Fig2D_GSE165231_Kay2021_Mesoderm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Genes of interest Mesoderm Kay2021Theconservedlongnon\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GEO: GSE165231\n",
    "\n",
    "doi:10.1093/cvr/cvab281\n",
    "\n",
    "The conserved long non-coding RNA CARMA regulates cardiomyocyte differentiation.\n",
    "\n",
    "Maryam Kay, Bahram M. Soltani, Mohamed Nemir, Parisa Aghagolzadeh,\n",
    "Iole Pezzuto, Panagiotis Chouvardas, Francesco Ruberto, Fatemeh Movahedi,\n",
    "Hassan Ansari, Hossein Baharvand, and Thierry Pedrazzini\n",
    "\n",
    "Cardiovascular Research (2021) 00, 1–15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import scipy.cluster.hierarchy as sch\n",
    "import seaborn as sns\n",
    "\n",
    "import pylab\n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Day0-R1</th>\n",
       "      <th>Day0-R2</th>\n",
       "      <th>Day0-R3</th>\n",
       "      <th>Day3-R1</th>\n",
       "      <th>Day3-R2</th>\n",
       "      <th>Day3-R3</th>\n",
       "      <th>Day6-R1</th>\n",
       "      <th>Day6-R2</th>\n",
       "      <th>Day6-R3</th>\n",
       "      <th>Day10-R1</th>\n",
       "      <th>Day10-R2</th>\n",
       "      <th>Day10-R3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zfp982</th>\n",
       "      <td>84.52577</td>\n",
       "      <td>74.561975</td>\n",
       "      <td>56.587216</td>\n",
       "      <td>5.819602</td>\n",
       "      <td>4.727085</td>\n",
       "      <td>5.737349</td>\n",
       "      <td>0.429495</td>\n",
       "      <td>0.848170</td>\n",
       "      <td>0.772362</td>\n",
       "      <td>0.752484</td>\n",
       "      <td>0.221293</td>\n",
       "      <td>0.322671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanog</th>\n",
       "      <td>266.29256</td>\n",
       "      <td>268.514400</td>\n",
       "      <td>300.566257</td>\n",
       "      <td>175.926898</td>\n",
       "      <td>161.692213</td>\n",
       "      <td>146.963590</td>\n",
       "      <td>9.183218</td>\n",
       "      <td>9.141495</td>\n",
       "      <td>7.232134</td>\n",
       "      <td>8.279653</td>\n",
       "      <td>7.956503</td>\n",
       "      <td>5.022370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day0-R1     Day0-R2     Day0-R3     Day3-R1     Day3-R2     Day3-R3  \\\n",
       "Gene                                                                            \n",
       "Zfp982   84.52577   74.561975   56.587216    5.819602    4.727085    5.737349   \n",
       "Nanog   266.29256  268.514400  300.566257  175.926898  161.692213  146.963590   \n",
       "\n",
       "         Day6-R1   Day6-R2   Day6-R3  Day10-R1  Day10-R2  Day10-R3  \n",
       "Gene                                                                \n",
       "Zfp982  0.429495  0.848170  0.772362  0.752484  0.221293  0.322671  \n",
       "Nanog   9.183218  9.141495  7.232134  8.279653  7.956503  5.022370  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Kay = pd.read_table('~/Downloads/PythonNotebook/Mesoderm-Data_Kay2021_GSE165231/heat map final-Fig2C.txt').set_index('Gene')\n",
    "df_Kay[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Heatmap = df_Kay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plotme = df_Heatmap#.sort_values(by=['gene_name'], ascending=[True], inplace=False)\n",
    "\n",
    "column_labels = df_plotme.index #CGNP.axes[0]\n",
    "row_labels = df_plotme.columns[:] #CGNP.axes[1]\n",
    "data = df_plotme.values[:,:]\n",
    "\n",
    "# figure size\n",
    "#plt.figure(figsize=(20, 10))\n",
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "\n",
    "# color scheme\n",
    "heatmap = ax.pcolormesh(data, cmap='Reds', alpha=1) # plt.cm.bwr\n",
    "    \n",
    "# add colorbar\n",
    "fig.colorbar(heatmap, ax=ax, label='log10 FPKM')#, ax=ax)\n",
    "\n",
    "# put the major ticks at the middle of each cell, notice \"reverse\" use of dimension\n",
    "ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)\n",
    "ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)\n",
    "ax.set_xticklabels(row_labels, minor=False)\n",
    "ax.set_yticklabels(column_labels, minor=False)\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.tick_top()\n",
    "#plt.xticks(rotation=90)\n",
    "\n",
    "#no frame\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# Turn off all the ticks\n",
    "ax = plt.gca()\n",
    "for t in ax.xaxis.get_major_ticks():\n",
    "    t.tick1line.set_visible(False)\n",
    "    t.tick2line.set_visible(False)\n",
    "for t in ax.yaxis.get_major_ticks():\n",
    "    t.tick1line.set_visible(False)\n",
    "    t.tick2line.set_visible(False)\n",
    "    \n",
    "plt.show()\n",
    "#fig.savefig(\"RIC-Heatmap.jpg\", dpi=300)\n",
    "#fig.savefig(\"Prot15-Heatmap.svg\", dpi=300)\n",
    "#fig.savefig(\"RIC-Heatmap.pdf\", dpi=300)"
   ]
  },
  {
   "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>Day0-R1</th>\n",
       "      <th>Day0-R2</th>\n",
       "      <th>Day0-R3</th>\n",
       "      <th>Day3-R1</th>\n",
       "      <th>Day3-R2</th>\n",
       "      <th>Day3-R3</th>\n",
       "      <th>Day6-R1</th>\n",
       "      <th>Day6-R2</th>\n",
       "      <th>Day6-R3</th>\n",
       "      <th>Day10-R1</th>\n",
       "      <th>Day10-R2</th>\n",
       "      <th>Day10-R3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zfp982</th>\n",
       "      <td>1.926989</td>\n",
       "      <td>1.872517</td>\n",
       "      <td>1.752718</td>\n",
       "      <td>0.764893</td>\n",
       "      <td>0.674594</td>\n",
       "      <td>0.758711</td>\n",
       "      <td>-0.367041</td>\n",
       "      <td>-0.071516</td>\n",
       "      <td>-0.112179</td>\n",
       "      <td>-0.123502</td>\n",
       "      <td>-0.655031</td>\n",
       "      <td>-0.491239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanog</th>\n",
       "      <td>2.425359</td>\n",
       "      <td>2.428968</td>\n",
       "      <td>2.477940</td>\n",
       "      <td>2.245332</td>\n",
       "      <td>2.208689</td>\n",
       "      <td>2.167210</td>\n",
       "      <td>0.962995</td>\n",
       "      <td>0.961017</td>\n",
       "      <td>0.859267</td>\n",
       "      <td>0.918012</td>\n",
       "      <td>0.900722</td>\n",
       "      <td>0.700909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day0-R1   Day0-R2   Day0-R3   Day3-R1   Day3-R2   Day3-R3   Day6-R1  \\\n",
       "Gene                                                                           \n",
       "Zfp982  1.926989  1.872517  1.752718  0.764893  0.674594  0.758711 -0.367041   \n",
       "Nanog   2.425359  2.428968  2.477940  2.245332  2.208689  2.167210  0.962995   \n",
       "\n",
       "         Day6-R2   Day6-R3  Day10-R1  Day10-R2  Day10-R3  \n",
       "Gene                                                      \n",
       "Zfp982 -0.071516 -0.112179 -0.123502 -0.655031 -0.491239  \n",
       "Nanog   0.961017  0.859267  0.918012  0.900722  0.700909  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log10_df_Heatmap = np.log10(df_Kay + 0.000001)\n",
    "log10_df_Heatmap[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plotme = log10_df_Heatmap#\n",
    "\n",
    "column_labels = df_plotme.index #CGNP.axes[0]\n",
    "row_labels = df_plotme.columns[:] #CGNP.axes[1]\n",
    "data = df_plotme.values[:,:]\n",
    "\n",
    "# figure size\n",
    "#plt.figure(figsize=(20, 10))\n",
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "\n",
    "# color scheme\n",
    "heatmap = ax.pcolormesh(data, cmap='Reds', alpha=1) # plt.cm.bwr\n",
    "    \n",
    "# add colorbar\n",
    "fig.colorbar(heatmap, ax=ax, label='log10 FPKM')#, ax=ax)\n",
    "\n",
    "# put the major ticks at the middle of each cell, notice \"reverse\" use of dimension\n",
    "ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)\n",
    "ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)\n",
    "ax.set_xticklabels(row_labels, minor=False)\n",
    "ax.set_yticklabels(column_labels, minor=False)\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.tick_top()\n",
    "#plt.xticks(rotation=90)\n",
    "\n",
    "#no frame\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# Turn off all the ticks\n",
    "ax = plt.gca()\n",
    "for t in ax.xaxis.get_major_ticks():\n",
    "    t.tick1line.set_visible(False)\n",
    "    t.tick2line.set_visible(False)\n",
    "for t in ax.yaxis.get_major_ticks():\n",
    "    t.tick1line.set_visible(False)\n",
    "    t.tick2line.set_visible(False)\n",
    "    \n",
    "plt.show()\n",
    "#fig.savefig(\"RIC-Heatmap.jpg\", dpi=300)\n",
    "#fig.savefig(\"Prot15-Heatmap.svg\", dpi=300)\n",
    "#fig.savefig(\"RIC-Heatmap.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_Heatmap, \n",
    "               metric=\"correlation\",\n",
    "               col_cluster=False,\n",
    "               #standard_scale=1,\n",
    "               cmap='Reds',\n",
    "               figsize=(5, 3.5)\n",
    "               #yticklabels=False\n",
    "              )\n",
    "plt.savefig(\"FPKM-Heatmap-Kay2021-All-cluster.svg\", dpi=300)\n",
    "plt.savefig(\"FPKM-Heatmap-Kay2021-All-cluster.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_Heatmap, \n",
    "               metric=\"correlation\",\n",
    "               col_cluster=False,\n",
    "               standard_scale=1,\n",
    "               cmap='Reds',\n",
    "               figsize=(5, 3.5)\n",
    "               #yticklabels=False\n",
    "              )\n",
    "plt.savefig(\"FPKM-Heatmap-Kay2021-All-cluster-to1.svg\", dpi=300)\n",
    "plt.savefig(\"FPKM-Heatmap-Kay2021-All-cluster-to1.png\", dpi=300)#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f72351244e0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(log10_df_Heatmap, \n",
    "              # metric=\"correlation\",\n",
    "               col_cluster=False,\n",
    "               #standard_scale=1,\n",
    "               cmap='Reds',\n",
    "               figsize=(5, 3.5)\n",
    "               #yticklabels=False\n",
    "              )\n",
    "#plt.savefig(\"log10-FPKM-Heatmap-Kay2021-All-cluster.svg\", dpi=300)\n",
    "#plt.savefig(\"log10-FPKM-Heatmap-Kay2021-All-cluster.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}