examples/chrov_viz_chrom_custom.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "25c902df-0cd6-4e32-8eda-959bb408713e",
   "metadata": {},
   "source": [
    "---\n",
    "# 🔥🗺️Visualization of heatmaps along chromosome arm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88365141-ecb2-487d-873b-64e0295e8711",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8aa0f354-4795-4d3e-9a57-776d1b81f8bb",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "## input parameters\n",
    "chromosome='1'\n",
    "arm='p'\n",
    "test=False\n",
    "# test=True\n",
    "input_dir_path='inputs'\n",
    "output_dir_path='outputs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd81768b-8a1c-41e3-8028-f182a0b62fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## set the current directory to 'examples'\n",
    "import os\n",
    "if len(set(['chrov','examples','tests']) - set(os.listdir('.')))==0:\n",
    "    ## change the current directory from 'chrov' to 'examples'\n",
    "    os.chdir('./examples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e230ae6-5993-4bf2-a625-b3f75a0f101b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if test:\n",
    "    import logging\n",
    "    logging.basicConfig(level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffc224d-9723-4a93-896a-b10e2f8640ea",
   "metadata": {},
   "source": [
    "**Demo data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23995df1-3814-41f1-b079-bdf56cc4d13b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chromosome</th>\n",
       "      <th>p start</th>\n",
       "      <th>q end</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>cytoband</th>\n",
       "      <th>cytoband type</th>\n",
       "      <th>arm</th>\n",
       "      <th>chromosome arm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>121700001</td>\n",
       "      <td>125100000</td>\n",
       "      <td>1</td>\n",
       "      <td>2300000</td>\n",
       "      <td>p36.33</td>\n",
       "      <td>gneg</td>\n",
       "      <td>p</td>\n",
       "      <td>chr1p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chromosome    p start      q end  start      end cytoband cytoband type arm  \\\n",
       "0          1  121700001  125100000      1  2300000   p36.33          gneg   p   \n",
       "\n",
       "  chromosome arm  \n",
       "0          chr1p  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cytobands to plot the chromosome\n",
    "cytobands=pd.read_table(f'{input_dir_path}/cytobands.tsv',index_col=[0]).query(expr=f\"`chromosome` == '{chromosome}' & `arm` == '{arm}'\")\n",
    "cytobands.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b59b4ac-bb54-4a20-b8cd-5b32e1c6cee3",
   "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>gene symbol</th>\n",
       "      <th>gene start</th>\n",
       "      <th>gene id</th>\n",
       "      <th>gene end</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature1</th>\n",
       "      <th>expression</th>\n",
       "      <th>differential expression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DCAF16</td>\n",
       "      <td>17800655</td>\n",
       "      <td>ENSG00000163257</td>\n",
       "      <td>17810758</td>\n",
       "      <td>0.997842</td>\n",
       "      <td>0.356694</td>\n",
       "      <td>0.419945</td>\n",
       "      <td>0.944215</td>\n",
       "      <td>10.826516</td>\n",
       "      <td>-3.46125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene symbol  gene start          gene id  gene end  feature4  feature3  \\\n",
       "0      DCAF16    17800655  ENSG00000163257  17810758  0.997842  0.356694   \n",
       "\n",
       "   feature2  feature1  expression  differential expression  \n",
       "0  0.419945  0.944215   10.826516                 -3.46125  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame({'gene symbol': ['DCAF16', 'TEC', 'MXD4', 'TLR10', 'POLN', 'ZNF721', 'CTBP1', 'OTOP1', 'ZBTB49', 'SMIM14', 'IDUA', 'SLBP', 'TMEM156', 'DRD5', 'KCTD8', 'MYL5', 'RNF212', 'GABRG1', 'SHISA3', 'SLAIN2'], 'gene start': [17800655, 48135783, 2247432, 38772238, 2071918, 425815, 1211445, 4188726, 4290251, 39546336, 986997, 1692731, 38966744, 9781634, 44173903, 673580, 1056250, 46035769, 42397488, 48341529], 'gene id': ['ENSG00000163257', 'ENSG00000135605', 'ENSG00000123933', 'ENSG00000174123', 'ENSG00000130997', 'ENSG00000182903', 'ENSG00000159692', 'ENSG00000163982', 'ENSG00000168826', 'ENSG00000163683', 'ENSG00000127415', 'ENSG00000163950', 'ENSG00000121895', 'ENSG00000169676', 'ENSG00000183783', 'ENSG00000215375', 'ENSG00000178222', 'ENSG00000163285', 'ENSG00000178343', 'ENSG00000109171'], 'gene end': [17810758, 48269838, 2262109, 38782990, 2242121, 499156, 1250333, 4226929, 4321786, 39638902, 1004564, 1712344, 39032922, 9784009, 44448809, 682028, 1113564, 46124054, 42402487, 48426201], 'feature4': [0.9978415737204072, 0.4545855939302902, 0.8573880517430308, 0.5369694154703196, 0.4457115449195024, 0.9765010037492002, 0.7076748305942623, 0.7249182689441283, 0.8556377476044005, 0.03310947281449672, 0.7976369417420842, 0.4143486090921996, 0.3150593948939062, 0.8596323398233959, 0.46504636110657616, 0.6789853107280641, 0.2978882296072487, 0.6359111145039221, 0.7810018812278806, 0.25627277183171904], 'feature3': [0.3566935974431962, 0.04823230413616064, 0.026306275635885723, 0.8506035236781101, 0.12397957830307027, 0.09622607852088605, 0.7510194295771837, 0.28532642279486886, 0.9077930496933677, 0.30597152430412267, 0.3125346095450551, 0.1318426530402026, 0.32218397914740515, 0.048972675187913595, 0.71018128846817, 0.3520131563850356, 0.7604625247363084, 0.6746375970484595, 0.2962249547560276, 0.5557764813821117], 'feature2': [0.41994505200469723, 0.49899410085102647, 0.04966002877825171, 0.2882714372027311, 0.12530940800497614, 0.9778505913257984, 0.5091710702451585, 0.11184212564565776, 0.7332539588514982, 0.2619236934962125, 0.2447293909158732, 0.11340833322232813, 0.09392775765398509, 0.8138133036751269, 0.8988200952204632, 0.3292719158968931, 0.4054109255542675, 0.4887042617664549, 0.6935745073377755, 0.3945997530158041], 'feature1': [0.9442146414428945, 0.029169687972744884, 0.658979746729614, 0.5136194713696834, 0.8243893393455316, 0.010312310246625378, 0.17856679646501383, 0.5393365344217116, 0.373158202003001, 0.7789524371639759, 0.9355711390777366, 0.30994562834493466, 0.5947124442716721, 0.8072087291309447, 0.7809503381629245, 0.25137092436557207, 0.804851285119722, 0.23192222823325437, 0.3336109658037789, 0.38825792686583227], 'expression': [10.826515598126914, 0.1732662403804863, 4.865983483487643, 6.792936088719567, 1.834804743239546, 12.952176820967551, 3.829768844147359, 6.457107943577316, 11.826325191122624, 11.784126621228086, 3.741973500259156, 11.221591806005003, 7.454615664513031, 4.620759600761383, 12.539226926086789, 5.440683683397464, 3.4207894585662526, 5.191982474146752, 8.708856972210224, 6.214336020716142], 'differential expression': [-3.4612499064035385, -3.208411823312507, -3.6856203104882046, -0.4948295622771379, -1.0167383420666098, 2.705781166527648, 1.3765674513286346, 1.8006900116463438, -1.1384579302765072, 2.5552288478319385, -2.4935532760477463, -1.879328453252897, 4.874562898886824, -2.5620866243460902, 4.715983364054573, 0.3926466168302109, 2.012265168759974, -3.710401016265068, -2.485234955358937, -3.647425207312329]})\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc097bb-2679-4963-bc01-58fab0129517",
   "metadata": {},
   "source": [
    "## Input table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19ec8b7e-7354-4e0e-b100-0b3273e31d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene symbol</th>\n",
       "      <th>gene start</th>\n",
       "      <th>gene id</th>\n",
       "      <th>gene end</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature1</th>\n",
       "      <th>expression</th>\n",
       "      <th>differential expression</th>\n",
       "      <th>genome coordinate mean</th>\n",
       "      <th>heatmap x</th>\n",
       "      <th>gene symbol label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZNF721</td>\n",
       "      <td>425815</td>\n",
       "      <td>ENSG00000182903</td>\n",
       "      <td>499156</td>\n",
       "      <td>0.976501</td>\n",
       "      <td>0.096226</td>\n",
       "      <td>0.977851</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>12.952177</td>\n",
       "      <td>2.705781</td>\n",
       "      <td>462485.5</td>\n",
       "      <td>1</td>\n",
       "      <td>$ZNF721$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene symbol  gene start          gene id  gene end  feature4  feature3  \\\n",
       "0      ZNF721      425815  ENSG00000182903    499156  0.976501  0.096226   \n",
       "\n",
       "   feature2  feature1  expression  differential expression  \\\n",
       "0  0.977851  0.010312   12.952177                 2.705781   \n",
       "\n",
       "   genome coordinate mean  heatmap x gene symbol label  \n",
       "0                462485.5          1          $ZNF721$  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=(\n",
    "    data\n",
    "    .sort_values('gene start').reset_index(drop=True)\n",
    "     .assign(\n",
    "     **{\n",
    "         'genome coordinate mean':lambda df: df.loc[:,['gene start','gene end']].mean(axis=1),\n",
    "         'heatmap x':lambda df: df.index+1,\n",
    "         'gene symbol label':lambda df: df['gene symbol'].apply(lambda x: f\"${x}$\"),\n",
    "     }    \n",
    "     )\n",
    ")\n",
    "df1.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed1ea53-c43f-4bde-974d-57aecc742c71",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2811ae5c-7fb7-4613-898c-ec77ad812b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from chrov.viz.plot import heatmaps_strips\n",
    "fig,axs=heatmaps_strips(\n",
    "    df1,\n",
    "    strips_kws={\n",
    "        'expression': { # column name in the table\n",
    "            'cmap':'Blues', # colormap\n",
    "        },\n",
    "        'differential expression': { # column name in the table\n",
    "            'cmap':'RdBu', # colormap\n",
    "        },\n",
    "        'feature1': { # column name in the table\n",
    "        },\n",
    "        'feature2': { # column name in the table\n",
    "        },\n",
    "        'feature3': { # column name in the table\n",
    "        },\n",
    "        \n",
    "    },\n",
    ")\n",
    "## show chromosome arm\n",
    "ax=fig.add_axes([0.2,1,0.5,0.1])\n",
    "from chrov.viz.chrom import plot_arm\n",
    "plot_arm(\n",
    "    cytobands,\n",
    "    y=0,\n",
    "    lw=20,\n",
    "    ax=ax,\n",
    "    ec='k',\n",
    "    test=False,\n",
    ").axis(False)\n",
    "ax.set_zorder(-1)\n",
    "## annotations\n",
    "from matplotlib.patches import ConnectionPatch\n",
    "_=df1.apply(lambda x:  fig.add_artist(\n",
    "    ConnectionPatch(\n",
    "        xyA=[x['heatmap x']-0.5,0], \n",
    "        xyB=[x['genome coordinate mean'],0], \n",
    "        coordsA=axs[0].transData,#\"data\", \n",
    "        coordsB=ax.transData,#\"data\",\n",
    "        # axesA=axs[0],\n",
    "        # axesB=ax,\n",
    "        color=\"k\",\n",
    "        alpha=0.5,\n",
    "        zorder=2,\n",
    "        clip_on=False\n",
    "    ),\n",
    "),\n",
    "axis=1)\n",
    "# plt.savefig(f\"{output_dir_path}/arm_linear_heatmaps.png\",bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chrov",
   "language": "python",
   "name": "chrov"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}