BWold_ENCODE_Analyzer.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "path = r'C:\\Users\\dbaka\\OneDrive\\Desktop\\Eiden lab\\RNAseq datasets\\Barbara Wold ENCODE\\tsv files TPM FKPM' # use your path\n",
    "all_files = glob.glob(path + \"/*.tsv\")\n",
    "\n",
    "li = []\n",
    "\n",
    "for filename in all_files:\n",
    "    if \"gene\" in filename:\n",
    "        df = pd.read_csv(filename, sep='\\t')\n",
    "        df = df[df[\"gene_id\"]==\"ENSMUSG00000062232.10\"]\n",
    "        if \"10.5\" in filename:\n",
    "            df.insert(0,'Age',\"10.5\")\n",
    "        if \"11.5\" in filename:\n",
    "            df.insert(0,'Age',\"11.5\")\n",
    "        if \"12.5\" in filename:\n",
    "            df.insert(0,'Age',\"12.5\")\n",
    "        if \"13.5\" in filename:\n",
    "            df.insert(0,'Age',\"13.5\")\n",
    "        if \"14.5\" in filename:\n",
    "            df.insert(0,'Age',\"14.5\")\n",
    "        if \"15.5\" in filename:\n",
    "            df.insert(0,'Age',\"15.5\")\n",
    "        if \"16.5\" in filename:\n",
    "            df.insert(0,'Age',\"16.5\")\n",
    "        if \"p0\" in filename:\n",
    "            df.insert(0,'Age',\"p0\")\n",
    "            \n",
    "            \n",
    "        if \"heart\" in filename:\n",
    "            df.insert(0,'Tissue',\"heart\")\n",
    "        if \"liver\" in filename:\n",
    "            df.insert(0,'Tissue',\"liver\")\n",
    "        if \"forebrain\" in filename:\n",
    "            df.insert(0,'Tissue',\"forebrain\")\n",
    "        if \"midbrain\" in filename:\n",
    "            df.insert(0,'Tissue',\"midbrain\")         \n",
    "        if \"hindbrain\" in filename:\n",
    "            df.insert(0,'Tissue',\"hindbrain\")\n",
    "        if \"tube\" in filename:\n",
    "            df.insert(0,'Tissue',\"neural tube\")\n",
    "        li.append(df)\n",
    "\n",
    "frame = pd.concat(li, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reshape for graphing\n",
    "df1 = pd.melt(frame, id_vars =['Age','Tissue'],\n",
    "                     value_vars=['TPM','FPKM' ]\n",
    "             )\n",
    "df1.to_excel(r'C:\\Users\\dbaka\\OneDrive\\Desktop\\Eiden lab\\RNAseq datasets\\Barbara Wold ENCODE\\ENCODE_Rapgef2_geneLevel.xlsx', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1 = pd.read_excel(r'C:\\Users\\dbaka\\OneDrive\\Desktop\\Eiden lab\\Python Scripts\\Rapgef2 Datasets\\ENCODE_Rapgef2_geneLevel.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Tissue</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>midbrain</td>\n",
       "      <td>FPKM</td>\n",
       "      <td>2.13</td>\n",
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       "      <th>124</th>\n",
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       "      <td>forebrain</td>\n",
       "      <td>FPKM</td>\n",
       "      <td>2.80</td>\n",
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       "      <td>hindbrain</td>\n",
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       "      <td>10.5</td>\n",
       "      <td>forebrain</td>\n",
       "      <td>FPKM</td>\n",
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       "      <td>midbrain</td>\n",
       "      <td>TPM</td>\n",
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       "      <th>...</th>\n",
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       "      <th>100</th>\n",
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       "      <td>liver</td>\n",
       "      <td>FPKM</td>\n",
       "      <td>1.32</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>p0</td>\n",
       "      <td>heart</td>\n",
       "      <td>FPKM</td>\n",
       "      <td>2.14</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>p0</td>\n",
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       "      <td>forebrain</td>\n",
       "      <td>TPM</td>\n",
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       "<p>180 rows × 4 columns</p>\n",
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       "      Age     Tissue variable  value\n",
       "126  10.5   midbrain     FPKM   2.13\n",
       "124  10.5  forebrain     FPKM   2.80\n",
       "94   10.5  hindbrain     FPKM   2.77\n",
       "113  10.5  forebrain     FPKM   2.42\n",
       "88   10.5   midbrain      TPM   4.19\n",
       "..    ...        ...      ...    ...\n",
       "16     p0   midbrain      TPM   8.31\n",
       "100    p0      liver     FPKM   1.32\n",
       "101    p0      heart     FPKM   2.14\n",
       "69     p0  hindbrain      TPM   6.88\n",
       "79     p0  forebrain      TPM   4.49\n",
       "\n",
       "[180 rows x 4 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1.sort_values(by=['Age'], inplace=True)\n",
    "df1"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "FPKM = df1[df1[\"variable\"]=='FKPM']\n",
    "TPM = df1[df1[\"variable\"]=='TPM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('710790c1-5019-4472-9dd2-172ce58bb3df');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "fig = px.bar(TPM, x=\"Tissue\", y=\"value\", color = \"Age\", \n",
    "                    facet_col = \"Age\",\n",
    "             #facet_wrap = 4,\n",
    "                    width=1000, height=600,\n",
    "                    #hover_name = \"cluster_label\",\n",
    "                    #hover_data = [\"higher_cluster\", \"Cell_Type\", \"Expression\"]\n",
    "                    )\n",
    "for a in fig.layout.annotations:\n",
    "    a.text = a.text.split(\"=\")[1]\n",
    "fig.update_yaxes(title_text='')\n",
    "fig.update_layout(\n",
    "    title=\"Rapgef2: Poly-A Oligo-dt primed, B Wold, ENCODE\",\n",
    "    xaxis_title=\"Cell Type\",\n",
    "    yaxis_title=\"Expression\",\n",
    "xaxis={'categoryorder':'total descending'})\n",
    "\n",
    "#fig.update_layout(barmode='stack', xaxis={'categoryorder':'category ascending'})\n",
    "\n",
    "\n",
    "fig.show()\n",
    "\n",
    "            ## print table\n",
    "fig2 = go.Figure(data=[go.Table(\n",
    "    header=dict(values=list(TPM.columns),\n",
    "                fill_color='paleturquoise',\n",
    "                align='left'),\n",
    "    cells=dict(values=[TPM.Age,TPM.Tissue, TPM.variable, TPM.value],\n",
    "               fill_color='lavender',\n",
    "               align='left'))\n",
    "])\n",
    "\n",
    "\n",
    "fig2.update_layout(title=\"Rapgef2: Poly-A Oligo-dt primed, B Wold, ENCODE\")\n",
    "fig2.show()\n"
   ]
  },
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   "source": []
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