Fig1B_GSE109418_Guo2018Feedersfacilitatetelomere.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Genes of interest under different Culture conditions after Guo2018Feedersfacilitatetelomere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guo, R.; Ye, X.; Yang, J.; Zhou, Z.; Tian, C.; Wang, H.; Wang, H.; Fu, H.; Liu, C.; Zeng, M.; Yang, J. & Liu, L.\n",
    "Feeders facilitate telomere maintenance and chromosomal stability of embryonic stem cells.\n",
    "Nature Communications, Springer Science and Business Media LLC, 2018, 9\n",
    "\n",
    "DOI: https://doi.org/10.1038/s41467-018-05038-2\n",
    "\n",
    "GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109418\n",
    "\n",
    "Summary \t\n",
    "Pluripotent mouse embryonic stem cells (ESCs) were originally derived and stably maintained on feeder cells such as inactivated mouse embryo fibroblasts, and can generate complete ESC-pups by tetraploid embryo complementation (TEC), the most stringent functional test of naive pluripotency. Remarkably, 2i (inhibitors of Mek and Gsk3β signaling) medium with LIF in the absence of serum and feeders was developed to achieve ground state of mouse ESCs, and also has been successfully used for derivation of germline competent ESCs in other species such as rat. Notably, 2i-culture gives rise to transcriptional profiles and epigenetic landscapes quite distinct from serum-based ESCs. To better understand transcriptional landscape and signal transductions, we performed RNA-seq analysis of ESCs cultured in four different conditions: serum without feeder, serum with feeder, serum with feeder and 2i, and N2B27+2i.\n",
    "  \t\n",
    "Overall design \t\n",
    "We sought to investigate the differentiated expressed genes of N33 ESCs cultured in four conditions: serum without feeder (on gelatin, N33_G), serum with feeder (N33_F), serum with feeder and 2i (N33_F2i), and N2B27+2i (N33_NB2i). ESCs with biological duplicates were collected for RNA extraction and RNA-seq analysis simultaneously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_G_1 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942633_N33_G_rep1.gene.fpkm\")\n",
    "df_G_2 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942634_N33_G_rep2.gene.fpkm\")\n",
    "df_F_1 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942635_N33_F_rep1.gene.fpkm\")\n",
    "df_F_2 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942636_N33_F_rep2.gene.fpkm\")\n",
    "df_F2i_1 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942637_N33_F2i_rep1.gene.fpkm\")\n",
    "df_F2i_2 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942638_N33_F2i_rep2.gene.fpkm\")\n",
    "df_NB2i_1 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942639_N33_NB2i_rep1.gene.fpkm\")\n",
    "df_NB2i_2 = pd.read_excel('./Guo2018Feedersfacilitatetelomere/GSE109418_RAW.gene.fpkm.xls', sheet_name=\"GSM2942640_N33_NB2i_rep2.gene.fpkm\")"
   ]
  },
  {
   "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>gene_id</th>\n",
       "      <th>transcript_id(s)</th>\n",
       "      <th>length</th>\n",
       "      <th>expected_count</th>\n",
       "      <th>FPKM</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Cellular Component</th>\n",
       "      <th>Molecular Function</th>\n",
       "      <th>Biological Process</th>\n",
       "      <th>Kegg Orthology</th>\n",
       "      <th>Nr Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100009600</td>\n",
       "      <td>NM_001103168</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>Zglp1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K09183//GATA-binding protein 4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100017</td>\n",
       "      <td>NM_145554</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>11.71</td>\n",
       "      <td>Ldlrap1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K12474//low density lipoprotein receptor adapt...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100019</td>\n",
       "      <td>NM_001081392</td>\n",
       "      <td>17959.0</td>\n",
       "      <td>6043.0</td>\n",
       "      <td>16.20</td>\n",
       "      <td>Mdn1</td>\n",
       "      <td>GO:0031981//nuclear lumen</td>\n",
       "      <td>GO:0005515//protein binding;GO:0017111//nucleo...</td>\n",
       "      <td>GO:0009154//purine ribonucleotide catabolic pr...</td>\n",
       "      <td>K14572//midasin</td>\n",
       "      <td>gi|124487133|ref|NP_001074861.1|//midasin [Mus...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gene_id transcript_id(s)   length  expected_count   FPKM   Symbol  \\\n",
       "0  100009600     NM_001103168   1010.0            37.0   1.83    Zglp1   \n",
       "1     100017        NM_145554   2671.0           642.0  11.71  Ldlrap1   \n",
       "2     100019     NM_001081392  17959.0          6043.0  16.20     Mdn1   \n",
       "\n",
       "          Cellular Component  \\\n",
       "0                        NaN   \n",
       "1                        NaN   \n",
       "2  GO:0031981//nuclear lumen   \n",
       "\n",
       "                                  Molecular Function  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  GO:0005515//protein binding;GO:0017111//nucleo...   \n",
       "\n",
       "                                  Biological Process  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  GO:0009154//purine ribonucleotide catabolic pr...   \n",
       "\n",
       "                                      Kegg Orthology  \\\n",
       "0                     K09183//GATA-binding protein 4   \n",
       "1  K12474//low density lipoprotein receptor adapt...   \n",
       "2                                    K14572//midasin   \n",
       "\n",
       "                                      Nr Description  \n",
       "0                                                NaN  \n",
       "1                                                NaN  \n",
       "2  gi|124487133|ref|NP_001074861.1|//midasin [Mus...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_G_1[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc_df_G_1 = df_G_1.loc[:,['Symbol','FPKM']]\n",
    "loc_df_G_2 = df_G_2.loc[:,['Symbol','FPKM']]\n",
    "loc_df_F_1 = df_F_1.loc[:,['Symbol','FPKM']]\n",
    "loc_df_F_2 = df_F_2.loc[:,['Symbol','FPKM']]\n",
    "loc_df_F2i_1 = df_F2i_1.loc[:,['Symbol','FPKM']]\n",
    "loc_df_F2i_2 = df_F2i_2.loc[:,['Symbol','FPKM']]\n",
    "loc_df_NB2i_1 = df_NB2i_1.loc[:,['Symbol','FPKM']]\n",
    "loc_df_NB2i_2 = df_NB2i_2.loc[:,['Symbol','FPKM']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Symbol</th>\n",
       "      <th>FPKM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zglp1</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ldlrap1</td>\n",
       "      <td>11.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mdn1</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pydc3</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mfap1b</td>\n",
       "      <td>16.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17715</th>\n",
       "      <td>Arfip1</td>\n",
       "      <td>10.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17716</th>\n",
       "      <td>Prmt6</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17717</th>\n",
       "      <td>Ifi44</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17718</th>\n",
       "      <td>Tiparp</td>\n",
       "      <td>13.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17719</th>\n",
       "      <td>Kdm1a</td>\n",
       "      <td>102.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17720 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Symbol    FPKM\n",
       "0        Zglp1    1.83\n",
       "1      Ldlrap1   11.71\n",
       "2         Mdn1   16.20\n",
       "3        Pydc3    0.10\n",
       "4       Mfap1b   16.65\n",
       "...        ...     ...\n",
       "17715   Arfip1   10.94\n",
       "17716    Prmt6    8.61\n",
       "17717    Ifi44    0.01\n",
       "17718   Tiparp   13.71\n",
       "17719    Kdm1a  102.38\n",
       "\n",
       "[17720 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_df_G_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## set index to 'Symbol' and sort by Gennames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_df_G_1 = loc_df_G_1.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_G_2 = loc_df_G_2.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_F_1 = loc_df_F_1.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_F_2 = loc_df_F_2.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_F2i_1 = loc_df_F2i_1.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_F2i_2 = loc_df_F2i_2.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_NB2i_1 = loc_df_NB2i_1.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)\n",
    "sort_df_NB2i_2 = loc_df_NB2i_2.set_index('Symbol').sort_values(by=['Symbol'], ascending=[True], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17720 17723 18107 18075 18091 18111 17147 17114\n"
     ]
    }
   ],
   "source": [
    "print(len(sort_df_G_1),\n",
    "      len(sort_df_G_2),\n",
    "      len(sort_df_F_1),\n",
    "      len(sort_df_F_2), \n",
    "      len(sort_df_F2i_1),\n",
    "      len(sort_df_F2i_2),\n",
    "      len(sort_df_NB2i_1),\n",
    "      len(sort_df_NB2i_2)\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## concatenate FPKMs on Gennames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               FPKM  FPKM  FPKM  FPKM  FPKM  FPKM  FPKM  FPKM\n",
      "0610005C13Rik  0.44  0.49  0.76  0.83  1.39  1.47  1.23  1.01\n",
      "20278\n"
     ]
    }
   ],
   "source": [
    "Guo = [sort_df_G_1, \n",
    "       sort_df_G_2, \n",
    "       sort_df_F_1, \n",
    "       sort_df_F_2, \n",
    "       sort_df_F2i_1, \n",
    "       sort_df_F2i_2,\n",
    "       sort_df_NB2i_1,\n",
    "       sort_df_NB2i_2\n",
    "      ]\n",
    "\n",
    "df_Guo = pd.concat(Guo, axis=1)\n",
    "\n",
    "print(df_Guo[:1])\n",
    "print(len(df_Guo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rename all FPKM colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                G_1   G_2   F_1   F_2  F2i_1  F2i_2  NB2i_1  NB2i_2\n",
      "0610005C13Rik  0.44  0.49  0.76  0.83   1.39   1.47    1.23    1.01\n",
      "20278\n"
     ]
    }
   ],
   "source": [
    "colum_names =['G_1',\n",
    "              'G_2',\n",
    "              'F_1',\n",
    "              'F_2',\n",
    "              'F2i_1',\n",
    "              'F2i_2',\n",
    "              'NB2i_1',\n",
    "              'NB2i_2'\n",
    "             ]\n",
    "\n",
    "df_Guo = df_Guo.set_axis(colum_names, axis=1)\n",
    "\n",
    "print(df_Guo[:1])\n",
    "print(len(df_Guo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## remove duplicate Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                G_1   G_2   F_1   F_2  F2i_1  F2i_2  NB2i_1  NB2i_2\n",
      "0610005C13Rik  0.44  0.49  0.76  0.83   1.39   1.47    1.23    1.01\n",
      "19214\n"
     ]
    }
   ],
   "source": [
    "rmdup_Guo = df_Guo.drop_duplicates()\n",
    "\n",
    "print(rmdup_Guo[:1])\n",
    "print(len(rmdup_Guo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sort by G_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pb1031/anaconda3/envs/Python3andR/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eef1a1</th>\n",
       "      <td>6078.10</td>\n",
       "      <td>6061.72</td>\n",
       "      <td>6259.54</td>\n",
       "      <td>6244.26</td>\n",
       "      <td>6288.69</td>\n",
       "      <td>6297.58</td>\n",
       "      <td>7350.23</td>\n",
       "      <td>7175.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ftl1</th>\n",
       "      <td>3608.52</td>\n",
       "      <td>3361.81</td>\n",
       "      <td>3587.07</td>\n",
       "      <td>3339.46</td>\n",
       "      <td>3384.18</td>\n",
       "      <td>3548.96</td>\n",
       "      <td>3261.61</td>\n",
       "      <td>3303.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gapdh</th>\n",
       "      <td>3563.94</td>\n",
       "      <td>3353.79</td>\n",
       "      <td>3302.78</td>\n",
       "      <td>3304.96</td>\n",
       "      <td>3270.02</td>\n",
       "      <td>3466.12</td>\n",
       "      <td>3409.30</td>\n",
       "      <td>3162.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ptma</th>\n",
       "      <td>3223.09</td>\n",
       "      <td>3055.86</td>\n",
       "      <td>2969.44</td>\n",
       "      <td>3089.22</td>\n",
       "      <td>3361.51</td>\n",
       "      <td>3405.96</td>\n",
       "      <td>3022.20</td>\n",
       "      <td>3229.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rps2</th>\n",
       "      <td>3077.88</td>\n",
       "      <td>2961.71</td>\n",
       "      <td>3043.68</td>\n",
       "      <td>2779.07</td>\n",
       "      <td>2961.11</td>\n",
       "      <td>3065.49</td>\n",
       "      <td>3676.54</td>\n",
       "      <td>3357.88</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mir486</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mir6939</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mir7012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mir8111</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rnu3a</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19214 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             G_1      G_2      F_1      F_2    F2i_1    F2i_2   NB2i_1  \\\n",
       "Eef1a1   6078.10  6061.72  6259.54  6244.26  6288.69  6297.58  7350.23   \n",
       "Ftl1     3608.52  3361.81  3587.07  3339.46  3384.18  3548.96  3261.61   \n",
       "Gapdh    3563.94  3353.79  3302.78  3304.96  3270.02  3466.12  3409.30   \n",
       "Ptma     3223.09  3055.86  2969.44  3089.22  3361.51  3405.96  3022.20   \n",
       "Rps2     3077.88  2961.71  3043.68  2779.07  2961.11  3065.49  3676.54   \n",
       "...          ...      ...      ...      ...      ...      ...      ...   \n",
       "Mir486       NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "Mir6939      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "Mir7012      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "Mir8111      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "Rnu3a        NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "          NB2i_2  \n",
       "Eef1a1   7175.68  \n",
       "Ftl1     3303.32  \n",
       "Gapdh    3162.73  \n",
       "Ptma     3229.01  \n",
       "Rps2     3357.88  \n",
       "...          ...  \n",
       "Mir486      0.52  \n",
       "Mir6939     1.24  \n",
       "Mir7012     1.74  \n",
       "Mir8111     0.15  \n",
       "Rnu3a       0.71  \n",
       "\n",
       "[19214 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmdup_Guo.sort_values(by=['G_1'], ascending=[False], inplace=True)\n",
    "rmdup_Guo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## export to excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmdup_Guo.to_excel('./Guo2018Feedersfacilitatetelomere/rmdup_Guo-FPKM.xlsx',\n",
    "                   sheet_name='rmdup_Guo-FPKM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## top50 Genes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eef1a1</th>\n",
       "      <td>6078.10</td>\n",
       "      <td>6061.72</td>\n",
       "      <td>6259.54</td>\n",
       "      <td>6244.26</td>\n",
       "      <td>6288.69</td>\n",
       "      <td>6297.58</td>\n",
       "      <td>7350.23</td>\n",
       "      <td>7175.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ftl1</th>\n",
       "      <td>3608.52</td>\n",
       "      <td>3361.81</td>\n",
       "      <td>3587.07</td>\n",
       "      <td>3339.46</td>\n",
       "      <td>3384.18</td>\n",
       "      <td>3548.96</td>\n",
       "      <td>3261.61</td>\n",
       "      <td>3303.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gapdh</th>\n",
       "      <td>3563.94</td>\n",
       "      <td>3353.79</td>\n",
       "      <td>3302.78</td>\n",
       "      <td>3304.96</td>\n",
       "      <td>3270.02</td>\n",
       "      <td>3466.12</td>\n",
       "      <td>3409.30</td>\n",
       "      <td>3162.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            G_1      G_2      F_1      F_2    F2i_1    F2i_2   NB2i_1   NB2i_2\n",
       "Eef1a1  6078.10  6061.72  6259.54  6244.26  6288.69  6297.58  7350.23  7175.68\n",
       "Ftl1    3608.52  3361.81  3587.07  3339.46  3384.18  3548.96  3261.61  3303.32\n",
       "Gapdh   3563.94  3353.79  3302.78  3304.96  3270.02  3466.12  3409.30  3162.73"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top50_Heatmap = rmdup_Guo.head(50)\n",
    "top50_Heatmap[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eef1a1</th>\n",
       "      <td>3.783768</td>\n",
       "      <td>3.782596</td>\n",
       "      <td>3.796542</td>\n",
       "      <td>3.795481</td>\n",
       "      <td>3.798560</td>\n",
       "      <td>3.799174</td>\n",
       "      <td>3.866301</td>\n",
       "      <td>3.855863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ftl1</th>\n",
       "      <td>3.557329</td>\n",
       "      <td>3.526573</td>\n",
       "      <td>3.554740</td>\n",
       "      <td>3.523676</td>\n",
       "      <td>3.529453</td>\n",
       "      <td>3.550101</td>\n",
       "      <td>3.513432</td>\n",
       "      <td>3.518951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gapdh</th>\n",
       "      <td>3.551930</td>\n",
       "      <td>3.525536</td>\n",
       "      <td>3.518880</td>\n",
       "      <td>3.519166</td>\n",
       "      <td>3.514550</td>\n",
       "      <td>3.539844</td>\n",
       "      <td>3.532665</td>\n",
       "      <td>3.500062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             G_1       G_2       F_1       F_2     F2i_1     F2i_2    NB2i_1  \\\n",
       "Eef1a1  3.783768  3.782596  3.796542  3.795481  3.798560  3.799174  3.866301   \n",
       "Ftl1    3.557329  3.526573  3.554740  3.523676  3.529453  3.550101  3.513432   \n",
       "Gapdh   3.551930  3.525536  3.518880  3.519166  3.514550  3.539844  3.532665   \n",
       "\n",
       "          NB2i_2  \n",
       "Eef1a1  3.855863  \n",
       "Ftl1    3.518951  \n",
       "Gapdh   3.500062  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log10_top50_Heatmap = np.log10(top50_Heatmap)\n",
    "log10_top50_Heatmap[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_indexed = rmdup_Guo.set_index('Protein')\n",
    "\n",
    "column_labels = log10_top50_Heatmap.index #CGNP.axes[0]\n",
    "row_labels = log10_top50_Heatmap.columns[:] #CGNP.axes[1]\n",
    "data = log10_top50_Heatmap.values[:,:]\n",
    "\n",
    "# figure size\n",
    "#plt.figure(figsize=(20, 10))\n",
    "fig, ax = plt.subplots(figsize=(7, 20))\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": [
    "## filter for Genes of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": 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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pou5f1</th>\n",
       "      <td>606.01</td>\n",
       "      <td>569.14</td>\n",
       "      <td>583.09</td>\n",
       "      <td>595.15</td>\n",
       "      <td>602.03</td>\n",
       "      <td>620.25</td>\n",
       "      <td>765.13</td>\n",
       "      <td>735.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sox2</th>\n",
       "      <td>265.35</td>\n",
       "      <td>256.30</td>\n",
       "      <td>230.93</td>\n",
       "      <td>245.58</td>\n",
       "      <td>235.70</td>\n",
       "      <td>244.45</td>\n",
       "      <td>214.98</td>\n",
       "      <td>215.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanog</th>\n",
       "      <td>208.49</td>\n",
       "      <td>198.70</td>\n",
       "      <td>271.07</td>\n",
       "      <td>269.25</td>\n",
       "      <td>465.08</td>\n",
       "      <td>481.66</td>\n",
       "      <td>294.76</td>\n",
       "      <td>303.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa3</th>\n",
       "      <td>69.31</td>\n",
       "      <td>67.66</td>\n",
       "      <td>60.13</td>\n",
       "      <td>55.51</td>\n",
       "      <td>33.42</td>\n",
       "      <td>34.63</td>\n",
       "      <td>28.10</td>\n",
       "      <td>28.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yap1</th>\n",
       "      <td>55.24</td>\n",
       "      <td>52.74</td>\n",
       "      <td>54.85</td>\n",
       "      <td>56.02</td>\n",
       "      <td>54.35</td>\n",
       "      <td>53.66</td>\n",
       "      <td>54.19</td>\n",
       "      <td>52.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa2</th>\n",
       "      <td>45.55</td>\n",
       "      <td>45.73</td>\n",
       "      <td>51.59</td>\n",
       "      <td>54.72</td>\n",
       "      <td>46.70</td>\n",
       "      <td>45.03</td>\n",
       "      <td>7.74</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp42</th>\n",
       "      <td>39.92</td>\n",
       "      <td>39.54</td>\n",
       "      <td>39.46</td>\n",
       "      <td>39.94</td>\n",
       "      <td>60.00</td>\n",
       "      <td>58.80</td>\n",
       "      <td>69.87</td>\n",
       "      <td>65.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gm13152</th>\n",
       "      <td>24.50</td>\n",
       "      <td>21.88</td>\n",
       "      <td>22.24</td>\n",
       "      <td>24.07</td>\n",
       "      <td>42.23</td>\n",
       "      <td>37.72</td>\n",
       "      <td>48.93</td>\n",
       "      <td>53.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eomes</th>\n",
       "      <td>3.05</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.66</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cdx2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            G_1     G_2     F_1     F_2   F2i_1   F2i_2  NB2i_1  NB2i_2\n",
       "Pou5f1   606.01  569.14  583.09  595.15  602.03  620.25  765.13  735.75\n",
       "Sox2     265.35  256.30  230.93  245.58  235.70  244.45  214.98  215.39\n",
       "Nanog    208.49  198.70  271.07  269.25  465.08  481.66  294.76  303.72\n",
       "Dppa3     69.31   67.66   60.13   55.51   33.42   34.63   28.10   28.42\n",
       "Yap1      55.24   52.74   54.85   56.02   54.35   53.66   54.19   52.64\n",
       "Dppa2     45.55   45.73   51.59   54.72   46.70   45.03    7.74    8.97\n",
       "Zfp42     39.92   39.54   39.46   39.94   60.00   58.80   69.87   65.47\n",
       "Gm13152   24.50   21.88   22.24   24.07   42.23   37.72   48.93   53.17\n",
       "Eomes      3.05    2.98    3.03    2.87    2.66    2.66    5.67    5.68\n",
       "Cdx2       0.23    0.16    0.21    0.20    0.35    0.39    0.09    0.14"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['Dppa3',\n",
    "          'Oct4', 'Pou5f1',\n",
    "          'Zfp42',\n",
    "          'Dppa2',\n",
    "          'Nanog',\n",
    "          'Zfp982', 'Gm13152',\n",
    "          'Sox2',\n",
    "          'Yap1',\n",
    "          'Cdx2',\n",
    "          'Eomes'\n",
    "         ]\n",
    "\n",
    "df_Heatmap = rmdup_Guo.loc[rmdup_Guo.index.intersection(labels),:]\n",
    "df_Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rename Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oct4</th>\n",
       "      <td>606.01</td>\n",
       "      <td>569.14</td>\n",
       "      <td>583.09</td>\n",
       "      <td>595.15</td>\n",
       "      <td>602.03</td>\n",
       "      <td>620.25</td>\n",
       "      <td>765.13</td>\n",
       "      <td>735.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sox2</th>\n",
       "      <td>265.35</td>\n",
       "      <td>256.30</td>\n",
       "      <td>230.93</td>\n",
       "      <td>245.58</td>\n",
       "      <td>235.70</td>\n",
       "      <td>244.45</td>\n",
       "      <td>214.98</td>\n",
       "      <td>215.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanog</th>\n",
       "      <td>208.49</td>\n",
       "      <td>198.70</td>\n",
       "      <td>271.07</td>\n",
       "      <td>269.25</td>\n",
       "      <td>465.08</td>\n",
       "      <td>481.66</td>\n",
       "      <td>294.76</td>\n",
       "      <td>303.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa3</th>\n",
       "      <td>69.31</td>\n",
       "      <td>67.66</td>\n",
       "      <td>60.13</td>\n",
       "      <td>55.51</td>\n",
       "      <td>33.42</td>\n",
       "      <td>34.63</td>\n",
       "      <td>28.10</td>\n",
       "      <td>28.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yap1</th>\n",
       "      <td>55.24</td>\n",
       "      <td>52.74</td>\n",
       "      <td>54.85</td>\n",
       "      <td>56.02</td>\n",
       "      <td>54.35</td>\n",
       "      <td>53.66</td>\n",
       "      <td>54.19</td>\n",
       "      <td>52.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa2</th>\n",
       "      <td>45.55</td>\n",
       "      <td>45.73</td>\n",
       "      <td>51.59</td>\n",
       "      <td>54.72</td>\n",
       "      <td>46.70</td>\n",
       "      <td>45.03</td>\n",
       "      <td>7.74</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp42</th>\n",
       "      <td>39.92</td>\n",
       "      <td>39.54</td>\n",
       "      <td>39.46</td>\n",
       "      <td>39.94</td>\n",
       "      <td>60.00</td>\n",
       "      <td>58.80</td>\n",
       "      <td>69.87</td>\n",
       "      <td>65.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp982</th>\n",
       "      <td>24.50</td>\n",
       "      <td>21.88</td>\n",
       "      <td>22.24</td>\n",
       "      <td>24.07</td>\n",
       "      <td>42.23</td>\n",
       "      <td>37.72</td>\n",
       "      <td>48.93</td>\n",
       "      <td>53.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eomes</th>\n",
       "      <td>3.05</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.66</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cdx2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           G_1     G_2     F_1     F_2   F2i_1   F2i_2  NB2i_1  NB2i_2\n",
       "Oct4    606.01  569.14  583.09  595.15  602.03  620.25  765.13  735.75\n",
       "Sox2    265.35  256.30  230.93  245.58  235.70  244.45  214.98  215.39\n",
       "Nanog   208.49  198.70  271.07  269.25  465.08  481.66  294.76  303.72\n",
       "Dppa3    69.31   67.66   60.13   55.51   33.42   34.63   28.10   28.42\n",
       "Yap1     55.24   52.74   54.85   56.02   54.35   53.66   54.19   52.64\n",
       "Dppa2    45.55   45.73   51.59   54.72   46.70   45.03    7.74    8.97\n",
       "Zfp42    39.92   39.54   39.46   39.94   60.00   58.80   69.87   65.47\n",
       "Zfp982   24.50   21.88   22.24   24.07   42.23   37.72   48.93   53.17\n",
       "Eomes     3.05    2.98    3.03    2.87    2.66    2.66    5.67    5.68\n",
       "Cdx2      0.23    0.16    0.21    0.20    0.35    0.39    0.09    0.14"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Heatmap = df_Heatmap.rename({'Pou5f1':'Oct4', 'Gm13152':'Zfp982'})\n",
    "df_Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plott heatmap for the Genes of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_indexed = rmdup_Guo.set_index('Protein')\n",
    "\n",
    "column_labels = df_Heatmap.index #CGNP.axes[0]\n",
    "row_labels = df_Heatmap.columns[:] #CGNP.axes[1]\n",
    "data = df_Heatmap.values[:,:]\n",
    "\n",
    "# figure size\n",
    "#plt.figure(figsize=(20, 10))\n",
    "fig, ax = plt.subplots(figsize=(7, 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='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(\"FPKM-Heatmap-Guo2018Feedersfacilitatetelomere.svg\", dpi=300)\n",
    "#fig.savefig(\"RIC-Heatmap.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f01bc0b2d30>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_Heatmap, \n",
    "               #metric=\"correlation\",\n",
    "               #standard_scale=1,\n",
    "               cmap='Reds',\n",
    "               figsize=(5, 5)\n",
    "               #yticklabels=False\n",
    "              )\n",
    "#plt.savefig(\"FPKM-Heatmap-Ngondo2018Argonaute2Is-short.svg\", dpi=300)\n",
    "#plt.savefig(\"FPKM-Heatmap-Ngondo2018Argonaute2Is-short.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## log10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": 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>G_1</th>\n",
       "      <th>G_2</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_2</th>\n",
       "      <th>F2i_1</th>\n",
       "      <th>F2i_2</th>\n",
       "      <th>NB2i_1</th>\n",
       "      <th>NB2i_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oct4</th>\n",
       "      <td>2.782480</td>\n",
       "      <td>2.755219</td>\n",
       "      <td>2.765736</td>\n",
       "      <td>2.774626</td>\n",
       "      <td>2.779618</td>\n",
       "      <td>2.792567</td>\n",
       "      <td>2.883735</td>\n",
       "      <td>2.866730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sox2</th>\n",
       "      <td>2.423819</td>\n",
       "      <td>2.408749</td>\n",
       "      <td>2.363480</td>\n",
       "      <td>2.390193</td>\n",
       "      <td>2.372360</td>\n",
       "      <td>2.388190</td>\n",
       "      <td>2.332398</td>\n",
       "      <td>2.333226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nanog</th>\n",
       "      <td>2.319085</td>\n",
       "      <td>2.298198</td>\n",
       "      <td>2.433081</td>\n",
       "      <td>2.430156</td>\n",
       "      <td>2.667528</td>\n",
       "      <td>2.682741</td>\n",
       "      <td>2.469469</td>\n",
       "      <td>2.482473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa3</th>\n",
       "      <td>1.840796</td>\n",
       "      <td>1.830332</td>\n",
       "      <td>1.779091</td>\n",
       "      <td>1.744371</td>\n",
       "      <td>1.524006</td>\n",
       "      <td>1.539452</td>\n",
       "      <td>1.448706</td>\n",
       "      <td>1.453624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yap1</th>\n",
       "      <td>1.742254</td>\n",
       "      <td>1.722140</td>\n",
       "      <td>1.739177</td>\n",
       "      <td>1.748343</td>\n",
       "      <td>1.735200</td>\n",
       "      <td>1.729651</td>\n",
       "      <td>1.733919</td>\n",
       "      <td>1.721316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dppa2</th>\n",
       "      <td>1.658488</td>\n",
       "      <td>1.660201</td>\n",
       "      <td>1.712566</td>\n",
       "      <td>1.738146</td>\n",
       "      <td>1.669317</td>\n",
       "      <td>1.653502</td>\n",
       "      <td>0.888741</td>\n",
       "      <td>0.952792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp42</th>\n",
       "      <td>1.601191</td>\n",
       "      <td>1.597037</td>\n",
       "      <td>1.596157</td>\n",
       "      <td>1.601408</td>\n",
       "      <td>1.778151</td>\n",
       "      <td>1.769377</td>\n",
       "      <td>1.844291</td>\n",
       "      <td>1.816042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zfp982</th>\n",
       "      <td>1.389166</td>\n",
       "      <td>1.340047</td>\n",
       "      <td>1.347135</td>\n",
       "      <td>1.381476</td>\n",
       "      <td>1.625621</td>\n",
       "      <td>1.576572</td>\n",
       "      <td>1.689575</td>\n",
       "      <td>1.725667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eomes</th>\n",
       "      <td>0.484300</td>\n",
       "      <td>0.474216</td>\n",
       "      <td>0.481443</td>\n",
       "      <td>0.457882</td>\n",
       "      <td>0.424882</td>\n",
       "      <td>0.424882</td>\n",
       "      <td>0.753583</td>\n",
       "      <td>0.754348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cdx2</th>\n",
       "      <td>-0.638272</td>\n",
       "      <td>-0.795880</td>\n",
       "      <td>-0.677781</td>\n",
       "      <td>-0.698970</td>\n",
       "      <td>-0.455932</td>\n",
       "      <td>-0.408935</td>\n",
       "      <td>-1.045757</td>\n",
       "      <td>-0.853872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             G_1       G_2       F_1       F_2     F2i_1     F2i_2    NB2i_1  \\\n",
       "Oct4    2.782480  2.755219  2.765736  2.774626  2.779618  2.792567  2.883735   \n",
       "Sox2    2.423819  2.408749  2.363480  2.390193  2.372360  2.388190  2.332398   \n",
       "Nanog   2.319085  2.298198  2.433081  2.430156  2.667528  2.682741  2.469469   \n",
       "Dppa3   1.840796  1.830332  1.779091  1.744371  1.524006  1.539452  1.448706   \n",
       "Yap1    1.742254  1.722140  1.739177  1.748343  1.735200  1.729651  1.733919   \n",
       "Dppa2   1.658488  1.660201  1.712566  1.738146  1.669317  1.653502  0.888741   \n",
       "Zfp42   1.601191  1.597037  1.596157  1.601408  1.778151  1.769377  1.844291   \n",
       "Zfp982  1.389166  1.340047  1.347135  1.381476  1.625621  1.576572  1.689575   \n",
       "Eomes   0.484300  0.474216  0.481443  0.457882  0.424882  0.424882  0.753583   \n",
       "Cdx2   -0.638272 -0.795880 -0.677781 -0.698970 -0.455932 -0.408935 -1.045757   \n",
       "\n",
       "          NB2i_2  \n",
       "Oct4    2.866730  \n",
       "Sox2    2.333226  \n",
       "Nanog   2.482473  \n",
       "Dppa3   1.453624  \n",
       "Yap1    1.721316  \n",
       "Dppa2   0.952792  \n",
       "Zfp42   1.816042  \n",
       "Zfp982  1.725667  \n",
       "Eomes   0.754348  \n",
       "Cdx2   -0.853872  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log10Heatmap = np.log10(df_Heatmap)\n",
    "log10Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_indexed = rmdup_Guo.set_index('Protein')\n",
    "\n",
    "column_labels = log10Heatmap.index #CGNP.axes[0]\n",
    "row_labels = log10Heatmap.columns[:] #CGNP.axes[1]\n",
    "data = log10Heatmap.values[:,:]\n",
    "\n",
    "# figure size\n",
    "#plt.figure(figsize=(20, 10))\n",
    "fig, ax = plt.subplots(figsize=(7, 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(\"log10-FPKM-Heatmap-Guo2018Feedersfacilitatetelomere.svg\", dpi=300)\n",
    "#fig.savefig(\"RIC-Heatmap.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(log10Heatmap, \n",
    "              #metric=\"correlation\",\n",
    "               #standard_scale=1,\n",
    "               cmap='Reds',\n",
    "               figsize=(5, 5)\n",
    "               #yticklabels=False\n",
    "              )\n",
    "plt.savefig(\"log10-FPKM-Heatmap-Guo2018Feedersfacilitatetelomere-cluster.svg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### conditions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "serum without feeder (on gelatin, N33_G), serum with feeder (N33_F), serum with feeder and 2i (N33_F2i), and N2B27+2i (N33_NB2i)"
   ]
  },
  {
   "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
}