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                         1.789590   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                         2.431153   \n",
       "\n",
       "                                        groupTNBC_Labeled_inhib_added  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                       1.596220   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                       4.093946   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                       1.181261   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                      -2.278491   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                       0.923873   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                      -0.368419   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                       4.727871   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                       1.984455   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                       4.352456   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                       2.073957   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                       2.750383   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                       3.874963   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                       3.594175   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                       3.990437   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                      -0.099665   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                       2.492743   \n",
       "\n",
       "                                          UMAP_1    UMAP_2  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1  6.784558  6.086753   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1  5.505533 -1.606483   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1  4.317109  0.651766   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  5.871239  4.877238   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1  5.571806  5.048438   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1  4.288418  6.644714   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1  6.333059  0.030782   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1  5.251798  6.231791   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1  6.515573  3.002038   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1  3.653918  0.246762   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1  7.716782 -1.929050   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1  6.178156  4.303478   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1  6.885855  3.034356   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1  4.874705  0.786061   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1  5.626551  1.015566   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1  3.987141  5.834962   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1  5.975085  6.063810   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1  7.599732  3.952429   \n",
       "\n",
       "                                        TNvsHER2_neg_log10(SpatialFDR)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                        0.849080   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                        1.780537   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                        2.952230   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                        3.372030   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                        7.650844   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                        3.730161   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                        3.243834   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                        4.969519   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                        2.173543   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                        3.158579   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                        0.175013   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                        0.358634   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                        0.983527   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                        3.870483   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                        3.870483   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                        2.287333   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                        3.800295   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                        0.061820   \n",
       "\n",
       "                                        TNvsHER2_logFC  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1       -0.954538   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1       -1.414719   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1       -2.034951   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1       -2.213236   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1       -7.425336   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1       -2.641267   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1       -2.211142   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1       -3.668111   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1       -1.478638   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1       -2.024358   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1       -0.265661   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1       -0.465278   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1       -0.873365   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1       -2.552924   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1       -2.728600   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1       -1.743871   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1       -2.725619   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1        0.088855   \n",
       "\n",
       "                                        TNvsCtrl_neg_log10(SpatialFDR)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                        1.108281   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                        2.117366   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                        2.123225   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                        0.910424   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                        0.672091   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                        0.416770   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                        2.117366   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                        0.130056   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                        3.278711   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                        1.677719   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                        2.753976   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                        1.965558   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                        3.278711   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                        1.965558   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                        1.632009   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                        2.117366   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                        0.048406   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                        2.753976   \n",
       "\n",
       "                                        TNvsCtrl_logFC  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1        2.302859   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1        5.756983   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1        5.906316   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1        1.704199   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1       -3.287167   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1        1.332867   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1        5.756983   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1       -0.531516   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1        6.820876   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1        2.862964   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1        6.279266   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1        2.992087   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1        3.967964   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1        5.590389   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1        5.185298   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1        5.756983   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1       -0.143786   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1        3.596267   \n",
       "\n",
       "                                        HER2vsCtrl_neg_log10(SpatialFDR)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                          2.890810   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                          5.752000   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                          7.518279   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                          6.105733   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                          5.257264   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                          4.692660   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                          7.518279   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                          4.108428   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                          8.608748   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                          7.147466   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                          4.099064   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                          3.299535   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                          7.069361   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                          8.080907   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                          7.501858   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                          6.445610   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                          3.388511   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                          3.299535   \n",
       "\n",
       "                                        HER2vsCtrl_logFC  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1          3.257397   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1          7.171702   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1          7.941267   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1          3.917434   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1          4.138168   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1          3.974135   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1          7.968125   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1          3.136596   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1          8.299514   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1          4.887322   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1          6.544927   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1          3.457365   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1          4.841329   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1          8.143314   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1          7.913899   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1          7.500854   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1          2.581833   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1          3.507413   \n",
       "\n",
       "                                        LabeledvsCtrl_neg_log10(SpatialFDR)  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                             2.334188   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                             5.414358   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                             7.735831   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                             6.632005   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                             9.313412   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                             5.885967   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                             8.011858   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                             6.632005   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                             8.467735   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                             7.387751   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                             3.515692   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                             2.652890   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                             6.373341   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                             8.912225   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                             8.406671   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                             6.365992   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                             4.933244   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                             3.050734   \n",
       "\n",
       "                                        nhood_size  \n",
       "SG-602_d27_inh_mCh_165260070608243-1-1          29  \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1          40  \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1          63  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1          72  \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1          38  \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1          38  \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1          63  \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1          39  \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1          87  \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1          76  \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1          33  \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1          37  \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1          88  \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1          69  \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1          58  \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1          48  \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1          42  \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1          45  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for k in ['TNvsHER2', 'TNvsCtrl', 'HER2vsCtrl', 'LabeledvsCtrl']:\n",
    "    \n",
    "    da[k] = pd.read_csv(MILO_DIR+'Tr_tl_inhibitors_Added_Proliferating microglia_milo.da_'+k+'.csv', index_col=0)\n",
    "\n",
    "    # Note \"-1\" comes from the fact that index starts from 1 in R and from 0 in Python\n",
    "    # To match the Milo results obtained in R to Python, need to -1\n",
    "    da[k].loc[:,'Nhood'] = da[k].index-1\n",
    "\n",
    "    da[k].index = nhoodIdx.index\n",
    "    da[k].loc[:,'neg_log10(SpatialFDR)'] = -np.log10(da[k].loc[:,'SpatialFDR'])\n",
    "\n",
    "    # Total No. of cells in the neighborhood\n",
    "    da[k].loc[:,'nhood_size'] = nhoodCounts.sum(axis=1).to_numpy()\n",
    "    \n",
    "    # Update results to adata\n",
    "    adata.obs[k+'_neg_log10(SpatialFDR)'] = np.nan\n",
    "    adata.obs[k+'_neg_log10(SpatialFDR)'][da[k].index] = da[k].loc[:,'neg_log10(SpatialFDR)']\n",
    "    fit.loc[:,k+'_neg_log10(SpatialFDR)'] = da[k].loc[fit.index,'neg_log10(SpatialFDR)']\n",
    "    \n",
    "    if k != 'LabeledvsCtrl':\n",
    "        adata.obs[k+'_logFC'] = np.nan\n",
    "        adata.obs[k+'_logFC'][da[k].index] = da[k].loc[:,'logFC']\n",
    "        fit.loc[:,k+'_logFC'] = da[k].loc[fit.index,'logFC']\n",
    "        \n",
    "fit.loc[:,'nhood_size'] = da['TNvsHER2'].loc[fit.index,'nhood_size']\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521d1369",
   "metadata": {},
   "source": [
    "### Adjacency matrix of neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb2cca85",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22</td>\n",
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       "      <td>17</td>\n",
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       "      <th>41</th>\n",
       "      <td>7</td>\n",
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       "      <td>19</td>\n",
       "      <td>4</td>\n",
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       "      <td>16</td>\n",
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       "      <td>6</td>\n",
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       "      <td>8</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
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       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
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       "      <td>3</td>\n",
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       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "     32  65  44  18  41  153  45  35  43  81  33  75  64  34  104  37  121  \\\n",
       "32   29   0   0  19   7    8   0   7   7   0   0   7   8   0    0   8   21   \n",
       "65    0  40  14   0   0    0  11   0   1  18  19   0   0   8    5   1    0   \n",
       "44    0  14  63   1   0    0  25   0  17  40   3   1   6  43   23   0    0   \n",
       "18   19   0   1  72  24   11   0  18  27   2   0  22  35   2    2  12   28   \n",
       "41    7   0   0  24  38   15   0  21  14   2   0   4  14   0    0   9   19   \n",
       "153   8   0   0  11  15   38   0  20   4   0   0   3   4   0    0  24   17   \n",
       "45    0  11  25   0   0    0  63   0   7  11   5   0   6  35   40   0    0   \n",
       "35    7   0   0  18  21   20   0  39   3   0   0   2   4   2    2  11   23   \n",
       "43    7   1  17  27  14    4   7   3  87  35   3  13  51  14    7  12    6   \n",
       "81    0  18  40   2   2    0  11   0  35  76   8   3  21  25    6   1    0   \n",
       "33    0  19   3   0   0    0   5   0   3   8  33   0   3   0    0   4    0   \n",
       "75    7   0   1  22   4    3   0   2  13   3   0  37  10   0    0   7    7   \n",
       "64    8   0   6  35  14    4   6   4  51  21   3  10  88   9   11  10    7   \n",
       "34    0   8  43   2   0    0  35   2  14  25   0   0   9  69   39   0    0   \n",
       "104   0   5  23   2   0    0  40   2   7   6   0   0  11  39   58   0    1   \n",
       "37    8   1   0  12   9   24   0  11  12   1   4   7  10   0    0  48    8   \n",
       "121  21   0   0  28  19   17   0  23   6   0   0   7   7   0    1   8   42   \n",
       "118   9   0   1  17   4    1   0   1  20   9   0  16  31   1    2   3    6   \n",
       "\n",
       "     118  \n",
       "32     9  \n",
       "65     0  \n",
       "44     1  \n",
       "18    17  \n",
       "41     4  \n",
       "153    1  \n",
       "45     0  \n",
       "35     1  \n",
       "43    20  \n",
       "81     9  \n",
       "33     0  \n",
       "75    16  \n",
       "64    31  \n",
       "34     1  \n",
       "104    2  \n",
       "37     3  \n",
       "121    6  \n",
       "118   45  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of neighbors: i.e., number of cells shared between different neighborhoods (off-diagonal)\n",
    "# On the diagonal line, number of cells per neighborhood \n",
    "\n",
    "nhoodAdj = pd.read_csv(MILO_DIR+'Tr_tl_inhibitors_Added_Proliferating microglia_milo.nhoodIndex.adj.csv', index_col=0)\n",
    "nhoodAdj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "050ea0b9",
   "metadata": {},
   "outputs": [
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       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120726897682292-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_230610192485676-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_135621978577187-1-1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_235686931623196-1-1</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170677547482357-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_121812464823195-1-1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_195617061460406-1-1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>87</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_206068851521894-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_170328415233844-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_126820516542699-1-1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_240550874605990-1-1</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134515756001013-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_120856552032182-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_226829501058356-1-1</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_157140462496684-1-1</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134034853938908-1-1</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        SG-602_d27_inh_mCh_165260070608243-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                      29   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       9   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_120726897682292-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                      40   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      18   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       5   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       0   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_230610192485676-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      63   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      25   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      17   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      40   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      43   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                      23   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       1   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_204411025409907-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      72   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      24   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      18   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      27   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                      22   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      12   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      28   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                      17   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_135621978577187-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      24   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      38   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      15   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       9   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       4   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_235686931623196-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      15   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      38   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      20   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      24   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      17   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       1   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_170677547482357-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      25   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      63   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       5   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                      40   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       0   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_121812464823195-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      18   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      20   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      39   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      23   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       1   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_195617061460406-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      17   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      27   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      87   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                      13   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      51   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      12   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                      20   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_206068851521894-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                      18   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      40   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      76   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      25   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       9   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_170328415233844-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       5   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                      33   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       0   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_126820516542699-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      22   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      13   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                      37   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      10   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                      16   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_240550874605990-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      51   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       3   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                      10   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      88   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       9   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      10   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                      31   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_134515756001013-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      43   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      35   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      14   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                      25   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       9   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      69   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                      39   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       1   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_120856552032182-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       5   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                      23   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                      40   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       2   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                      39   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                      58   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       2   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_226829501058356-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      12   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       9   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      24   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      11   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      12   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       4   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      10   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                      48   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       3   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_157140462496684-1-1  \\\n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                      21   \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      28   \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                      19   \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                      17   \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                      23   \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                       6   \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                       7   \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       0   \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       1   \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       8   \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                      42   \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                       6   \n",
       "\n",
       "                                        SG-602_d27_inh_mCh_134034853938908-1-1  \n",
       "SG-602_d27_inh_mCh_165260070608243-1-1                                       9  \n",
       "SG-602_d27_inh_mCh_120726897682292-1-1                                       0  \n",
       "SG-602_d27_inh_mCh_230610192485676-1-1                                       1  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                                      17  \n",
       "SG-602_d27_inh_mCh_135621978577187-1-1                                       4  \n",
       "SG-602_d27_inh_mCh_235686931623196-1-1                                       1  \n",
       "SG-602_d27_inh_mCh_170677547482357-1-1                                       0  \n",
       "SG-602_d27_inh_mCh_121812464823195-1-1                                       1  \n",
       "SG-602_d27_inh_mCh_195617061460406-1-1                                      20  \n",
       "SG-602_d27_inh_mCh_206068851521894-1-1                                       9  \n",
       "SG-602_d27_inh_mCh_170328415233844-1-1                                       0  \n",
       "SG-602_d27_inh_mCh_126820516542699-1-1                                      16  \n",
       "SG-602_d27_inh_mCh_240550874605990-1-1                                      31  \n",
       "SG-602_d27_inh_mCh_134515756001013-1-1                                       1  \n",
       "SG-602_d27_inh_mCh_120856552032182-1-1                                       2  \n",
       "SG-602_d27_inh_mCh_226829501058356-1-1                                       3  \n",
       "SG-602_d27_inh_mCh_157140462496684-1-1                                       6  \n",
       "SG-602_d27_inh_mCh_134034853938908-1-1                                      45  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update row and column names to obs_names of the index cells of the neighborhoods \n",
    "nhoodAdj.index = nhoodIdx.index\n",
    "nhoodAdj.columns = nhoodIdx.index\n",
    "nhoodAdj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08cf3f94",
   "metadata": {},
   "source": [
    "# Analysis & Visualization\n",
    "\n",
    "## Examine the differential sample distribution \n",
    "\n",
    "Since many visualization and analysis steps rely on a random number seed, each run may produce slight variations. For consistency in this illustration, we load and use the data as processed in the manuscript.\n",
    "\n",
    "### HER2BC- vs. TNBC-labeled\n",
    "\n",
    "Due to the limited number of unlabeled cells in non-cycling microglia, we compare HER2BC-labeled microglia to TNBC-labeled cells rather than to unlabeled cells here. Please refer to the Methods section of the paper for a detailed description of the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3dba6933",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit.loc[:,'HER2vsTN_logFC'] = - fit.loc[:,'TNvsHER2_logFC']\n",
    "fit.loc[:,'HER2vsTN_neg_log10(SpatialFDR)'] = fit.loc[:,'TNvsHER2_neg_log10(SpatialFDR)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067569dd",
   "metadata": {},
   "source": [
    "<b> HER2BC-associated cycling microglia </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dbc12b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "FDRthre = 1\n",
    "k = 'TNvsHER2'\n",
    "\n",
    "incl = (da[k].loc[:,'neg_log10(SpatialFDR)'] >= FDRthre) & \\\n",
    "       (da[k].loc[:,'logFC']<0)\n",
    "da_sig_idx = da[k].index[incl] \n",
    "# Index cells of the neighborhoods w/ significant differential abundance in HER2BC-labeled in ref. to TNBC-labeled microglia "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b96456",
   "metadata": {},
   "source": [
    "Create the 'nhood_class' obs. to save the classification of neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c414bdf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['nhood_class'] = 'TNBC_cycling' # Default: associated w/ TNBC-labeled cycling microliga\n",
    "adata.obs['nhood_class'][da_sig_idx] = 'HER2BC_cycling' # da_sig_idx: associated w/ HER2BC-labeled cycling microliga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eefbe97a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 328,
       "width": 633
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[4,4])\n",
    "k = 'HER2vsTN'\n",
    "\n",
    "# Show all cells\n",
    "umap = pd.DataFrame(adata.obsm['X_umap'],\n",
    "                    columns = [\"UMAP_1\",\"UMAP_2\"])\n",
    "\n",
    "# Plot all dots\n",
    "ax = sns.scatterplot(data=umap, x=\"UMAP_1\", y=\"UMAP_2\", \n",
    "                     alpha=1, color='gainsboro', s=50) \n",
    "\n",
    "# Highlight the index cells of \"null\" neighborhoods (i.e., not differentially enriched in \n",
    "# HER2BC-labeled cells compared to TNBC-labeled cells) as blue dots\n",
    "ax = sns.scatterplot(data=fit.loc[-incl,:], x=\"UMAP_1\", y=\"UMAP_2\", \n",
    "                     alpha=1, color='#00ace6', edgecolor = '#00ace6', s=100) \n",
    "\n",
    "sns.scatterplot(data=fit.loc[incl,:],\n",
    "                x=\"UMAP_1\", y=\"UMAP_2\", \n",
    "                size=k+'_neg_log10(SpatialFDR)', \n",
    "                hue=k+'_logFC', \n",
    "                palette=mpl.cm.seismic,\n",
    "                hue_norm=(-6,6),\n",
    "                sizes=(150/2,400/2),\n",
    "                edgecolor = '#d96a9e',\n",
    "                linewidth=3)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e08229",
   "metadata": {},
   "source": [
    "## Examine homeostasis-to-DAM transition together w/ non-cycling microglia "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "12d8f383",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Signatures of interest\n",
    "features = ['nhood_Amit_homeostatic_SCANPY_score',\n",
    "            'nhood_TGFb_in_vivo_UP_SCANPY_score',\n",
    "            'nhood_Disease-associated_MG_SCANPY_score',\n",
    "            'nhood_Amit_stage 1 DAM_UP_SCANPY_score',\n",
    "            'nhood_Amit_stage 2 DAM_SCANPY_score',\n",
    "            'nhood_HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score',\n",
    "            'nhood_HALLMARK_GLYCOLYSIS_SCANPY_score',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5d665ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neighborhood index cells\n",
    "MG_idx = adata.obs_names[adata.obs['nhoodIdx']]\n",
    "values = adata[MG_idx].obs[features].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c797479",
   "metadata": {},
   "source": [
    "### Load non-cycling microglia data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ca2b583d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sub-directory for the data of non-cycling microglia from experiment 2\n",
    "source_0 = 'Non_Cycling_MG_2/'\n",
    "\n",
    "# Processed adata\n",
    "for h5_file in Path(DATA_DIR+source_0).glob('*.h5'):    \n",
    "    k = str(h5_file).split('.h5')[0].split('_')[-1]\n",
    "    adata_0 = sc.read(h5_file) # Log-transformed, normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d57b5e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amit_homeostatic_SCANPY_score</th>\n",
       "      <th>TGFb_in_vivo_UP_SCANPY_score</th>\n",
       "      <th>Disease-associated_MG_SCANPY_score</th>\n",
       "      <th>Amit_stage 1 DAM_UP_SCANPY_score</th>\n",
       "      <th>Amit_stage 2 DAM_SCANPY_score</th>\n",
       "      <th>HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score</th>\n",
       "      <th>HALLMARK_GLYCOLYSIS_SCANPY_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_neg_169107449396019-0-1</th>\n",
       "      <td>1.445795</td>\n",
       "      <td>0.664026</td>\n",
       "      <td>0.991157</td>\n",
       "      <td>1.660492</td>\n",
       "      <td>0.384841</td>\n",
       "      <td>0.495108</td>\n",
       "      <td>0.145941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_neg_130673819466478-0-1</th>\n",
       "      <td>2.250806</td>\n",
       "      <td>1.151333</td>\n",
       "      <td>1.222650</td>\n",
       "      <td>1.939929</td>\n",
       "      <td>0.511068</td>\n",
       "      <td>0.421581</td>\n",
       "      <td>-0.009663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_230816753015667-1-1</th>\n",
       "      <td>0.323466</td>\n",
       "      <td>0.412494</td>\n",
       "      <td>1.289732</td>\n",
       "      <td>1.923384</td>\n",
       "      <td>0.599391</td>\n",
       "      <td>0.714306</td>\n",
       "      <td>0.152595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_226764285697389-1-1</th>\n",
       "      <td>1.005756</td>\n",
       "      <td>0.677703</td>\n",
       "      <td>1.321730</td>\n",
       "      <td>1.821086</td>\n",
       "      <td>0.821359</td>\n",
       "      <td>0.592221</td>\n",
       "      <td>0.131019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134393214979310-1-1</th>\n",
       "      <td>1.942714</td>\n",
       "      <td>0.752083</td>\n",
       "      <td>1.161190</td>\n",
       "      <td>1.938618</td>\n",
       "      <td>0.434791</td>\n",
       "      <td>0.752012</td>\n",
       "      <td>0.110913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_164698921000357-1-1</th>\n",
       "      <td>2.965529</td>\n",
       "      <td>1.333858</td>\n",
       "      <td>0.549314</td>\n",
       "      <td>1.201823</td>\n",
       "      <td>-0.359223</td>\n",
       "      <td>0.319027</td>\n",
       "      <td>0.012640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_134585682615589-1-1</th>\n",
       "      <td>2.863232</td>\n",
       "      <td>1.132853</td>\n",
       "      <td>0.564761</td>\n",
       "      <td>1.000971</td>\n",
       "      <td>-0.570891</td>\n",
       "      <td>0.229467</td>\n",
       "      <td>0.034076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_157148381133043-1-1</th>\n",
       "      <td>3.425159</td>\n",
       "      <td>1.138527</td>\n",
       "      <td>0.210419</td>\n",
       "      <td>0.883463</td>\n",
       "      <td>-0.577172</td>\n",
       "      <td>0.244278</td>\n",
       "      <td>0.009433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>3.043032</td>\n",
       "      <td>1.218085</td>\n",
       "      <td>0.558298</td>\n",
       "      <td>0.650160</td>\n",
       "      <td>-0.349640</td>\n",
       "      <td>0.258472</td>\n",
       "      <td>0.003924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_191648484776179-1-1</th>\n",
       "      <td>2.836087</td>\n",
       "      <td>1.124874</td>\n",
       "      <td>0.488261</td>\n",
       "      <td>1.128378</td>\n",
       "      <td>-0.254057</td>\n",
       "      <td>0.255831</td>\n",
       "      <td>0.005659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Amit_homeostatic_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                       1.445795   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                       2.250806   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                       0.323466   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                       1.005756   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                       1.942714   \n",
       "...                                                               ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                       2.965529   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                       2.863232   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                       3.425159   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                       3.043032   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                       2.836087   \n",
       "\n",
       "                                        TGFb_in_vivo_UP_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                      0.664026   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                      1.151333   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                      0.412494   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                      0.677703   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                      0.752083   \n",
       "...                                                              ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                      1.333858   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                      1.132853   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                      1.138527   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                      1.218085   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                      1.124874   \n",
       "\n",
       "                                        Disease-associated_MG_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                            0.991157   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                            1.222650   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                            1.289732   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                            1.321730   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                            1.161190   \n",
       "...                                                                    ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                            0.549314   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                            0.564761   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                            0.210419   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                            0.558298   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                            0.488261   \n",
       "\n",
       "                                        Amit_stage 1 DAM_UP_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                          1.660492   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                          1.939929   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                          1.923384   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                          1.821086   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                          1.938618   \n",
       "...                                                                  ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                          1.201823   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                          1.000971   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                          0.883463   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                          0.650160   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                          1.128378   \n",
       "\n",
       "                                        Amit_stage 2 DAM_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                       0.384841   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                       0.511068   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                       0.599391   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                       0.821359   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                       0.434791   \n",
       "...                                                               ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                      -0.359223   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                      -0.570891   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                      -0.577172   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                      -0.349640   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                      -0.254057   \n",
       "\n",
       "                                        HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score  \\\n",
       "SG-602_d27_inh_neg_169107449396019-0-1                                         0.495108   \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                                         0.421581   \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                                         0.714306   \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                                         0.592221   \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                                         0.752012   \n",
       "...                                                                                 ...   \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                                         0.319027   \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                                         0.229467   \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                                         0.244278   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                                         0.258472   \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                                         0.255831   \n",
       "\n",
       "                                        HALLMARK_GLYCOLYSIS_SCANPY_score  \n",
       "SG-602_d27_inh_neg_169107449396019-0-1                          0.145941  \n",
       "SG-602_d27_inh_neg_130673819466478-0-1                         -0.009663  \n",
       "SG-602_d27_inh_mCh_230816753015667-1-1                          0.152595  \n",
       "SG-602_d27_inh_mCh_226764285697389-1-1                          0.131019  \n",
       "SG-602_d27_inh_mCh_134393214979310-1-1                          0.110913  \n",
       "...                                                                  ...  \n",
       "SG-602_d27_inh_mCh_164698921000357-1-1                          0.012640  \n",
       "SG-602_d27_inh_mCh_134585682615589-1-1                          0.034076  \n",
       "SG-602_d27_inh_mCh_157148381133043-1-1                          0.009433  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                          0.003924  \n",
       "SG-602_d27_inh_mCh_191648484776179-1-1                          0.005659  \n",
       "\n",
       "[98 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MG_idx_0 = adata_0.obs_names[adata_0.obs['nhoodIdx']]\n",
    "\n",
    "adata_0[MG_idx_0].obs[['HER2vsCtrl_logFC','TNvsCtrl_logFC','TNvsHER2_logFC','LabeledvsCtrl_Nhood_merged']]\n",
    "adata_0[MG_idx_0].obs[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b5a72b",
   "metadata": {},
   "source": [
    "### Classify neighborhoods for non-cycling microglia\n",
    "\n",
    "Print the number of neighborhoods within each category. \n",
    "\n",
    "<b> Unlabeled </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "698811dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FDRthre = 1\n",
    "UN_idx = (adata_0[MG_idx_0].obs['TNvsCtrl_logFC'] < 1) & (adata_0[MG_idx_0].obs['HER2vsCtrl_logFC'] < 1) \n",
    "np.sum(UN_idx == True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6196edb",
   "metadata": {},
   "source": [
    "<b> TNBC-associated </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02bcc392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TNBC_idx = (adata_0[MG_idx_0].obs['TNvsCtrl_logFC'] > 0) & (adata_0[MG_idx_0].obs['TNvsHER2_logFC'] > 0) & \\\n",
    "           (adata_0[MG_idx_0].obs['LabeledvsCtrl_neg_log10(SpatialFDR)'] >= FDRthre)\n",
    "\n",
    "np.sum(TNBC_idx == True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f727e79",
   "metadata": {},
   "source": [
    "<b> HER2BC-associated </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c7930968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HER2BC_idx = (adata_0[MG_idx_0].obs['HER2vsCtrl_logFC'] > 0) & (adata_0[MG_idx_0].obs['TNvsHER2_logFC'] < 0) & \\\n",
    "             (adata_0[MG_idx_0].obs['LabeledvsCtrl_neg_log10(SpatialFDR)'] >= FDRthre)\n",
    "\n",
    "np.sum(HER2BC_idx == True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e8fc6d",
   "metadata": {},
   "source": [
    "Copy to obs. 'nhood_class' to save the updated adata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "516e1a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update the results in adata to save\n",
    "adata_0.obs['nhood_class'] = 'Unclassified'\n",
    "\n",
    "adata_0.obs['nhood_class'][MG_idx_0[UN_idx]] = 'Unlabeled_nonCycling'\n",
    "adata_0.obs['nhood_class'][MG_idx_0[TNBC_idx]] = 'TNBC_nonCycling'\n",
    "adata_0.obs['nhood_class'][MG_idx_0[HER2BC_idx]] = 'HER2BC_nonCycling'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255d956c",
   "metadata": {},
   "source": [
    "<b> Signature values for non-cycling microglia </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e06d65d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "values_0 = adata_0[adata_0.obs['nhood_class'] != 'Unclassified'].obs[features].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871954a3",
   "metadata": {},
   "source": [
    "### Combine & visualize non-cycling and cycling microglia data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "214b9423",
   "metadata": {},
   "outputs": [],
   "source": [
    "values_all = pd.concat([values, values_0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4766f51b",
   "metadata": {},
   "source": [
    "Visualize the combined, scaled scores using violin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a91d16c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sig_scaled = pd.DataFrame(scaler.fit_transform(values_all.to_numpy()),\n",
    "                              index = values_all.index,\n",
    "                              columns = values_all.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8b5ffbcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>signatures</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cells</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>Amit_homeostatic_SCANPY_score</td>\n",
       "      <td>0.286132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>TGFb_in_vivo_UP_SCANPY_score</td>\n",
       "      <td>0.171358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>Disease-associated_MG_SCANPY_score</td>\n",
       "      <td>0.393377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>Amit_stage 1 DAM_UP_SCANPY_score</td>\n",
       "      <td>0.195292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_204411025409907-1-1</th>\n",
       "      <td>Amit_stage 2 DAM_SCANPY_score</td>\n",
       "      <td>0.276702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>Disease-associated_MG_SCANPY_score</td>\n",
       "      <td>0.248526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>Amit_stage 1 DAM_UP_SCANPY_score</td>\n",
       "      <td>0.049107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>Amit_stage 2 DAM_SCANPY_score</td>\n",
       "      <td>0.113905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score</td>\n",
       "      <td>0.122218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-602_d27_inh_mCh_125168073750254-1-1</th>\n",
       "      <td>HALLMARK_GLYCOLYSIS_SCANPY_score</td>\n",
       "      <td>0.023094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                             signatures  \\\n",
       "cells                                                                                     \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                    Amit_homeostatic_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                     TGFb_in_vivo_UP_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1               Disease-associated_MG_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                 Amit_stage 1 DAM_UP_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1                    Amit_stage 2 DAM_SCANPY_score   \n",
       "...                                                                                 ...   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1               Disease-associated_MG_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                 Amit_stage 1 DAM_UP_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                    Amit_stage 2 DAM_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score   \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1                 HALLMARK_GLYCOLYSIS_SCANPY_score   \n",
       "\n",
       "                                          scores  \n",
       "cells                                             \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  0.286132  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  0.171358  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  0.393377  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  0.195292  \n",
       "SG-602_d27_inh_mCh_204411025409907-1-1  0.276702  \n",
       "...                                          ...  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  0.248526  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  0.049107  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  0.113905  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  0.122218  \n",
       "SG-602_d27_inh_mCh_125168073750254-1-1  0.023094  \n",
       "\n",
       "[546 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = all_sig_scaled.stack().reset_index()\n",
    "x.columns = ['cells', 'signatures', 'scores']\n",
    "x = x.set_index(['cells'])\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce3f105",
   "metadata": {},
   "source": [
    "Update the neighborhood classification to x (matrix created for visualization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ad87d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.loc[:,'nhood_class'] = 'TNBC_cycling'\n",
    "\n",
    "x.loc[MG_idx_0[UN_idx],'nhood_class'] = 'Unlabeled_nonCycling'\n",
    "x.loc[MG_idx_0[TNBC_idx],'nhood_class'] = 'TNBC_nonCycling'\n",
    "x.loc[MG_idx_0[HER2BC_idx],'nhood_class'] = 'HER2BC_nonCycling'\n",
    "\n",
    "x.loc[da_sig_idx,'nhood_class'] = 'HER2BC_cycling'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "71d41166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]),\n",
       " [Text(0, 0, 'Amit_homeostatic_SCANPY_score'),\n",
       "  Text(1, 0, 'TGFb_in_vivo_UP_SCANPY_score'),\n",
       "  Text(2, 0, 'Disease-associated_MG_SCANPY_score'),\n",
       "  Text(3, 0, 'Amit_stage 1 DAM_UP_SCANPY_score'),\n",
       "  Text(4, 0, 'Amit_stage 2 DAM_SCANPY_score'),\n",
       "  Text(5, 0, 'HALLMARK_OXIDATIVE_PHOSPHORYLATION_SCANPY_score'),\n",
       "  Text(6, 0, 'HALLMARK_GLYCOLYSIS_SCANPY_score')])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 762,
       "width": 1577
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,4])\n",
    "\n",
    "ax = sns.boxplot(data=x, x=\"signatures\", y=\"scores\", hue=\"nhood_class\",  \n",
    "                 hue_order=['Unlabeled_nonCycling', 'TNBC_nonCycling', 'TNBC_cycling', \n",
    "                            'HER2BC_nonCycling', 'HER2BC_cycling'], saturation=1,\n",
    "                 palette=['#21474F','#75C8E2','#00ace6','#F599B5','#d96a9e'], \n",
    "                ) \n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1490960f",
   "metadata": {},
   "source": [
    "# Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "77fa2c9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'nhood_class' as categorical\n",
      "... storing 'nhood_class' as categorical\n"
     ]
    }
   ],
   "source": [
    "# Save udpated adata to the original directory\n",
    "# Cycling microglia\n",
    "for h5_file in Path(DATA_DIR+source).glob('*.h5'):   \n",
    "    adata.write(h5_file)\n",
    "\n",
    "# Non-cycling microglia\n",
    "for h5_file in Path(DATA_DIR+source_0).glob('*.h5'):   \n",
    "    adata_0.write(h5_file)    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:notebook]",
   "language": "python",
   "name": "conda-env-notebook-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}