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dcolinmorgan/grn_bootstrap_demo.ipynb

Created January 20, 2025 02:51
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grn_bootstrap_demo.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/dcolinmorgan/32779fdc6b1062328f45341ac9a4fe1f/grn_bootstrap_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# ScenicPlus Bootstrap tutorial"
],
"metadata": {
"id": "9sUt3qc4bS4D"
}
},
{
"cell_type": "markdown",
"source": [
"## gather multiple runs"
],
"metadata": {
"id": "awpzKuCrbZBZ"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "gHUrAaGHYBJh"
},
"outputs": [],
"source": [
"# import pandas as pd\n",
"# import os\n",
"\n",
"# # Specify the base directory to search for files\n",
"# base_directory = '/Users/apple/Developer/dcolinmorgan/scenicplus/scplus_pipeline'\n",
"\n",
"# # List to hold DataFrames\n",
"# dataframes = []\n",
"\n",
"# # Loop through the directory and its subdirectories\n",
"# for root, dirs, files in os.walk(base_directory):\n",
"# for file in files:\n",
"# if file == 'eRegulon*.tsv': # s_extended.tsv': # Check for the specific file name\n",
"# file_path = os.path.join(root, file) # Get the full file path\n",
"# df = pd.read_csv(file_path, sep='\\t') # Load the DataFrame (assuming tab-separated values)\n",
"# df['source_file'] = file_path # Add a new column with the file path\n",
"# dataframes.append(df) # Append to the list\n",
"\n",
"# # Optionally, concatenate all DataFrames into a single DataFrame\n",
"# combined_df = pd.concat(dataframes, ignore_index=True)\n",
"\n",
"# # Print the combined DataFrame\n",
"# print(combined_df)\n"
]
},
{
"cell_type": "code",
"source": [
"!wget -O data.zip https://www.dropbox.com/scl/fi/yjuvl2acazttsube8pqqq/directANDextended_combined_df.txt.zip?rlkey=50jc27xds8tce6jjcfdw1cqff&st=hirswnuq&dl=0"
],
"metadata": {
"id": "uXYe6_B_nJII"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"data=pd.read_csv('data.zip',sep='\\t')\n",
"data.source_file=data.source_file.str.split('/').str[7]"
],
"metadata": {
"id": "--MuVeuj3RVP"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## naive edge count per ScenicPlus run"
],
"metadata": {
"id": "XIXhwNVEYgc_"
}
},
{
"cell_type": "code",
"source": [
"count=data.source_file.value_counts()\n",
"count"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 429
},
"id": "q-ITZGFu3ZD8",
"outputId": "85ab3d80-3f1e-4ded-ce59-ee2ddff4f703"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"source_file\n",
"run6 144113\n",
"run10 138891\n",
"run4 136875\n",
"run3 133909\n",
"run9 132933\n",
"run2 131809\n",
"run8 128147\n",
"run7 126583\n",
"run1 118074\n",
"run5 112721\n",
"Name: count, dtype: int64"
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"</div><br><label><b>dtype:</b> int64</label>"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"data[['Gene','TF','importance_TF2G','rho_TF2G','triplet_rank']]\n",
"# data[['target','TF','source_file']]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "Dhog4Lar3uPX",
"outputId": "ebeaf322-6f2a-4dca-92ed-a4a30fb204c0"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Gene TF importance_TF2G rho_TF2G triplet_rank\n",
"0 PCDHB2 ARID3A 0.072025 0.638654 40371\n",
"1 LRMDA ARID3A 0.228578 0.358036 19260\n",
"2 DCHS2 ARID3A 0.050875 0.352265 44200\n",
"3 ATCAY ARID3A 0.050157 0.458091 12008\n",
"4 LEKR1 ARID3A 0.052121 0.570661 44435\n",
"... ... ... ... ... ...\n",
"1304050 HCN1 RFX4 3.853835 -0.179892 162\n",
"1304051 MTUS2 RFX4 3.213123 -0.168589 9990\n",
"1304052 FGF14 RFX4 2.093614 -0.239278 4133\n",
"1304053 GPR158 RFX4 2.035685 -0.180641 11934\n",
"1304054 MYT1L RFX4 4.871685 -0.232857 18805\n",
"\n",
"[1304055 rows x 5 columns]"
],
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" <th></th>\n",
" <th>Gene</th>\n",
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" <th>importance_TF2G</th>\n",
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" <th>triplet_rank</th>\n",
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" <th>0</th>\n",
" <td>PCDHB2</td>\n",
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" <td>0.072025</td>\n",
" <td>0.638654</td>\n",
" <td>40371</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>LRMDA</td>\n",
" <td>ARID3A</td>\n",
" <td>0.228578</td>\n",
" <td>0.358036</td>\n",
" <td>19260</td>\n",
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" <th>2</th>\n",
" <td>DCHS2</td>\n",
" <td>ARID3A</td>\n",
" <td>0.050875</td>\n",
" <td>0.352265</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ATCAY</td>\n",
" <td>ARID3A</td>\n",
" <td>0.050157</td>\n",
" <td>0.458091</td>\n",
" <td>12008</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>LEKR1</td>\n",
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" <td>162</td>\n",
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" <td>MTUS2</td>\n",
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" <td>3.213123</td>\n",
" <td>-0.168589</td>\n",
" <td>9990</td>\n",
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" <td>FGF14</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1304054</th>\n",
" <td>MYT1L</td>\n",
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" <td>4.871685</td>\n",
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" <td>18805</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>1304055 rows × 5 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2b0b1ab-93de-430d-83b5-80aef6fdd8bb')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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"\n",
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" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" document.querySelector('#df-b2b0b1ab-93de-430d-83b5-80aef6fdd8bb button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-b2b0b1ab-93de-430d-83b5-80aef6fdd8bb');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-89a5c037-2c56-42ae-ac4a-c9714d72add6\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89a5c037-2c56-42ae-ac4a-c9714d72add6')\"\n",
" title=\"Suggest charts\"\n",
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"\n",
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" </g>\n",
"</svg>\n",
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"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
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"\n",
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" background-color: var(--bg-color);\n",
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"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
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" border-top-color: var(--fill-color);\n",
" }\n",
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" }\n",
" 80% {\n",
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" border-right-color: var(--fill-color);\n",
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" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-89a5c037-2c56-42ae-ac4a-c9714d72add6 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
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"</div>\n",
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" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe"
}
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import networkx as nx\n",
"graphs={}\n",
"for i in count.index:\n",
" # Step 1: Create a directed graph\n",
" G = nx.DiGraph() # Use DiGraph for directed edges, or Graph for undirected\n",
"\n",
" # Step 3: Add edges to the graph\n",
" for index, row in data[data['source_file']==i].iterrows():\n",
" gene = row['Gene']\n",
" tf = row['TF']\n",
" rho_TF2G = row['rho_TF2G']\n",
"\n",
" # Add an edge from TF to Gene with weight as rho_TF2G\n",
" G.add_edge(tf, gene, weight=rho_TF2G)\n",
" graphs[f'graph_{i}'] = G\n"
],
"metadata": {
"id": "bqYq5ve439kX"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "a2zaTV-S9HoP"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(12, 6))\n",
"\n",
"subax1 = plt.subplot2grid((2,5), (0,0))\n",
"subax1.set_xticks([])\n",
"degrees = [val for (node, val) in graphs['graph_run1'].degree()]\n",
"print(graphs['graph_run1'].number_of_edges()/graphs['graph_run1'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax2 = plt.subplot2grid((2,5), (0,1))\n",
"subax2.set_xticks([])\n",
"degrees = [val for (node, val) in graphs['graph_run2'].degree()]\n",
"print(graphs['graph_run2'].number_of_edges()/graphs['graph_run2'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax2.set_yticks([])\n",
"subax3 = plt.subplot2grid((2,5), (0,2))\n",
"subax3.set_xticks([])\n",
"degrees = [val for (node, val) in graphs['graph_run3'].degree()]\n",
"print(graphs['graph_run3'].number_of_edges()/graphs['graph_run3'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax3.set_yticks([])\n",
"subax4 = plt.subplot2grid((2,5), (0,3))\n",
"subax4.set_xticks([])\n",
"degrees = [val for (node, val) in graphs['graph_run4'].degree()]\n",
"print(graphs['graph_run4'].number_of_edges()/graphs['graph_run4'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax4.set_yticks([])\n",
"subax5 = plt.subplot2grid((2,5), (0,4))\n",
"subax5.set_xticks([])\n",
"degrees = [val for (node, val) in graphs['graph_run5'].degree()]\n",
"print(graphs['graph_run5'].number_of_edges()/graphs['graph_run5'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax5.set_yticks([])\n",
"subax6 = plt.subplot2grid((2,5), (1,0))\n",
"degrees = [val for (node, val) in graphs['graph_run6'].degree()]\n",
"print(graphs['graph_run6'].number_of_edges()/graphs['graph_run6'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax7 = plt.subplot2grid((2,5), (1,1))\n",
"degrees = [val for (node, val) in graphs['graph_run7'].degree()]\n",
"print(graphs['graph_run7'].number_of_edges()/graphs['graph_run7'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax7.set_yticks([])\n",
"subax8 = plt.subplot2grid((2,5), (1,2))\n",
"degrees = [val for (node, val) in graphs['graph_run8'].degree()]\n",
"print(graphs['graph_run8'].number_of_edges()/graphs['graph_run8'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax8.set_yticks([])\n",
"subax9 = plt.subplot2grid((2,5), (1,3))\n",
"degrees = [val for (node, val) in graphs['graph_run9'].degree()]\n",
"print(graphs['graph_run9'].number_of_edges()/graphs['graph_run9'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax9.set_yticks([])\n",
"subax10 = plt.subplot2grid((2,5), (1,4))\n",
"degrees = [val for (node, val) in graphs['graph_run10'].degree()]\n",
"print(graphs['graph_run10'].number_of_edges()/graphs['graph_run10'].number_of_nodes())\n",
"x=plt.hist(degrees,log=True,bins=20)\n",
"subax10.set_yticks([])"
],
"metadata": {
"id": "MxvAztcr80H-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 585
},
"outputId": "8e1d8808-0389-46c3-e3fd-11343bfa592c"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"11.403286249639665\n",
"13.022452504317789\n",
"13.132086330935252\n",
"12.562302158273381\n",
"10.961970613656007\n",
"13.511226252158895\n",
"12.378206976073796\n",
"12.574920908829451\n",
"13.013544668587896\n",
"12.927811331607709\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {},
"execution_count": 33
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x600 with 10 Axes>"
],
"image/png": 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hwoUYGBiI7du3T9+3YMGC6OzsjMOHD1/XNnfu3BkPPfRQXkusOS5sQ8qcKksK5BwA0pLrhc/OnDkTk5OT0dTUNOP+pqamGB4env51Z2dnfOADH4innnoqbr311isW8O3bt8fo6Oj0bWhoKM8lAwAAQG6q8hVe3/zmN6/5ufX19VFfXx+lUilKpVJMTk7O4crmhqMYzFaR8w95MAMzXe19xZlQ84v8X8yZgGkp6gz4f3W6cj2S3djYGAsXLoyRkZEZ94+MjERzc/Ostt3d3R0nTpyIo0ePzmo7UETyT+rMACmTf1JnBiiaXEv24sWLY82aNdHf3z9939TUVPT398eGDRvy3BUAAADUnLJPFz9//nw8//zz078+efJkDA4Oxhve8Ia47bbbore3N+6777648847Y926dfH444/H+Pj49NXGr1dRTxOZDaeY8JoU8w+/zQyQMvkndWaAoin7SPaxY8eio6MjOjo6IiKit7c3Ojo6oq+vLyIitm7dGo899lj09fVFe3t7DA4OxsGDBy+6GFq5nCZCyuSf1JkBUib/pM4MUDRlH8neuHFjZFl2xef09PRET0/PdS8KAAAAiqgqVxe/Hk4TIWVFzr+r65OHIs8AzFaq+ff+wWtSnIHZ5N9HSqsv1wufzSWniZAy+Sd1ZoCUyT+pMwMUTWFKNgAAANQ6JRsAAABy4jPZ89SVPsfhcxrFI/8Xm6vP6pmP2mQGyuM9YH6Zr/n3mWuulRmgaApzJNtnMUiZ/JM6M0DK5J/UmQGKpjAlGwAAAGqdkg0AAAA58ZlsKAD5J3VmgJTJf3lck2D+MQMUTWGOZPssBimTf1JnBkiZ/JM6M0DRFKZkAwAAQK1TsgEAACAnSjYAAADkxIXPCux6v8DeBUGKR/5JnRmoDd4/qkP+K0fGa5MZoGgKcyTbBQ9ImfyTOjNAyuSf1JkBiqYwJRsAAABqnZINAAAAOVGyAQAAICdKNgAAAOREyQYAAICc+AovyuKrLapD/ivner8aT/7nlhnIj/+PF4/8kzozQNEU5ki2S/eTMvkndWaAlMk/qTMDFE1hSjYAAADUOiUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5GRRtRdwrXwJfWWsfvDJai8hN1d7LT9/5L0VWsnsyX/xXe9sXS2nV9rulX7v9f6+ajEDxVa0vNUa+U+X2XmVGaBoCnMk25fQkzL5J3VmgJTJP6kzAxRNYUo2AAAA1DolGwAAAHKiZAMAAEBOlGwAAADIiZINAAAAOVGyAQAAICdKNgAAAOREyQYAAICcKNkAAACQk6qU7K9+9atx++23x5vf/Ob4/Oc/X40lAAAAQO4WVXqHr7zySvT29sa3v/3taGhoiDVr1sSWLVvi5ptvrvRSAAAAIFcVP5J95MiReOtb3xorV66MG2+8MTZv3hxf//rXK70MAAAAyF3ZJfvQoUNx9913R0tLS9TV1cWBAwcuek6pVIrVq1fHkiVLYv369XHkyJHpx1588cVYuXLl9K9XrlwZL7zwwvWtHgAAAGpI2SV7fHw82traolQqXfLx/fv3R29vb+zYsSOOHz8ebW1tsWnTpjh9+vSsFwsAAAC1rOySvXnz5nj44Ydjy5Ytl3x8165dsW3btujq6orW1tbYvXt3LF26NPbu3RsRES0tLTOOXL/wwgvR0tJy2f1NTEzE2NjYjBsAAADUolwvfHbhwoUYGBiI7du3T9+3YMGC6OzsjMOHD0dExLp16+LZZ5+NF154IRoaGuJrX/tafPrTn77sNnfu3BkPPfRQnsukBq1+8MnLPvbzR947b/bJ/HWlPEFRzCbH1ZiBudqn94D5q1oZv1Kmam125H9+u968XS0Xc7XduXC1teaxplwvfHbmzJmYnJyMpqamGfc3NTXF8PBwREQsWrQoPvOZz8Rdd90V7e3t8cADD1zxyuLbt2+P0dHR6dvQ0FCeSwYAAIDcVPwrvCIi7rnnnrjnnnuu6bn19fVRX18fpVIpSqVSTE5OzvHqoPbIP6kzA6RM/kmdGaBocj2S3djYGAsXLoyRkZEZ94+MjERzc/Ostt3d3R0nTpyIo0ePzmo7UETyT+rMACmTf1JnBiiaXEv24sWLY82aNdHf3z9939TUVPT398eGDRvy3BUAAADUnLJL9vnz52NwcDAGBwcjIuLkyZMxODgYp06dioiI3t7e2LNnT+zbty+ee+65uP/++2N8fDy6urpmtdBSqRStra2xdu3aWW0Hikj+SZ0ZIGXyT+rMAEVTdsk+duxYdHR0REdHR0S8Wqo7Ojqir68vIiK2bt0ajz32WPT19UV7e3sMDg7GwYMHL7oYWrmcJkLK5J/UmQFSJv+kzgxQNGVf+Gzjxo2RZdkVn9PT0xM9PT3XvSgAAAAooqpcXfx6vHZVwVdeeSUiIsbGxi75vKmJ/1fJZfFbLvczuRZX+rld73Znk4XL7fO1+6/2D015u9b8R5iB+eZq+b/e2bne31frMyD/xTNX7x2z4T2AvM3Fz6waf++q9RmQ/1dVI2/VyPH1utpa83gPqMsqPSWz9Mtf/jJWrVpV7WWQuKGhobj11lsrvl/5p1aYAVIm/6TODJCya8l/4Ur21NRUvPjii7Fs2bKoq6ur9nJITJZlce7cuWhpaYkFC3K9OP81kX+qzQyQMvkndWaAlJWT/8KVbAAAAKhVlf8nKAAAAJinlGwAAADIiZINAAAAOVGyAQAAICdKNgAAAOREyQYAAICcKNkAAACQEyUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwo2QAAAJATJRsAAAByomQDAABATpRsAAAAyImSDQAAADlRsgEAACAnSjYAAADkRMkGAACAnCjZAAAAkBMlGwAAAHKiZAMAAEBOlGwAAADIiZINAAAAOVGyAQAAICdKNgAAAOREyQYAAICcKNkAAACQEyUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwsqvYCyjU1NRUvvvhiLFu2LOrq6qq9HBKTZVmcO3cuWlpaYsGCyv8blfxTbWaAlMk/qTMDpKys/GcF8Y//+I/ZH/3RH2W///u/n0WEm1tVb0NDQ/LvlvTNDLilfJN/t9RvZsAt5du15L8uy7IsCmR0dDRWrFgRQ0NDsXz58movh8SMjY3FqlWr4uzZs9HQ0FDx/cs/1WYGSJn8kzozQMrKyX/hThd/7dSQ5cuXGy6qplqnKMk/tcIMkDL5J3VmgJRdS/5d+AwAAAByomQDAABATpRsAAAAyElhSnapVIrW1tZYu3ZttZcCFSf/pM4MkDL5J3VmgKIp3NXFx8bGoqGhIUZHR13wgIqrdv6qvX+odgarvX/SVu38VXv/UO0MVnv/pK2c/BXmSDYAAADUOiUbAAAAclK478m+mtUPPnnFx3/+yHsrtBKojivNgPwz38k/qTMDpEz+qRWOZAMAAEBOClOyXVWQlMk/qTMDpEz+SZ0ZoGgKU7K7u7vjxIkTcfTo0WovBSpO/kmdGSBl8k/qzABFU5iSDQAAALVOyQYAAICcKNkAAACQEyUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ4Up2aVSKVpbW2Pt2rXVXgpUnPyTOjNAyuSf1JkBiqYwJbu7uztOnDgRR48erfZSoOLkn9SZAVIm/6TODFA0hSnZAAAAUOuUbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwo2QAAAJATJRsAAAByomQDAABATipess+ePRt33nlntLe3xx133BF79uyp9BIAAABgTiyq9A6XLVsWhw4diqVLl8b4+Hjccccd8b73vS9uvvnmSi8FAAAAclXxI9kLFy6MpUuXRkTExMREZFkWWZZVehkAAACQu7JL9qFDh+Luu++OlpaWqKuriwMHDlz0nFKpFKtXr44lS5bE+vXr48iRIzMeP3v2bLS1tcWtt94an/rUp6KxsfG6XwAAAADUirJL9vj4eLS1tUWpVLrk4/v374/e3t7YsWNHHD9+PNra2mLTpk1x+vTp6eesWLEinnnmmTh58mR84QtfiJGRkcvub2JiIsbGxmbcAAAAoBaVXbI3b94cDz/8cGzZsuWSj+/atSu2bdsWXV1d0draGrt3746lS5fG3r17L3puU1NTtLW1xXe+853L7m/nzp3R0NAwfVu1alW5SwYAAICKyPUz2RcuXIiBgYHo7Oz83x0sWBCdnZ1x+PDhiIgYGRmJc+fORUTE6OhoHDp0KG6//fbLbnP79u0xOjo6fRsaGspzyQAAAJCbXK8ufubMmZicnIympqYZ9zc1NcWPf/zjiIj4xS9+ER//+MenL3j2iU98It72trdddpv19fVRX1+f5zIBAABgTlT8K7zWrVsXg4ODZf++UqkUpVIpJicn818U1Dj5J3VmgJTJP6kzAxRNrqeLNzY2xsKFCy+6kNnIyEg0NzfPatvd3d1x4sSJOHr06Ky2A0Uk/6TODJAy+Sd1ZoCiybVkL168ONasWRP9/f3T901NTUV/f39s2LBhVtsulUrR2toaa9eune0yoXDkHyBd3gNInRmgaMou2efPn4/BwcHpU75PnjwZg4ODcerUqYiI6O3tjT179sS+ffviueeei/vvvz/Gx8ejq6trVgv1L1ikTP5Jnb9gkTLvAaTODFA0ZX8m+9ixY3HXXXdN/7q3tzciIu6777544oknYuvWrfGrX/0q+vr6Ynh4ONrb2+PgwYMXXQwNAK5Vd3d3dHd3x9jYWDQ0NFR7OQAAl1V2yd64cWNkWXbF5/T09ERPT891LwoAAACKKNfPZM8lpwqSMvkndWaAlMk/qTMDFE1hSrbPYpAy+Sd1ZoCUyT+pMwMUTWFKNgAAANS6wpRsp4mQMvkndWaAlMk/qTMDFE1hSrbTREiZ/JM6M0DK5J/UmQGKpjAlGwAAAGqdkg0AAAA5UbIBAAAgJ4Up2S54QMrkn9SZAVIm/6TODFA0hSnZLnhAyuSf1JkBUib/pM4MUDSFKdkAAABQ65RsAAAAyImSDQAAADkpTMl2wQNSJv+kzgyQMvkndWaAoilMyXbBA1Im/6TODJAy+Sd1ZoCiKUzJBgAAgFqnZAMAAEBOlGwAAADIiZINAAAAOVGyAQAAICeFKdku3U/K5J/UmQFSJv+kzgxQNIUp2S7dT8rkn9SZAVIm/6TODFA0hSnZAAAAUOuUbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwo2QAAAJCTwpRsX0JPyuSf1JkBUib/pM4MUDSFKdm+hJ6UyT+pMwOkTP5JnRmgaApTsgEAAKDWKdkAAACQEyUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJxUvGQPDQ3Fxo0bo7W1Nd7+9rfHl7/85UovAQAAAObEoorvcNGiePzxx6O9vT2Gh4djzZo18Z73vCde//rXV3opAAAAkKuKl+xbbrklbrnlloiIaG5ujsbGxnj55ZeVbAAAAAqv7NPFDx06FHfffXe0tLREXV1dHDhw4KLnlEqlWL16dSxZsiTWr18fR44cueS2BgYGYnJyMlatWlX2wgEAAKDWlF2yx8fHo62tLUql0iUf379/f/T29saOHTvi+PHj0dbWFps2bYrTp0/PeN7LL78cH/nIR+Jzn/vcFfc3MTERY2NjM24AAABQi8ou2Zs3b46HH344tmzZcsnHd+3aFdu2bYuurq5obW2N3bt3x9KlS2Pv3r3Tz5mYmIh77703HnzwwXjHO95xxf3t3LkzGhoapm+OegMAAFCrcr26+IULF2JgYCA6Ozv/dwcLFkRnZ2ccPnw4IiKyLIuPfvSj8e53vzs+/OEPX3Wb27dvj9HR0enb0NBQnksGAACA3ORass+cOROTk5PR1NQ04/6mpqYYHh6OiIjvfve7sX///jhw4EC0t7dHe3t7/PCHP7zsNuvr62P58uUzbgAAAFCLKn518Xe+850xNTVV9u8rlUpRKpVicnJyDlYFtU3+SZ0ZIGXyT+rMAEWT65HsxsbGWLhwYYyMjMy4f2RkJJqbm2e17e7u7jhx4kQcPXp0VtuBIpJ/UmcGSJn8kzozQNHkWrIXL14ca9asif7+/un7pqamor+/PzZs2JDnrgAAAKDmlF2yz58/H4ODgzE4OBgRESdPnozBwcE4depURET09vbGnj17Yt++ffHcc8/F/fffH+Pj49HV1TWrhZZKpWhtbY21a9fOajtQRPJP6swAKZN/UmcGKJqyS/axY8eio6MjOjo6IuLVUt3R0RF9fX0REbF169Z47LHHoq+vL9rb22NwcDAOHjx40cXQyuU0EVIm/6TODJAy+Sd1ZoCiKfvCZxs3bowsy674nJ6enujp6bnuRQEAAEAR5fqZ7LnkNBFSJv+kzgyQMvkndWaAoilMyXaaCCmTf1JnBkiZ/JM6M0DRFKZkAwAAQK0rTMl2mggpk39SZwZImfyTOjNA0RSmZDtNhJTJP6kzA6RM/kmdGaBoClOyAQAAoNYp2QAAAJATJRsAAAByUpiS7YIHpEz+SZ0ZIGXyT+rMAEVTmJLtggekTP5JnRkgZfJP6swARVOYkg0AAAC1TskGAACAnCjZAAAAkJPClGwXPCBl8k/qzAApk39SZwYomsKUbBc8IGXyT+rMACmTf1JnBiiawpRsAAAAqHVKNgAAAOREyQYAAICcKNkAAACQEyUbAAAAcrKo2gu4VqVSKUqlUkxOTlZ7KVBx8k/qzAApk39SV4kZWP3gk5d97OePvHfO9sv8VJgj2S7dT8rkn9SZAVIm/6TODFA0hSnZAAAAUOsKc7o4MLeudJpUhFOlAADgWjiSDQAAADlxJBsScrWj1ZAyF70BAPLgSDYAAADkRMkGAACAnCR3urjTAQEAAJgrhSnZlfgS+uvlqszMtVrOP1SCGSBleeXfgQaKynsARVOYkt3d3R3d3d0xNjYWDQ0N1V4OVJT8kzozQMrkn9TlMQOzufirf6CiXD6TDQAAADkpzJHsavPVRwDpchSD+W42f88xAwAzKdkV4C9nAAAAaXC6OAAAAOTEkezf4pRwyJ8zOUidGQCAtCjZACTBP6QCAJXgdHEAAADIiSPZAFCDnGZOUcgqKZN/LkXJBgBgTiggQIqqcrr4li1b4qabbor3v//91dg9AAAAzImqHMn+5Cc/GR/72Mdi37591dg9cB3m4mjE1S5E5SgHAFBU/p6Trqocyd64cWMsW7asGrsGAACAOVP2kexDhw7Fo48+GgMDA/HSSy/FV77ylbj33ntnPKdUKsWjjz4aw8PD0dbWFp/97Gdj3bp1ea0ZqDG+GgkAAF5V9pHs8fHxaGtri1KpdMnH9+/fH729vbFjx444fvx4tLW1xaZNm+L06dOzXiwAAADUsrKPZG/evDk2b9582cd37doV27Zti66uroiI2L17dzz55JOxd+/eePDBB8te4MTERExMTEz/emxsrOxtAAAAQCXkeuGzCxcuxMDAQGzfvn36vgULFkRnZ2ccPnz4ura5c+fOeOihh/JaIlAgvvqFIvBxCQAqyQXVal+uFz47c+ZMTE5ORlNT04z7m5qaYnh4ePrXnZ2d8YEPfCCeeuqpuPXWW69YwLdv3x6jo6PTt6GhoTyXDAAAALmpyld4ffOb37zm59bX10d9fX2USqUolUoxOTk5hyubPxwBnF/kn9SZAVI2X/PvaBzXar7OAPNXrkeyGxsbY+HChTEyMjLj/pGRkWhubp7Vtru7u+PEiRNx9OjRWW0Hikj+SZ0ZIGXyT+rMAEWTa8levHhxrFmzJvr7+6fvm5qaiv7+/tiwYUOeuwIAAICaU/bp4ufPn4/nn39++tcnT56MwcHBeMMb3hC33XZb9Pb2xn333Rd33nlnrFu3Lh5//PEYHx+fvtr49XKayMVcbCcd8k/qzMBMTrNNi/yXx0fm5p/5OgOyOn+VfST72LFj0dHRER0dHRER0dvbGx0dHdHX1xcREVu3bo3HHnss+vr6or29PQYHB+PgwYMXXQytXE4TIWXyT+rMACmTf1JnBiiaso9kb9y4MbIsu+Jzenp6oqen57oXBQAAAEVUlauLXw+niZCy+Zp/uFZmgJSlmn8fi+M1Kc6A/Bdbrhc+m0tOEyFl8k/qzAApk39SZwYomsKUbAAAAKh1SjYAAADkpDAlu1QqRWtra6xdu7baS4GKk39SZwZImfyTOjNA0RSmZPssBimTf1JnBkiZ/JM6M0DRFKZkAwAAQK1TsgEAACAnvicbCkD+Sd18nQHfg8q1mK/5h2tlBiiawhzJ9lkMUib/pM4MkDL5J3VmgKIpTMkGAACAWqdkAwAAQE6UbAAAAMiJC58l6EoX2vn5I++t4Eq4VvJP6swAKZN/UmcGKJrCHMl2wQNSJv+kzgyQMvkndWaAoilMyQYAAIBap2QDAABATpRsAAAAyImSDQAAADlRsgEAACAnvsKLZBXpq8zk/2JX+vldTa39fLk6M1CeIv3/jauT//xc7b3DfNQmM1Ae7wHVV5gj2S7dT8rkn9SZAVIm/6TODFA0hSnZAAAAUOuUbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwo2QAAAJCTRdVewLVK8Uvor/RF8tXa5/V+gf1cbTcVKea/aGaT8Sv9XrPxKjNQ++Yqx+ZD/ougGn9/SiX/EWYgT9fbL1LKWx4KcyTbl9CTMvkndWaAlMk/qTMDFE1hSjYAAADUOiUbAAAAcqJkAwAAQE6UbAAAAMiJkg0AAAA5UbIBAAAgJ0o2AAAA5ETJBgAAgJwo2QAAAJCTqpTsr371q3H77bfHm9/85vj85z9fjSUAAABA7hZVeoevvPJK9Pb2xre//e1oaGiINWvWxJYtW+Lmm2+u9FIAAAAgVxU/kn3kyJF461vfGitXrowbb7wxNm/eHF//+tcrvQwAAADIXdkl+9ChQ3H33XdHS0tL1NXVxYEDBy56TqlUitWrV8eSJUti/fr1ceTIkenHXnzxxVi5cuX0r1euXBkvvPDC9a0eAAAAakjZJXt8fDza2tqiVCpd8vH9+/dHb29v7NixI44fPx5tbW2xadOmOH369KwXCwAAALWs7JK9efPmePjhh2PLli2XfHzXrl2xbdu26OrqitbW1ti9e3csXbo09u7dGxERLS0tM45cv/DCC9HS0nLZ/U1MTMTY2NiMGwAAANSiXC98duHChRgYGIjt27dP37dgwYLo7OyMw4cPR0TEunXr4tlnn40XXnghGhoa4mtf+1p8+tOfvuw2d+7cGQ899FCey6RgVj/45GUf+/kj763gSpgvrpSp+bTPKzFX6ZpNFuciG3M1G7X2OimGVN4fvAdQrrn6f+pc5X82+8xjBnK98NmZM2dicnIympqaZtzf1NQUw8PDERGxaNGi+MxnPhN33XVXtLe3xwMPPHDFK4tv3749RkdHp29DQ0N5LhkAAAByU/Gv8IqIuOeee+Kee+65pufW19dHfX19lEqlKJVKMTk5Ocerg9oj/6TODJAy+Sd1ZoCiyfVIdmNjYyxcuDBGRkZm3D8yMhLNzc2z2nZ3d3ecOHEijh49OqvtQBHJP6kzA6RM/kmdGaBoci3ZixcvjjVr1kR/f//0fVNTU9Hf3x8bNmzIc1cAAABQc8ou2efPn4/BwcEYHByMiIiTJ0/G4OBgnDp1KiIient7Y8+ePbFv37547rnn4v7774/x8fHo6uqa1UJLpVK0trbG2rVrZ7UdKCL5J3VmgJTJP6kzAxRN2SX72LFj0dHRER0dHRHxaqnu6OiIvr6+iIjYunVrPPbYY9HX1xft7e0xODgYBw8evOhiaOVymggpk39SZwZImfyTOjNA0ZR94bONGzdGlmVXfE5PT0/09PRc96IAAACgiKpydfHr8dpVBV955ZWIiBgbG7vk86Ym/l8ll5Wcy/25X83Vfi5X2u6Vfu/1rud6t/va/Vf7h6a8XWv+I8zAfDObjF/J9c5Vrc+A/M+tufh/9Wx+ZpXebq3nP8IM1Kq5+JlV+u9Av/1Yrc6A/Nemavw/azb7zOM9oC6r9JTM0i9/+ctYtWpVtZdB4oaGhuLWW2+t+H7ln1phBkiZ/JM6M0DKriX/hSvZU1NT8eKLL8ayZcuirq5uxmNjY2OxatWqGBoaiuXLl1dphfmbj6+rqK8py7I4d+5ctLS0xIIFuV6c/5pcKf8Rxf1zne/m08+llmdgPv05z2dF/jnJP7NV9J+TGeB6zJefTTn5L8zp4q9ZsGDBVf/lYPny5YX+AV7OfHxdRXxNDQ0NVdv3teQ/oph/rimYLz+XWp+B+fLnPN8V9eck/+ShyD8nM8D1mg8/m2vNf+X/CQoAAADmKSUbAAAAcjKvSnZ9fX3s2LEj6uvrq72UXM3H1zUfX1Mt8Odam/xcKsOfczH4Oc0Nf67F4Oc0d/zZ1q4UfzaFu/AZAAAA1Kp5dSQbAAAAqknJBgAAgJwo2QAAAJATJRsAAAByomQDAABATuZVyS6VSrF69epYsmRJrF+/Po4cOVLtJV3W3//930ddXd2M21ve8pbpx//nf/4nuru74+abb44bb7wx/vIv/zJGRkZmbOPUqVPx3ve+N5YuXRpvfOMb41Of+lS88sorFXsNhw4dirvvvjtaWlqirq4uDhw4MOPxLMuir68vbrnllrjhhhuis7MzfvrTn854zssvvxwf+tCHYvny5bFixYr4q7/6qzh//vyM5/zgBz+Id73rXbFkyZJYtWpV/MM//MNcv7RCKlL+i0jea58ZqBzzUHvkv3Lkv/bI/9yT+zJl88SXvvSlbPHixdnevXuzH/3oR9m2bduyFStWZCMjI9Ve2iXt2LEje+tb35q99NJL07df/epX04//9V//dbZq1aqsv78/O3bsWPbHf/zH2Tve8Y7px1955ZXsjjvuyDo7O7Onn346e+qpp7LGxsZs+/btFXsNTz31VPZ3f/d32b/9279lEZF95StfmfH4I488kjU0NGQHDhzInnnmmeyee+7J3vSmN2W//vWvp5/z53/+51lbW1v2ve99L/vOd76T/cEf/EH2wQ9+cPrx0dHRrKmpKfvQhz6UPfvss9kXv/jF7IYbbsj+5V/+pVIvsxCKlv8ikvfaZgYqyzzUFvmvLPmvLfJfGXJfnnlTstetW5d1d3dP/3pycjJraWnJdu7cWcVVXd6OHTuytra2Sz529uzZ7HWve1325S9/efq+5557LouI7PDhw1mWvRr0BQsWZMPDw9PP+ed//uds+fLl2cTExJyu/VL+77BNTU1lzc3N2aOPPjp939mzZ7P6+vrsi1/8YpZlWXbixIksIrKjR49OP+drX/taVldXl73wwgtZlmXZP/3TP2U33XTTjNf0t3/7t9ntt98+x6+oWIqW/6KT99pjBqrHPFSf/FeP/Fef/Fee3F/dvDhd/MKFCzEwMBCdnZ3T9y1YsCA6Ozvj8OHDVVzZlf30pz+NlpaW+L3f+7340Ic+FKdOnYqIiIGBgfjNb34z4/W85S1vidtuu2369Rw+fDje9ra3RVNT0/RzNm3aFGNjY/GjH/2osi/kEk6ePBnDw8MzXkNDQ0OsX79+xmtYsWJF3HnnndPP6ezsjAULFsT3v//96ef8yZ/8SSxevHj6OZs2bYqf/OQn8d///d8VejW1raj5n0/kvbrMQG0xD5Ul/7VF/itL/muD3F9sXpTsM2fOxOTk5IzCGRHR1NQUw8PDVVrVla1fvz6eeOKJOHjwYPzzP/9znDx5Mt71rnfFuXPnYnh4OBYvXhwrVqyY8Xt++/UMDw9f8vW+9li1vbaGK/1MhoeH441vfOOMxxctWhRveMMbCvM6a0ER8z/fyHt1mYHaYh4qS/5ri/xXlvzXBrm/2KJqLyBVmzdvnv7vt7/97bF+/fr43d/93fjXf/3XuOGGG6q4MgAAAK7XvDiS3djYGAsXLrzo6tsjIyPR3NxcpVWVZ8WKFfGHf/iH8fzzz0dzc3NcuHAhzp49O+M5v/16mpubL/l6X3us2l5bw5V+Js3NzXH69OkZj7/yyivx8ssvF+Z11oL5kP+ik/fqMgO1xTxUlvzXFvmvLPmvDXJ/sXlRshcvXhxr1qyJ/v7+6fumpqaiv78/NmzYUMWVXbvz58/Hz372s7jllltizZo18brXvW7G6/nJT34Sp06dmn49GzZsiB/+8IczwvqNb3wjli9fHq2trRVf///1pje9KZqbm2e8hrGxsfj+978/4zWcPXs2BgYGpp/zrW99K6ampmL9+vXTzzl06FD85je/mX7ON77xjbj99tvjpptuqtCrqW3zIf9FJ+/VZQZqi3moLPmvLfJfWfJfG+T+Eqp95bW8fOlLX8rq6+uzJ554Ijtx4kT28Y9/PFuxYsWMq2/XkgceeCD7j//4j+zkyZPZd7/73ayzszNrbGzMTp8+nWXZq1/hddttt2Xf+ta3smPHjmUbNmzINmzYMP37X/sKrz/7sz/LBgcHs4MHD2a/8zu/U9Gv8Dp37lz29NNPZ08//XQWEdmuXbuyp59+OvvFL36RZdmrl/JfsWJF9u///u/ZD37wg+wv/uIvLnkp/46Ojuz73/9+9p//+Z/Zm9/85hmX8j979mzW1NSUffjDH86effbZ7Etf+lK2dOnSQl7Kfy4VLf9FJO+1zQxUlnmoLfJfWfJfW+S/MuS+PPOmZGdZln32s5/Nbrvttmzx4sXZunXrsu9973vVXtJlbd26NbvllluyxYsXZytXrsy2bt2aPf/889OP//rXv87+5m/+JrvpppuypUuXZlu2bMleeumlGdv4+c9/nm3evDm74YYbssbGxuyBBx7IfvOb31TsNXz729/OIuKi23333Zdl2auX8//0pz+dNTU1ZfX19dmf/umfZj/5yU9mbOO//uu/sg9+8IPZjTfemC1fvjzr6urKzp07N+M5zzzzTPbOd74zq6+vz1auXJk98sgjlXqJhVKk/BeRvNc+M1A55qH2yH/lyH/tkf+5J/flqcuyLKvEEXMAAACY7+bFZ7IBAACgFijZAAAAkBMlGwAAAHKiZAMAAEBOlGwAAADIiZINAAAAOVGyAQAAICdKNgAAAOREyQYAAICcKNkAAACQEyUbAAAAcvL/AQN7PpUNW6WGAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 100% intersection of bootstrap runs"
],
"metadata": {
"id": "FeWr0NMeYtfh"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"GG=None\n",
"for i in np.sort(count.index)[4:]:\n",
" if GG is None:\n",
" GG=nx.intersection(graphs['graph_run1'],graphs['graph_run2'])\n",
" else:\n",
" GG=nx.intersection(GG,graphs['graph_'+i])"
],
"metadata": {
"id": "92kTm36A-soA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"degrees = [val for (node, val) in GG.degree()]\n",
"x=plt.hist(degrees,log=True,bins=20)"
],
"metadata": {
"id": "1FTIeXpSRvdC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
},
"outputId": "5c6f7e56-8317-4314-ea4b-64added8f11f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(GG.number_of_nodes())\n",
"print(GG.number_of_edges())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9otzLB77larV",
"outputId": "b026e0cd-feeb-4064-e644-5506609926e4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"3408\n",
"7809\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## see how lowering confidence threshold, based on presence in X/10 runs changes the intersection graph"
],
"metadata": {
"id": "2ws3fpq_Yyj8"
}
},
{
"cell_type": "code",
"source": [
"DD=data[['TF','Gene','source_file']].drop_duplicates()\n"
],
"metadata": {
"id": "eyLd4arQW5sb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"grouped = DD.groupby(['TF','Gene']).count()\n",
"intersect=grouped.reset_index()[['TF','Gene','source_file']]"
],
"metadata": {
"id": "8qalC-agT7so"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### 80% confidence in links based on bootstrap runs"
],
"metadata": {
"id": "C00cDLX3ZDYv"
}
},
{
"cell_type": "code",
"source": [
"ninety=intersect[intersect.source_file>7]"
],
"metadata": {
"id": "Dw09EqRBVBXf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"G = nx.DiGraph() # Use DiGraph for directed edges, or Graph for undirected\n",
"\n",
"# Step 3: Add edges to the graph\n",
"for index, row in ninety.iterrows():\n",
" gene = row['Gene']\n",
" tf = row['TF']\n",
" rho_TF2G = row['source_file']\n",
"\n",
" # Add an edge from TF to Gene with weight as rho_TF2G\n",
" G.add_edge(tf, gene, weight=rho_TF2G/10)"
],
"metadata": {
"id": "uSLb4EtiVSb-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"degrees = [val for (node, val) in G.degree()]\n",
"x=plt.hist(degrees,log=True,bins=20)"
],
"metadata": {
"id": "QvFNqw_SXrYN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
},
"outputId": "5781c486-8a7b-4ce5-c747-c92cad0c3870"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(G.number_of_nodes())\n",
"print(G.number_of_edges())"
],
"metadata": {
"id": "LTeRuvB6YYbd",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "755c32e1-aeef-4af9-a3f8-9fa5d8400c71"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"3167\n",
"16435\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# !pip install graphistry\n",
"# import graphistry\n",
"# from google.colab import userdata\n",
"# g_user=userdata.get('g_user')\n",
"# g_pass=userdata.get('g_pass')\n",
"# graphistry.register(api=3,protocol=\"https\", server=\"hub.graphistry.com\", username=g_user, password=g_pass) ## key id, secret key\n"
],
"metadata": {
"id": "W1uRSfg07eGE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plotter = graphistry.bind(source='TF', destination='Gene')\n",
"plotter.plot(ninety[['TF','Gene','source_file']])"
],
"metadata": {
"id": "AQsxAI8sX4WH",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 543
},
"outputId": "b0efbaad-74ab-491a-da46-9520cd0361a7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <iframe id=\"ab4f2ba6-6adc-4873-8dd3-b0fc25b37447\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=0417481b40934a6c975a4c2cc007ebbd&type=arrow&viztoken=dd7bb95b-ffa1-40a1-b02f-eca8c207c22a&usertag=a97c3297-pygraphistry-0.35.4&splashAfter=1736485652&info=true\"\n",
" allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
" oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
" style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
" \n",
" >\n",
" </iframe>\n",
" \n",
" <script>\n",
" try {\n",
" $(\"#ab4f2ba6-6adc-4873-8dd3-b0fc25b37447\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
" } catch (e) { console.error('exn catching scroll', e); }\n",
" </script>\n",
" "
]
},
"metadata": {},
"execution_count": 141
}
]
}
],
"metadata": {
"colab": {
"provenance": [],
"mount_file_id": "1hWznfna_1LiCObJ7z0ZjaAXx8wucVx3U",
"authorship_tag": "ABX9TyMt1KhZQy0W4RZqDE56Ex6Z",
"include_colab_link": true
},
"kernelspec": {
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"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
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}
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