30_borderexprs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nplay around with plotting gene expression across a border\\n\\nShaina Lu\\nZador and Gillis Labs\\nSeptember 2020\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "play around with plotting gene expression across a border\n",
    "\n",
    "Shaina Lu\n",
    "Zador and Gillis Labs\n",
    "September 2020\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import h5py\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_style():\n",
    "    #function for matplotlib formatting\n",
    "    plt.style.use(['seaborn-white','seaborn-notebook'])\n",
    "    plt.rcParams['figure.figsize'] = [6,4]\n",
    "    plt.rcParams['axes.spines.top'] = False       #remove top line\n",
    "    plt.rcParams['axes.spines.right'] = False     #remove right line\n",
    "    plt.rcParams['axes.linewidth'] = 2.0          #set weight of axes\n",
    "    plt.rcParams['axes.titlesize'] = 20           #set font size of title\n",
    "    plt.rcParams['axes.labelsize'] = 18           #set font size of x,y labels\n",
    "    plt.rcParams['axes.labelpad'] = 14            #space between labels and axes\n",
    "    plt.rcParams['xtick.labelsize'] = 14          #set x label size\n",
    "    plt.rcParams['ytick.labelsize'] = 14          #set y label size\n",
    "    plt.rcParams['legend.fontsize'] = 16          #set legend font size\n",
    "    \n",
    "set_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in files and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#file paths\n",
    "ALLEN_FILT_PATH = \"/home/slu/spatial/data/ABAISH_filt_v6_avgdup.h5\"\n",
    "ONTOLOGY_PATH = \"/data/slu/allen_adult_mouse_ISH/ontologyABA.csv\"\n",
    "ST_CANTIN_FILT_PATH = \"/home/slu/spatial/data/cantin_ST_filt_v2.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ABA\n",
    "def read_ABAdata():\n",
    "    \"\"\"read in all ABA datasets needed using pandas\"\"\"\n",
    "    metabrain = pd.read_hdf(ALLEN_FILT_PATH, key='metabrain', mode='r')\n",
    "    voxbrain = pd.read_hdf(ALLEN_FILT_PATH, key='avgvoxbrain', mode='r')\n",
    "    propontvox = pd.read_hdf(ALLEN_FILT_PATH, key='propontology', mode='r')\n",
    "    #geneIDName = pd.read_hdf(ALLEN_FILT_PATH, key='geneIDName', mode='r')\t\n",
    "\n",
    "    return metabrain, voxbrain, propontvox\n",
    "\n",
    "#ST\n",
    "def read_STdata():\n",
    "    \"\"\"read in all ST datasets needed using pandas\"\"\"\n",
    "    STspotsmeta = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STspotsmeta', mode='r')\n",
    "    STspots = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STspots', mode='r')\n",
    "    STpropont = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STpropont', mode='r')\n",
    "    \n",
    "    return STspotsmeta, STspots, STpropont\n",
    "\n",
    "def read_ontology():\n",
    "    ontology = pd.read_csv(ONTOLOGY_PATH)\n",
    "    ontology = ontology.drop([ontology.columns[5], ontology.columns[6]], axis=1)\n",
    "    ontology = ontology.fillna(-1)  #make root's parent -1\n",
    "\n",
    "    return ontology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in data\n",
    "ontology = read_ontology()\n",
    "ABAmeta, ABAvox, ABApropont = read_ABAdata()\n",
    "STmeta, STspots, STpropont = read_STdata()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "okay, options:\n",
    "- look in the datasets independently\n",
    "- filter for overlapping areas and genes\n",
    "- look indepedently, but only use genes in both datasets (best I think, b/c don't need leaf resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#copied from 09_crossdataset.ipynb\n",
    "def getoverlapgenes(STspots, ABAvox):\n",
    "    ABAgenes = list(ABAvox)\n",
    "    STgenes = list(STspots)\n",
    "    \n",
    "    #get overlapping genes\n",
    "    overlap = []\n",
    "    for i in range(len(ABAgenes)):\n",
    "        if ABAgenes[i] in STgenes:\n",
    "            overlap.append(ABAgenes[i])\n",
    "    \n",
    "    print(\"number of overlapping genes: %d\" %len(overlap))\n",
    "    \n",
    "    #index datasets to keep only genes that are overlapping\n",
    "    STspots = STspots.loc[:,overlap]\n",
    "    ABAvox = ABAvox.loc[:,overlap]\n",
    "    \n",
    "    return STspots, ABAvox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of overlapping genes: 14299\n"
     ]
    }
   ],
   "source": [
    "#keep only genes that are overlapping between the two datasets\n",
    "STspots, ABAvox = getoverlapgenes(STspots, ABAvox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(STspots) == list(ABAvox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30780, 14299)\n",
      "(62527, 14299)\n",
      "(30780, 11)\n",
      "(62527, 4)\n"
     ]
    }
   ],
   "source": [
    "print(STspots.shape)\n",
    "print(ABAvox.shape)\n",
    "print(STmeta.shape)\n",
    "print(ABAmeta.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in file w/ vox coords\n",
    "COORD_PATH = \"/data/slu/allen_adult_mouse_ISH/voxelCoordinates.csv\"\n",
    "allcoords = pd.read_csv(COORD_PATH)\n",
    "ABAcoords = allcoords[allcoords.index.isin(ABAvox.index)] #keep only those in filtered voxbrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62527, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABAcoords.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### figure out an AP location to start with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>slice_index</th>\n",
       "      <th>ML</th>\n",
       "      <th>DV</th>\n",
       "      <th>AP</th>\n",
       "      <th>acronym</th>\n",
       "      <th>name</th>\n",
       "      <th>nuclei</th>\n",
       "      <th>radius</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02A_15.8x13.04</th>\n",
       "      <td>02A</td>\n",
       "      <td>3.156438</td>\n",
       "      <td>-3.545032</td>\n",
       "      <td>2.245</td>\n",
       "      <td>MOp1</td>\n",
       "      <td>Primary motor area, Layer 1</td>\n",
       "      <td>3</td>\n",
       "      <td>72.832245</td>\n",
       "      <td>3479.641</td>\n",
       "      <td>4936.516</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02A_18.75x13.07</th>\n",
       "      <td>02A</td>\n",
       "      <td>3.012475</td>\n",
       "      <td>-2.692800</td>\n",
       "      <td>2.245</td>\n",
       "      <td>MOp1</td>\n",
       "      <td>Primary motor area, Layer 1</td>\n",
       "      <td>1</td>\n",
       "      <td>76.475977</td>\n",
       "      <td>3491.171</td>\n",
       "      <td>4074.165</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02A_16.74x13.07</th>\n",
       "      <td>02A</td>\n",
       "      <td>3.124975</td>\n",
       "      <td>-3.292800</td>\n",
       "      <td>2.245</td>\n",
       "      <td>MOp1</td>\n",
       "      <td>Primary motor area, Layer 1</td>\n",
       "      <td>1</td>\n",
       "      <td>75.797361</td>\n",
       "      <td>3488.927</td>\n",
       "      <td>4661.036</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02A_17.81x13.08</th>\n",
       "      <td>02A</td>\n",
       "      <td>3.064854</td>\n",
       "      <td>-2.974134</td>\n",
       "      <td>2.245</td>\n",
       "      <td>MOp1</td>\n",
       "      <td>Primary motor area, Layer 1</td>\n",
       "      <td>5</td>\n",
       "      <td>73.206277</td>\n",
       "      <td>3493.646</td>\n",
       "      <td>4348.148</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02A_19.96x14.09</th>\n",
       "      <td>02A</td>\n",
       "      <td>2.831225</td>\n",
       "      <td>-2.405300</td>\n",
       "      <td>2.245</td>\n",
       "      <td>MOp1</td>\n",
       "      <td>Primary motor area, Layer 1</td>\n",
       "      <td>2</td>\n",
       "      <td>77.408797</td>\n",
       "      <td>3783.901</td>\n",
       "      <td>3720.984</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                slice_index        ML        DV     AP acronym  \\\n",
       "02A_15.8x13.04          02A  3.156438 -3.545032  2.245    MOp1   \n",
       "02A_18.75x13.07         02A  3.012475 -2.692800  2.245    MOp1   \n",
       "02A_16.74x13.07         02A  3.124975 -3.292800  2.245    MOp1   \n",
       "02A_17.81x13.08         02A  3.064854 -2.974134  2.245    MOp1   \n",
       "02A_19.96x14.09         02A  2.831225 -2.405300  2.245    MOp1   \n",
       "\n",
       "                                        name  nuclei     radius         x  \\\n",
       "02A_15.8x13.04   Primary motor area, Layer 1       3  72.832245  3479.641   \n",
       "02A_18.75x13.07  Primary motor area, Layer 1       1  76.475977  3491.171   \n",
       "02A_16.74x13.07  Primary motor area, Layer 1       1  75.797361  3488.927   \n",
       "02A_17.81x13.08  Primary motor area, Layer 1       5  73.206277  3493.646   \n",
       "02A_19.96x14.09  Primary motor area, Layer 1       2  77.408797  3783.901   \n",
       "\n",
       "                        y   id  \n",
       "02A_15.8x13.04   4936.516  320  \n",
       "02A_18.75x13.07  4074.165  320  \n",
       "02A_16.74x13.07  4661.036  320  \n",
       "02A_17.81x13.08  4348.148  320  \n",
       "02A_19.96x14.09  3720.984  320  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STmeta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(STmeta.AP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do I determine an AP location in ST that is synonymous with a coronal slice in the x,y,z system of ABA? Start with just working with median of Allen? \\\n",
    "\\\n",
    "From previous visualization (allenadultmouseISH/visualize_twelve.ipynb), x-cooridinate captures AP location in ABA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABAcoords.xcord.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xslice = ABAcoords.loc[ABAcoords.xcord == 38.0]\n",
    "plt.scatter(Xslice.zcord, Xslice.ycord)\n",
    "plt.xlabel(\"Z coordinate\")\n",
    "plt.ylabel(\"Y coordinate\")\n",
    "plt.savefig(\"092320_1.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xline = Xslice.loc[Xslice.zcord == 20]\n",
    "Xlinevox = ABAvox.loc[Xline.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>xcord</th>\n",
       "      <th>ycord</th>\n",
       "      <th>zcord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52431</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52498</th>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52565</th>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52632</th>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52699</th>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       xcord  ycord  zcord\n",
       "52431     38      4     20\n",
       "52498     38      5     20\n",
       "52565     38      6     20\n",
       "52632     38      7     20\n",
       "52699     38      8     20"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Xline is already in the order of increasing ycord so no need to sort\n",
    "Xline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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>ids</th>\n",
       "      <th>acronym</th>\n",
       "      <th>color</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52431</th>\n",
       "      <td>241</td>\n",
       "      <td>PTLp2/3</td>\n",
       "      <td>009FAC</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52498</th>\n",
       "      <td>241</td>\n",
       "      <td>PTLp2/3</td>\n",
       "      <td>009FAC</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52565</th>\n",
       "      <td>683</td>\n",
       "      <td>PTLp5</td>\n",
       "      <td>009FAC</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52632</th>\n",
       "      <td>308</td>\n",
       "      <td>PTLp6a</td>\n",
       "      <td>009FAC</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52699</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52766</th>\n",
       "      <td>407</td>\n",
       "      <td>CA1sp</td>\n",
       "      <td>66A83D</td>\n",
       "      <td>Field C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52833</th>\n",
       "      <td>415</td>\n",
       "      <td>CA1sr</td>\n",
       "      <td>7ED04B</td>\n",
       "      <td>Field C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52900</th>\n",
       "      <td>391</td>\n",
       "      <td>CA1slm</td>\n",
       "      <td>7ED04B</td>\n",
       "      <td>Field C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52967</th>\n",
       "      <td>10703</td>\n",
       "      <td>DG-mo</td>\n",
       "      <td>7ED04B</td>\n",
       "      <td>Dentate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53034</th>\n",
       "      <td>632</td>\n",
       "      <td>DG-sg</td>\n",
       "      <td>66A83D</td>\n",
       "      <td>Dentate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53101</th>\n",
       "      <td>486</td>\n",
       "      <td>CA3so</td>\n",
       "      <td>7ED04B</td>\n",
       "      <td>Field C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53168</th>\n",
       "      <td>218</td>\n",
       "      <td>LP</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Lateral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53235</th>\n",
       "      <td>170</td>\n",
       "      <td>LGd</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Dorsal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53302</th>\n",
       "      <td>549</td>\n",
       "      <td>TH</td>\n",
       "      <td>FF7080</td>\n",
       "      <td>Thalamu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53369</th>\n",
       "      <td>549</td>\n",
       "      <td>TH</td>\n",
       "      <td>FF7080</td>\n",
       "      <td>Thalamu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53436</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53503</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53570</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53637</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53704</th>\n",
       "      <td>549</td>\n",
       "      <td>TH</td>\n",
       "      <td>FF7080</td>\n",
       "      <td>Thalamu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53771</th>\n",
       "      <td>718</td>\n",
       "      <td>VPL</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53838</th>\n",
       "      <td>797</td>\n",
       "      <td>ZI</td>\n",
       "      <td>F2483B</td>\n",
       "      <td>Zona in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53905</th>\n",
       "      <td>797</td>\n",
       "      <td>ZI</td>\n",
       "      <td>F2483B</td>\n",
       "      <td>Zona in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53972</th>\n",
       "      <td>470</td>\n",
       "      <td>STN</td>\n",
       "      <td>F2483B</td>\n",
       "      <td>Subthal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54039</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54106</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54173</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54240</th>\n",
       "      <td>435</td>\n",
       "      <td>MEApv</td>\n",
       "      <td>80C0E2</td>\n",
       "      <td>Medial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54307</th>\n",
       "      <td>435</td>\n",
       "      <td>MEApv</td>\n",
       "      <td>80C0E2</td>\n",
       "      <td>Medial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54374</th>\n",
       "      <td>435</td>\n",
       "      <td>MEApv</td>\n",
       "      <td>80C0E2</td>\n",
       "      <td>Medial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54441</th>\n",
       "      <td>278</td>\n",
       "      <td>sAMY</td>\n",
       "      <td>80C0E2</td>\n",
       "      <td>Striatu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ids  acronym   color     name\n",
       "52431    241  PTLp2/3  009FAC  Posteri\n",
       "52498    241  PTLp2/3  009FAC  Posteri\n",
       "52565    683    PTLp5  009FAC  Posteri\n",
       "52632    308   PTLp6a  009FAC  Posteri\n",
       "52699      8     grey  BFDAE3  Basic c\n",
       "52766    407    CA1sp  66A83D  Field C\n",
       "52833    415    CA1sr  7ED04B  Field C\n",
       "52900    391   CA1slm  7ED04B  Field C\n",
       "52967  10703    DG-mo  7ED04B  Dentate\n",
       "53034    632    DG-sg  66A83D  Dentate\n",
       "53101    486    CA3so  7ED04B  Field C\n",
       "53168    218       LP  FF909F  Lateral\n",
       "53235    170      LGd  FF8084  Dorsal \n",
       "53302    549       TH  FF7080  Thalamu\n",
       "53369    549       TH  FF7080  Thalamu\n",
       "53436    733      VPM  FF8084  Ventral\n",
       "53503    733      VPM  FF8084  Ventral\n",
       "53570    733      VPM  FF8084  Ventral\n",
       "53637    733      VPM  FF8084  Ventral\n",
       "53704    549       TH  FF7080  Thalamu\n",
       "53771    718      VPL  FF8084  Ventral\n",
       "53838    797       ZI  F2483B  Zona in\n",
       "53905    797       ZI  F2483B  Zona in\n",
       "53972    470      STN  F2483B  Subthal\n",
       "54039      8     grey  BFDAE3  Basic c\n",
       "54106      8     grey  BFDAE3  Basic c\n",
       "54173      8     grey  BFDAE3  Basic c\n",
       "54240    435    MEApv  80C0E2  Medial \n",
       "54307    435    MEApv  80C0E2  Medial \n",
       "54374    435    MEApv  80C0E2  Medial \n",
       "54441    278     sAMY  80C0E2  Striatu"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what brain areas are captured in these voxels\n",
    "ABAmeta.loc[Xline.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first try picking a few genes to focus on as those with highest variance\n",
    "Xlinevar = Xlinevox.var(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xlinevar.sort_values(ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Fbxo22'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xlinevar.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    plt.scatter(Xline.ycord, Xlinevox[Xlinevar.index[i]],label=Xlinevar.index[i])\n",
    "plt.xlabel(\"Y coordinate for X=38, Z=20\")\n",
    "plt.ylabel(\"expression\")\n",
    "plt.legend(loc=[1,0])\n",
    "plt.savefig(\"092320_3.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try making a plot crossing in the other direciton, fixed y cord\n",
    "currline = Xslice.loc[Xslice.ycord == 20]\n",
    "currlinevox = ABAvox.loc[currline.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#currline is already in order of increasing zcord so no need to sort\n",
    "#currline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get highest variance genes for these voxels\n",
    "currvar = currlinevox.var(axis=0)\n",
    "currvar.sort_values(ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    plt.scatter(currline.zcord, currlinevox[currvar.index[i]],label=currvar.index[i])\n",
    "plt.xlabel(\"Z coordinate for X=38, Y=20\")\n",
    "plt.ylabel(\"expression\")\n",
    "plt.legend(loc=[1,0])\n",
    "plt.savefig(\"092320_5.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>ids</th>\n",
       "      <th>acronym</th>\n",
       "      <th>color</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9551</th>\n",
       "      <td>959</td>\n",
       "      <td>AUDv1</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12298</th>\n",
       "      <td>755</td>\n",
       "      <td>AUDv2/3</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15045</th>\n",
       "      <td>1023</td>\n",
       "      <td>AUDv5</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17792</th>\n",
       "      <td>1023</td>\n",
       "      <td>AUDv5</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20539</th>\n",
       "      <td>520</td>\n",
       "      <td>AUDv6a</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23286</th>\n",
       "      <td>598</td>\n",
       "      <td>AUDv6b</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26033</th>\n",
       "      <td>672</td>\n",
       "      <td>CP</td>\n",
       "      <td>98D6F9</td>\n",
       "      <td>Caudopu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28780</th>\n",
       "      <td>672</td>\n",
       "      <td>CP</td>\n",
       "      <td>98D6F9</td>\n",
       "      <td>Caudopu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31527</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34274</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37021</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39768</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42515</th>\n",
       "      <td>178</td>\n",
       "      <td>LGv</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45262</th>\n",
       "      <td>178</td>\n",
       "      <td>LGv</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48009</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50756</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53503</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56250</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58997</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61744</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64491</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67238</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69985</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72732</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75479</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78226</th>\n",
       "      <td>59</td>\n",
       "      <td>IMD</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Interme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80973</th>\n",
       "      <td>59</td>\n",
       "      <td>IMD</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Interme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83720</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86467</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89214</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91961</th>\n",
       "      <td>930</td>\n",
       "      <td>PF</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Parafas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94708</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97455</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100202</th>\n",
       "      <td>1020</td>\n",
       "      <td>PO</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Posteri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102949</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105696</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108443</th>\n",
       "      <td>733</td>\n",
       "      <td>VPM</td>\n",
       "      <td>FF8084</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111190</th>\n",
       "      <td>549</td>\n",
       "      <td>TH</td>\n",
       "      <td>FF7080</td>\n",
       "      <td>Thalamu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113937</th>\n",
       "      <td>178</td>\n",
       "      <td>LGv</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116684</th>\n",
       "      <td>178</td>\n",
       "      <td>LGv</td>\n",
       "      <td>FF909F</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119431</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122178</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124925</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127672</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130419</th>\n",
       "      <td>672</td>\n",
       "      <td>CP</td>\n",
       "      <td>98D6F9</td>\n",
       "      <td>Caudopu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133166</th>\n",
       "      <td>8</td>\n",
       "      <td>grey</td>\n",
       "      <td>BFDAE3</td>\n",
       "      <td>Basic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135913</th>\n",
       "      <td>520</td>\n",
       "      <td>AUDv6a</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138660</th>\n",
       "      <td>520</td>\n",
       "      <td>AUDv6a</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141407</th>\n",
       "      <td>1023</td>\n",
       "      <td>AUDv5</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144154</th>\n",
       "      <td>1023</td>\n",
       "      <td>AUDv5</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146901</th>\n",
       "      <td>755</td>\n",
       "      <td>AUDv2/3</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149648</th>\n",
       "      <td>959</td>\n",
       "      <td>AUDv1</td>\n",
       "      <td>019399</td>\n",
       "      <td>Ventral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ids  acronym   color     name\n",
       "9551     959    AUDv1  019399  Ventral\n",
       "12298    755  AUDv2/3  019399  Ventral\n",
       "15045   1023    AUDv5  019399  Ventral\n",
       "17792   1023    AUDv5  019399  Ventral\n",
       "20539    520   AUDv6a  019399  Ventral\n",
       "23286    598   AUDv6b  019399  Ventral\n",
       "26033    672       CP  98D6F9  Caudopu\n",
       "28780    672       CP  98D6F9  Caudopu\n",
       "31527      8     grey  BFDAE3  Basic c\n",
       "34274      8     grey  BFDAE3  Basic c\n",
       "37021      8     grey  BFDAE3  Basic c\n",
       "39768      8     grey  BFDAE3  Basic c\n",
       "42515    178      LGv  FF909F  Ventral\n",
       "45262    178      LGv  FF909F  Ventral\n",
       "48009    733      VPM  FF8084  Ventral\n",
       "50756    733      VPM  FF8084  Ventral\n",
       "53503    733      VPM  FF8084  Ventral\n",
       "56250    733      VPM  FF8084  Ventral\n",
       "58997   1020       PO  FF909F  Posteri\n",
       "61744   1020       PO  FF909F  Posteri\n",
       "64491   1020       PO  FF909F  Posteri\n",
       "67238    930       PF  FF909F  Parafas\n",
       "69985    930       PF  FF909F  Parafas\n",
       "72732    930       PF  FF909F  Parafas\n",
       "75479    930       PF  FF909F  Parafas\n",
       "78226     59      IMD  FF909F  Interme\n",
       "80973     59      IMD  FF909F  Interme\n",
       "83720    930       PF  FF909F  Parafas\n",
       "86467    930       PF  FF909F  Parafas\n",
       "89214    930       PF  FF909F  Parafas\n",
       "91961    930       PF  FF909F  Parafas\n",
       "94708   1020       PO  FF909F  Posteri\n",
       "97455   1020       PO  FF909F  Posteri\n",
       "100202  1020       PO  FF909F  Posteri\n",
       "102949   733      VPM  FF8084  Ventral\n",
       "105696   733      VPM  FF8084  Ventral\n",
       "108443   733      VPM  FF8084  Ventral\n",
       "111190   549       TH  FF7080  Thalamu\n",
       "113937   178      LGv  FF909F  Ventral\n",
       "116684   178      LGv  FF909F  Ventral\n",
       "119431     8     grey  BFDAE3  Basic c\n",
       "122178     8     grey  BFDAE3  Basic c\n",
       "124925     8     grey  BFDAE3  Basic c\n",
       "127672     8     grey  BFDAE3  Basic c\n",
       "130419   672       CP  98D6F9  Caudopu\n",
       "133166     8     grey  BFDAE3  Basic c\n",
       "135913   520   AUDv6a  019399  Ventral\n",
       "138660   520   AUDv6a  019399  Ventral\n",
       "141407  1023    AUDv5  019399  Ventral\n",
       "144154  1023    AUDv5  019399  Ventral\n",
       "146901   755  AUDv2/3  019399  Ventral\n",
       "149648   959    AUDv1  019399  Ventral"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABAmeta.loc[currline.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>111190</th>\n",
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       "      <td>20</td>\n",
       "      <td>49</td>\n",
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       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138660</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141407</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144154</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146901</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149648</th>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        xcord  ycord  zcord\n",
       "9551       38     20      4\n",
       "12298      38     20      5\n",
       "15045      38     20      6\n",
       "17792      38     20      7\n",
       "20539      38     20      8\n",
       "23286      38     20      9\n",
       "26033      38     20     10\n",
       "28780      38     20     11\n",
       "31527      38     20     12\n",
       "34274      38     20     13\n",
       "37021      38     20     14\n",
       "39768      38     20     15\n",
       "42515      38     20     16\n",
       "45262      38     20     17\n",
       "48009      38     20     18\n",
       "50756      38     20     19\n",
       "53503      38     20     20\n",
       "56250      38     20     21\n",
       "58997      38     20     22\n",
       "61744      38     20     23\n",
       "64491      38     20     24\n",
       "67238      38     20     25\n",
       "69985      38     20     26\n",
       "72732      38     20     27\n",
       "75479      38     20     28\n",
       "78226      38     20     29\n",
       "80973      38     20     30\n",
       "83720      38     20     31\n",
       "86467      38     20     32\n",
       "89214      38     20     33\n",
       "91961      38     20     34\n",
       "94708      38     20     35\n",
       "97455      38     20     36\n",
       "100202     38     20     37\n",
       "102949     38     20     38\n",
       "105696     38     20     39\n",
       "108443     38     20     40\n",
       "111190     38     20     41\n",
       "113937     38     20     42\n",
       "116684     38     20     43\n",
       "119431     38     20     44\n",
       "122178     38     20     45\n",
       "124925     38     20     46\n",
       "127672     38     20     47\n",
       "130419     38     20     48\n",
       "133166     38     20     49\n",
       "135913     38     20     50\n",
       "138660     38     20     51\n",
       "141407     38     20     52\n",
       "144154     38     20     53\n",
       "146901     38     20     54\n",
       "149648     38     20     55"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABAcoords.loc[currline.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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