905 lines (904 with data), 199.3 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "60f20d62-82b4-4cc0-8d2b-d11bdb3434a8",
"metadata": {},
"outputs": [],
"source": [
"import hummuspy as hummus\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import networkx as nx"
]
},
{
"cell_type": "markdown",
"id": "701a1bed-5383-40c3-866a-caaa45c268f8",
"metadata": {},
"source": [
"### Load config file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38b8b52a-5684-4302-98e4-35311a66df39",
"metadata": {},
"outputs": [],
"source": [
"config = hummus.config.open_config(\"../HuMMuS/hummus_package/vignettes/test_multiplex_genes/config/config.yml\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "08bdde8d-9afa-47ea-9a74-c6666e16200f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hummus.config.check_lamb(config['lamb'], config)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "14cdb5c9-f886-45ee-b597-d52e1def30a4",
"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>RNA</th>\n",
" <th>TF</th>\n",
" <th>peaks</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RNA</th>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TF</th>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peaks</th>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RNA TF peaks\n",
"RNA 0.5 0.0 0.5\n",
"TF 0.0 0.5 0.0\n",
"peaks 0.5 0.5 0.5"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['lamb']"
]
},
{
"cell_type": "markdown",
"id": "7c45fa3f-ec86-418a-9b40-487678454b10",
"metadata": {},
"source": [
"### We can draw a graph to illustrate the probability matrix to jump in-between layers"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bf759c5e-c01a-4d7d-926b-58d6772ad4bb",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hummus.config.draw_config(config)"
]
},
{
"cell_type": "markdown",
"id": "0ae3b9c2-d00b-419c-96c2-6769c5d7182c",
"metadata": {},
"source": [
"_If the order is not suiting you, you can reorganise the columns in 'lamb' parameter_"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e1940ad2-272e-4236-8d77-48930bb905ab",
"metadata": {},
"outputs": [],
"source": [
"config['lamb'] = config['lamb'].loc[:, ['TF', 'peaks', 'RNA']]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dd0d8559-4b25-4033-9eff-73579fcfadcb",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hummus.config.draw_config(config)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "400d782c-049f-4891-a086-d6f5f8dbab8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Drawing the default configs for\n",
"- GRN\n",
"- Enhancers\n",
"- Binding regions\n",
"- Target genes\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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XG7J/+vTpGjhwYJirAoDWR5AB2qF58+Y1eYfS2LFjdfnll4e5IgBoGwQZoJ35+OOP9dprr4XsGzx4sMaPHx/migCg7RBkgHakuLhYM2bMCNl3wgknaMaMGXJyixCAdoS/0YB2IhAIaNq0aSqr39DlR9xutx577DF52CYXQDtDkAHaicWLF+uzzz4L2XfPPfcoKysrzBUBQNsjyADtwBdffKHnn38+ZN/gwYN1zTXXhLkiAAgPggxgc16vV1OnTlUgxBMc09LSNHXqVDnYhQ5AO0WQAWzMMAzNmjVL3333XVCf0+nUrFmzlJSUZEFlABAeBBnAxlavXq33338/ZN+tt97KpncA2j2CDGBTlZWVevLJJ0P2nXnmmbrxxhvDWxAAWIAgA9jUc889p+Li4qD2jh076pFHHmG/GABRgb/pABv6xz/+odzc3JB999xzj7p27RrmigDAGgQZwGYCgYAee+yxkHcpnXXWWRoyZIgFVQGANQgygM288cYb+vLLL4PaY2Njdd9993GrNYCoQpABbKS0tFTz5s0L2ffrX/9avXr1CnNFAGAtggxgI3PmzFF5eXlQe0ZGhm666SYLKgIAaxFkAJv49NNP9c4774TsmzJlitxud5grAgDrEWQAGzAMQ3Pnzg3Zd8kll+jss88Oc0UAEBkIMoANrFu3Ttu2bQtqT0hI0N13321BRQAQGQgyQIQzDEMLFy4M2XfrrbeyZwyAqEaQASJcU6Mx3bp107BhwyyoCAAiB0EGiGCHG4258cYbFRcXF+aKACCyEGSACLZ27domR2N+9atfhb8gAIgwBBkgQjEaAwDNI8gAEWrt2rXKz88Pamc0BgB+QJABIhCjMQDQMgQZIAJt3bqV0RgAaAGCDBCB3njjjZDtN910E6MxAPAjBBkgwlRWVmrlypVB7cnJybr66qstqAgAIhdBBogwq1atUlVVVVD7kCFDGI0BgJ8gyAAR5s033wzZztoYAAhGkAEiyI4dO/TFF18EtZ944onq27evBRUBQGQjyAAR5K233grZztoYAAiNIANEiLq6Oq1YsSKoPS4uTkOGDLGgIgCIfAQZIEKsX79epaWlQe0XX3yxkpOTLagIACIfQQaIEB988EHIdqaVAKBpBBkgAhiGoby8vKD27t2768wzz7SgIgCwB4IMEAF27Nih4uLioPZzzz1XTie/pgDQFP6GBCJAqNEYScrJyQlzJQBgLwQZIAJs3LgxqM3hcDCtBADNIMgAFqurq9Onn34a1N6vXz+lpKRYUBEA2AdBBrDYV199pcrKyqB2ppUAoHkEGcBioaaVJGnQoEFhrgQA7IcgA1js888/V2xsrGJiYhra3G63Tj31VAurAgB7iGn+EABt6bnnnmv4uqamRl988YX++c9/Ki4uzsKqAMAeCDJABImLi9Ppp5/O3UoA0EIEGSDCuFwuq0sAANtgjQwAALAtggwAALAtggwAALAtggwAALAtggzQQj6fT1OmTFF6erri4+OVk5OjlStXNnvejBkz5HA4gl4ejycMVQNA+8ZdS0AL3XjjjVq2bJkmTJigPn366KWXXtIVV1yh1atX67zzzmv2/AULFigxMbHhe+5OAoBjR5ABWiAvL0+5ubmaPXu2Jk2aJEkaM2aM+vfvr8mTJ2v9+vXNXmPYsGHq0qVLW5cKAFGFqSWgBZYtWyaXy6Vx48Y1tHk8Ho0dO1YbNmzQ7t27m72GYRjyer0yDKMtSwWAqEKQAVpg06ZN6tu3r5KTkxu11z/YcfPmzc1eIysrSykpKUpKStLo0aNVVFTUFqUCQFRhaglogb179yotLS2ovb6tsLCwyXM7deqkO+64Q2effbbcbrc+/vhjPfvss8rLy9Onn34aFI4AAC1HkAFaoKqqSm63O6i9/s6jqqqqJs+96667Gn0/dOhQDRo0SKNGjdL8+fN13333tW6xABBFmFoCWiA+Pl4+ny+ovbq6uqH/SIwcOVI9evTQqlWrWqU+AIhWBBmgBdLS0rR3796g9vq29PT0I75mZmamSktLj7k2AIhmBBmgBbKzs7V9+3Z5vd5G7Rs3bmzoPxKGYWjHjh3q2rVra5UIAFGJIAO0wLBhw+T3+7Vw4cKGNp/Pp8WLFysnJ0eZmZmSpF27dunrr79udG5JSUnQ9RYsWKCSkhINGTKkbQsHgHaOxb5AC+Tk5Oi6667T/fffr+LiYvXu3Vsvv/yyduzYoUWLFjUcN2bMGK1Zs6bRXjG9evXS8OHDNWDAAHk8Hq1du1a5ubnKzs7W+PHjrfg4ANBuEGSAFnrllVc0bdo0LVmyRAcOHNDAgQO1YsUKXXDBBYc9b9SoUVq/fr2WL1+u6upq9erVS5MnT9bUqVOVkJAQpuoBoH1yGC3YZtTr9SolJUVlZWXseQEAANpcS7MHa2QAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWSACNSCXREAACLIABHH7/errKzM6jIAwBbY2RewUFFRkZ555hlJ5ihMRUWFtm7dqmHDhunWW2+1uDoAiHwEGcBCXbt21YcffqiqqqpG7du2bbOoIgCwF6aWAAs5nU7169cvqP3LL79knQwAtABBBrDYKaecEtTm9Xq1Z88eC6oBAHshyAAWO/nkk0O2M70EAM0jyAAWCzUiI0mbNm0KcyUAYD8EGcBiaWlpSk1NDWr/8MMPFQgELKgIAOyDIANYzOFw6JxzzglqLykp0ZYtWyyoCADsgyADRIDBgweHbF+1alWYKwEAeyHIABEgJydHiYmJQe0ffPAB00sAcBgEGSACxMbG6sILLwxqLy4u1pdffmlBRQBgDwQZIEIwvQQAR44gA0SInJwcdejQIah91apVTC8BQBMIMkCEiIuLa3J6aevWrRZUBACRjyADRJCmppdyc3PDXMnh+Xw+TZkyRenp6YqPj1dOTo5WrlzZ7HkzZsyQw+EIenk8njBUDaA94unXQAT5+c9/rqSkJJWXlzdqf//991VYWKj09HSLKmvsxhtv1LJlyzRhwgT16dNHL730kq644gqtXr1a5513XrPnL1iwoNFdWi6Xqy3LBdCOEWSACBIXF6drrrlGr7zySqP2QCCgV199VZMmTbKosh/k5eUpNzdXs2fPbqhnzJgx6t+/vyZPnqz169c3e41hw4apS5cubV0qgCjA1BIQYUaMGKGYmOB/Y7z55pvyer0WVNTYsmXL5HK5NG7cuIY2j8ejsWPHasOGDdq9e3ez1zAMQ16vV4ZhtGWpAKIAQQaIMN26ddOQIUOC2quqqrRs2TILKmps06ZN6tu3r5KTkxu1Dxo0SJK0efPmZq+RlZWllJQUJSUlafTo0SoqKmqLUgFEAYIMEIFGjx4dsj03N1c1NTVhrqaxvXv3Ki0tLai9vq2wsLDJczt16qQ77rhDzz//vJYtW6ZbbrlFS5cu1fnnnx8Ro00A7Ic1MkAE6t27t84++2xt2LChUXtpaaneffddXXXVVRZVZo4Mud3uoPb6O4+qqqqaPPeuu+5q9P3QoUM1aNAgjRo1SvPnz9d9993XusUCaPcYkQEi1H/+53+GbF+yZImlG+TFx8fL5/MFtVdXVzf0H4mRI0eqR48e7GAM4KgQZIAIddZZZ6lv375B7f/+97/117/+1YKKTGlpadq7d29Qe33b0dwinpmZqdLS0mOuDUD0IcgAEcrhcDQ5KjNv3jxVVlaGuSJTdna2tm/fHrSmZePGjQ39R8IwDO3YsUNdu3ZtrRIBRBGCDBDBLrnkEh133HFB7fv27dPixYstqMjcA8bv92vhwoUNbT6fT4sXL1ZOTo4yMzMlSbt27dLXX3/d6NySkpKg6y1YsEAlJSUh79QCgOY4jBZs5OD1epWSkqKysrKgWy4BtK3Vq1fr3nvvDWqPjY3VsmXLQgadtnb99dfrL3/5iyZOnKjevXvr5ZdfVl5ent5//31dcMEFkqSLLrpIa9asabRXTEJCgoYPH64BAwbI4/Fo7dq1ys3N1amnnqp169YpISEh7J8FQGRqafZgRAaIcBdddJHOOuusoPba2lrNmTMn/AVJeuWVVzRhwgQtWbJEd955p2pra7VixYqGENOUUaNGKS8vTzNmzNCECRP097//XZMnT9ZHH31EiAFwVBiRAWzg22+/1ciRI0PerTR//vyGzegAoL1gRAZoR3r37q2hQ4eG7Hvqqafk9/vDXBEARAaCDGATt956a8h/lfzzn//U66+/bkFFAGA9ggxgEykpKRo/fnzIvvnz56u4uDjMFQGA9QgygI0MHTpUWVlZQe3l5eWaMWOGpTv+AoAVCDKAjcTExOiee+4J2ZeXl6fc3NwwVwQA1iLIADaTk5PT5OZx8+bN07fffhvmigDAOgQZwIYmT56s7t27B7XX1tbqwQcfVE1NjQVVAUD4EWQAG0pOTtbDDz8sh8MR1Pftt9/q2WeftaAqAAg/ggxgU2eeeWaTD5X805/+pLy8vDBXBADhR5ABbOzWW29V3759Q/bNmDEj6AnVANDeEGQAG4uLi9PMmTMVFxcX1FdcXKyZM2dySzaAdo0gA9hcVlaW7rzzzpB9H3zwgV544YUwVwQA4UOQAdqB66+/Xj//+c9D9i1cuFCrVq0Kc0UAEB4EGaAdcDqdeuihh5SSkhKy/6GHHtLXX38d5qoAoO0RZIB2omvXrnriiSfkcrmC+nw+n+6++27t27fPgsoAoO0QZIB25Mwzz9SUKVNC9hUXF2vSpElslgegXYmxugAArevaa6/Vt99+qz//+c9BfV9++aV++9vfqra2VgUFBerfv78mTZqk9PR0CyoFgGPnMAzDaO4gr9erlJQUlZWVKTk5ORx1ATgGfr9fv/3tb4M2xSsvL9eePXvUtWtXde7cWZI0YMAALVq0SE4nA7QAIkdLswd/cwHtkMvl0uOPP66ePXs2tHm9XhUUFMgwDBUXFzdslrd161Zt2bLFqlIB4JgQZIB2Kjk5WU8//bQSExN18OBB7dmzp6HP7/drx44dqqyslCStWbPGqjIB4JgQZIB27Pjjj9cVV1yhvXv3NrTV1dWptrZWfr9fu3fvVnV1tdasWaMWzDK3mGFINTXSoUPSgQPmnzU1ZjsAtCYW+wLtWHFxsd566y11795dRUVFqqurU11dXUN/XV2ddu3aJafTqR07duhnP/vZEb9HICDt3i3l55uvrVulzZvN8GIY5svhMF+JiVJ2tjRggNSvn/nKzJRYngPgaBFkgHZs+/btqq6uVmpqqioqKoL2kfH7/XI6ndq1a5dWrFih3/72ty2+dkGBtHy5lJsrFRVJ1dVmu2FIbrcUG2uGF6fzh0BTWir99a/Su++ax3o8Uvfu0ogR0tChUkZGa31yANGCu5aAduxf//qXrr/++obvCwoKdPDgQfn9fgUCATkcDrndbklSnz59tHnz5sNeLxCQ1q6Vli6V/vY3c9QlNtYcafF4pBB78TXJ7zfDz6FDUm2teY3LLpOGD5fOO49RGiDacdcSAGVlZenGG29s+P64445TcnKy4uLi5Ha75XK5GtbG7Nq167DX2rxZuvJKadQocyRGktLTzRGVDh2OLMRI5vEdOpjn129js3y5ef0rrzTfDwCaw4gMEAW2bNmit956S6tWrZLX69WuXbtUVVXV6JjU1FTt3r076Nzqamn+fGnBAqm8XOrcWYqPb7taq6rMKajEROm226TbbzdHewBEl5ZmD4IMEEVqamq0du1a/eUvf9HSpUtVUVEhyXzo5JNPPqnx48c3On7zZumBB6RNm8wwkZpqrntpa/XraaqrpdNPl2bNMhcJA4geBBkAh3Xw4EH9z//8j4qLi/XrX/9a/fv3b9S/dKk0bZrk9Updu5oLeMPN55NKSqTkZOmRR8z1MwCiA0EGwFExDGnRImnmTHNBbrdu4RmFOVw9xcXmmppp06Sbb7a2HgDhwWJfAEdl0SLpd78zv7Y6xEjm+3frZn79u99JL75obT0AIgtBBkCD3FxzJMbplLp0sT7E1HM4fqhn5kxz2gsAJIIMgO9t3ixNn25OJ33/YOyI07mzVFdnTjFxezYAiSADQObdQQ88YC7sjYTppKbUTzOVl5v11u8mDCB6EWQAaP588xbrrl0jN8TUq59m2rTJrBtAdCPIAFFu0yZzszuPx5pbrI+G223Wu2ABU0xAtCPIAFEsEJAefNCcqklNtbqaI5Oaaj6naepU83MAiE4EGSCKrV0rbdliLqKN9Cmln3I4zDCzZYu0bp3V1QCwCkEGiGJLl5pPnm7LZye1pfh4s/7cXKsrAWAVggwQpQoKpL/9zXw4o50lJkrvvWd+HgDRhyADRKnly801JlY+daSi4jPl55+p/PwzVVg446iukZxsrvF5/fXWrQ2APRBkgCgUCJjTMbGx5i6+duZ0mp/jtddY9AtEI5v/FQbgaOzeLRUV2X9aqV6HDubnYXoJiD4EGSAK5eebu+J6PFZX0jri483Pk59vdSUAwi3G6gIAhF9+vlRePkNlZSskST17PqvKys918OBb8vvLFB9/irp3v0cez0kN59TUFGr//hdVUfGJ6ur2y+lMVIcOZ6pLl3Fyu3/WcFxtbbFKSp5TdXW+6uqK5fcfksvVQW53X6WmjlBS0kXN1rdv3wsqKXlOkuTx9FPPngvkciXqwIHXdfDgX1RTs0OGUSeXq6Pc7p+pQ4efSxqj/Hzpssta9T8VgAhHkAGi0NatkmH88P133/23amp2NnxfWblJO3eO189+tkRxcT1VXf21du68TYFAecMxfv8Beb0rdejQOvXsuUDx8adIkurqilRW9laj9/P7vaqs/FSVlZ8qPf1hpaT8vyZrO3Dg9YYQ43b3Uc+ez8rlSlRZ2Tv67rtHGx1bV1eiuroS+Xw7lJAwRlu3HvV/EgA2RZABooxhmNv6x8SYe7BIUm1tkbp3n6TY2DTt27dI1dXbFAhUqLj4GR133BMqLHyoIcSkpo5WYuLZqq7+h4qLn1EgUKm9ex/Wz362VA6HQy5XZ3Xr9lvFxmbK5UqU5FJd3XcqKpojv/+A9u1b1GSQ8Xo/0HffPS5Jios7/vsQY95WVV7+4fdHudSjxxTFxWWqrm6/qqu/VlXVl3K7zcctGIb9NvcDcPQIMkCUqa01b7v+8d1KqakjlZo6QpLkdv9M//zntZKkQ4fWqro6Xz7fP7/v69swNRQfP1Dx8f1VVbVFPt+/VF39teLj+ykuLl0uV2cdOPCaqqu/VSBwSNIPwz81Nbvk91fI5erQqK7q6nx5ve9JCiguLlM9ey5QTMwPz01wOGK+/zNWcXEZ8nhOlsvVQSkpQyRJBw9KFRVSXZ15FxOA6ECQAaJMTU3wqEV8fP+Gr+PiesrlSpbf75Vh1Mjn+1dDn8+3XTt33tLEdf+t+Ph+Ki19VUVFTx+2hkCgPCjI1IclyamMjN8rNrZro/6UlKvk9a6UYVRr167bJUkxMd2UkHCGUlNvkNN5sgIB8/MRZIDowV1LQJSprW28Pqa1BALVkqTS0h+eF9C58xj17LlAvXq9ILe7d0O7YYTa8KX+r6OAiovnBh2TmPhz9eq1SB07XiOP50Q5HB7V1RXL6/2rdu4cp7q6PTIMM8gAiB6MyABRJjY2eA1JdfVXSkq6QJJUU7Nbfr9XkuRwxMntzmo4LiHhdPXqtTDomoFAtZxO817uuroSSZLLlaJu3e78vr9KdXXFh60rOXmwamu/U1XVFh069JG+++5xpaU90NBvGIYSEgYqIWHg998HVFr6moqLfy/DqFZV1XolJl6nuLgj+a8BwO4IMkCUiYszg8yPR2VKS1+Vy9VZsbE9tG/foob2xMRz5fH0k9t9gny+f6qy8nMVFk5XUtJgORwxqq3dq6qqr1RevlonnrhakhQbm/b9Opgy7dv3kjyePiotfa0hHDXF4YhTRsZT2rHj16qtLdTBg68rNraHunS5WZJUVDRbdXX71KHDzxUb212SS1VVmxrODwRq5XSKIANEGYIMEGViY80dfYuLf9yWoaKi/250nNOZoK5d/0sOh0Pp6Q833H5dVvaOysreafL6HTteo+LiP0iSSkqekSS5XB0VF9er0S3eocTEdFJm5hzt2HGzAoFDKimZr5iYburY8ZcyDJ/Kyz9QefkHQec5HG7Fxl6oDh3Mu7EARA/WyABRxuGQsrPNu3vqde8+UV26jFNMTDc5HHGKj89Wz57Pye0+XpLk8ZykrKxX1bHjUMXGHieHI1ZOZ5Lc7hPUseNQ9ey5oOFaqamj1LXr7YqNTZPD4VFCwhnq2fM5xcR0blF9bneWMjKekOSSJH333UwdOrRBycmXKyXll4qL6yWnM1GSUy5XqpKSLlKvXi/I7z9Op53GrddAtHEYRvPL/rxer1JSUlRWVqZkKx+VC6BV/P730vTpMxQI1O/s+7w6dDjD4qqOTWGhNHmyNGGC1ZUAaA0tzR6MyABRqF8/88+2uHvJCn6/+Wf95wIQPQgyQBTq189cS9JegkxVlfkATIIMEH0IMkAUysyUOnT4YSTD7ioqpO7dpYwMqysBEG4EGSAKOZ3SvffOUGrqpzrxxE9tvT4mEDA3+bvhhsaPXQAQHfi1B6LU0KHmbdjew2/vEvG8XikpSbr2WqsrAWAFggwQpTIypMsuMx8gaWeHDkmXXsq0EhCtCDJAFBs+3Nwgr6rK6kqOTlWVWf+IEVZXAsAqBBkgip13njRwoLR/v/3uYDIMqbTUrP/cc62uBoBVCDJAFHM6pVmzzDUmpaVWV3NkSkvNNT6PPsoiXyCa8esPRLnsbOm226SKCr927Nijb775Rrt27ZLP57O6tCb5fFJ1tVn3qadaXQ0AKxFkAOiXv9ylQOAzlZW5VVtbq4qKQyoo2C3DCFhdWhDDkPbtk04/Xbr9dqurAWA1ggwQ5YqLi3X33berW7c5crkqVVeXKsOQampqIm5UxjDMp3YnJZlTYh6P1RUBsBpBBohiBw8e1O23367vvvtOCQnblZ6+QA5HQH5/R0mSM8IWn+zfbz5a4ZFHzCkxAIixugAA1qisrNSdd96pHTt2NLSlpq5UIJCgwsJbFBOTpthYt3UF/ohh/HBn1bRp5m3jdXV1mjdvnoqLi/Uf//EfGjx4sBwOh9WlAggzh2E0f9NlSx+lDcAe6urqdOeddyovLy+ozzCk+Pj/0u7dv1Yg4FS3bpKV+aB+OikmxgwxN90k+f11GjBggHbt2iVJcjgcOuWUU3TTTTfp8ssvV69evawrGECraGn2iKxxYwBtzjAMPf744yFDjCT16dNb77wzVP/9307Fx0uFheZdQlbw+aQ9e6T4eOnxx6WbbzZD1VNPPdUQYiTzM3355Zf6/e9/r6FDh2rMmDHKzc1VZWWlNYUDCBuCDBBlXn31Vb3xxhsh+zIyMvTMM88oOTlZw4dLf/6zdNpp5l1C4dw0r34qad8+6YwzzDqGD/+hv7CwsOFrv98v//eP8d6zZ48qKiq0bds2PfnkkxoxYoR27twZnqIBWIIgA0SRjz76SHPmzAnZ16VLF82fP19dunRpaMvOll5/Xbr3XnNqp7Cw7R9nUFVlvk9MjPm+y5cHL+y98MILG31fW1urmpoauVwu7dq1S1XfF1lYWKjnnnuubQsGYCmCDBAltm/frqlTpyrUsrj4+HjNnTtX6enpQX0ej3T33dL//q+5d4vXKxUUSAcPSoFW2mYmEDCvV1BgXv/00833u/vu0LdY//KXv1TG90+JdDqdcjgcio2NVU1Njaqrq/XNN9803Dq+Z8+e1ikSQEQiyABRYN++fZo4cWLDSMWPORwOzZo1S3379j3sNbKzpbffll59VRo61FyrUlgoFRVJFRXS97M7Leb3m0+uLioyr+NwmNd99VXzfQ53e3VcXJx+9atfKTU1VQ6HQwkJCZLMUONyuRQTE6Pdu3fL7/frVLb+Bdo1br8G2jmfz6e7775bRUVFIfsnTJigCy64oEXXcjql8883XwUF5rTTa6+ZYaSszDzGMCS325wacjrNgGIY5qhLXZ25gLf+LiiPR8rIkG64Qbr2WvPrlrrooov0/vvvy+/3a//+/aqrq5Pb7VZycrKqqqpUW1ur8vJyjR07tuUXBWA7BBmgHQsEAnrooYe0bdu2kP3XXHONRo4ceVTXzsiQ7rxTuuMOM9Tk55uvLVukzZvNUZpAwHw5HJLLJSUnm4uHBwyQ+vUzXxkZR/fQx3PPPVdOp1Pp6emqra3VgQMHJKlh1Mnj8SgpKUnPPfec7rvvvqP6jAAiH0EGaMcWLlyoVatWhew766yzNGXKlGPeRM7plHr2NF+XXWa2GYY5+lJTY77i4sxXTEzr7UmTnJysM844Q3//+9/Vs2dPGYbREGLi4+OVmZkpl8ulZcuW6YQTTtB1113XOm8MIKKwRgZop9577z298MILIft69uypJ554QjExbfNvGYdDio2VOnSQOnUy/4yNbf2N9f7zP//z+/dzKCMjQzExMerQoYN69uwpl8vVcNzs2bOb3DcHgL0RZIB2qKCgQDNnzgzZl5ycrDlz5rSLXbrPOeccPfDAA0pPT1daWpruvvtu9e7dO+gZUYFAQPfdd1+jTfQAtA88ogBoZ2prazV27NiQ62JcLpfmz5+vM844w4LKwuODDz7Q5MmTQ/b16tVLL730kpKSksJcFYAjxSMKgCi1YMGCJhf3PvDAA+06xEjSf/zHf+i2224L2bdz50498MADDTsBA7A/ggzQjnzyySd65ZVXQvZdddVVuvrqq8NckTVuvvlmXXrppSH7NmzYoOeffz7MFQFoKwQZoJ0oLS3V9OnTQ/Ydf/zxuvfee8NckXUcDoceeughnXzyySH7Fy9erE2bNoW5KgBtgSADtAOBQEDTp09XaWlpUF9cXJweffRRxcfHW1CZddxut5588slGz46qZxiGpk+frkOHDllQGYDWRJAB2oE//vGP+uSTT0L2TZgwodnHD7RX3bp101NPPRXyNvO9e/dq9uzZFlQFoDURZACb++qrr/Tss8+G7LvggguifiO4U045RbfffnvIvv/7v//TypUrw1wRgNZEkAFsrKqqqsm7cLp166aHHnromHfubQ9Gjx6t008/PWTfY489puLi4jBXBKC1EGQAG3vhhRe0Z8+eoHan06mZM2cqJSXFgqoij9Pp1O9+9zslJiYG9Xm9Xs2YMUOBQMCCygAcK4IMYFP/+te/9Mc//jFk39ixY5scgYhWPXr0aPLhkXl5ecrNzQ1zRQBaA0EGsKFAIKBHH3005JTSwIEDdcstt1hQVeQbMmSILqt/suVPzJs3T99++22YKwJwrAgygA2tWLFCmzdvDmp3uVyaOnVqowcmorEpU6aoe/fuQe21tbV67LHH1IKntgCIIAQZwGbKysr0hz/8IWTfqFGjdMIJJ4S5IntJTk7Www8/HHIR9BdffMFdTIDNEGQAm5k7d67KysqC2nv06KHf/OY3FlRkP2eeeaZGjx4dsm/OnDmqrq4Oc0UAjhZBBrCRL774Qm+++WbIvsmTJ0fd7r3HYvz48SGnmIqLi5t8XhWAyEOQAWyirq5Ojz76aMi+Cy+8UBdccEGYK7I3j8eju+66K2TfSy+9pO+++y7MFQE4GgQZwCaWLl2qf/7zn0HtHo8nqh4I2ZouueQSZWdnB7XX1NRo7ty54S8IwBEjyAA2UFlZqcWLF4fsGz9+vHr06BHmitoHh8OhSZMmhVz4+9577/GEbMAGCDKADSxbtkwHDx4Maj/hhBN0ww03hL+gduSkk07S1VdfHbLvySefZMdfIMIRZIAIV1lZ2eTi03vuuSfkk51xZG6//XZ16NAhqP0f//iHVqxYYUFFAFqKIANEuKZGY04//XSdddZZ4S+oHUpNTW3y1vWXX36ZURkgghFkgAh2uNGYcePG8WTrVjR8+HD17NkzqH3nzp1au3atBRUBaAmCDBDBDjcac8YZZ4S/oHYsNjZWt956a8i+JUuWhLkaAC1FkAEi1OFGY8aPH89oTBv4xS9+obS0tKD2TZs26csvv7SgIgDNIcgAEYrRmPBzuVwaOXJkyD5GZYDIRJABIpDP5zvsaAzaztVXX62kpKSg9tWrV6ugoMCCigAcDkEGiEAffPABozEWSUhI0LBhw4LaA4GAXn31VQsqAnA4BBkgAjX1YMhx48aFuZLoNHz48JD787z55pshnzwOwDoEGSDC7NmzR59++mlQe1ZWFqMxYdKlSxddccUVQe0+n6/JkAnAGgQZIMK8/fbbIduvvvpq7lQKo9GjR4dsX7lyZZgrAXA4BBkgggQCAb311ltB7S6XK+QIAdpOVlaWBg0aFNSen5+vPXv2WFARgFAIMkAE2bhxo4qLi4PaL7zwQnXq1MmCiqLbJZdcErL9/fffD3MlAJpCkAEiSFPrL6666qowVwJJuvjii+V0Bv81uWrVKguqARAKQQaIEAcPHtSHH34Y1N61a1edffbZ4S8I6tixo84888yg9m3btqmwsNCCigD8FEEGiBDvvfee6urqgtp/+ctfyuVyWVARJGnw4MEh2xmVASIDQQaIEE09YZlpJWtddNFFTC8BEYwgA0SAmpoaff7550Htp5xyijIzMy2oCPVSU1ND7t/D9BIQGQgyQAT48ssvVV1dHdTO2pjI0NT00vr168NcCYCfIsgAEeCTTz4J2Z6TkxPmShDKxRdfHLL9q6++CnMlAH6KIANEgLy8vKC2+Ph49e/f34Jq8FOpqanq2bNnUDtBBrAeQQawmNfr1bZt24LazzjjDMXGxlpQEUI55ZRTgtr+/e9/q7Ky0oJqANQjyAAW++yzzxQIBILaQ22PD+uECjKGYSg/P9+CagDUI8gAFtu4cWPI9p///OdhrgSHEyrISEwvAVaLsboAINrFx8fr8ssvl2EYqqio0NatWxUTE6Of/exnVpeGHznxxBMVExOjE088UWlpaQ3TfnFxcRZXBkQ3h2EYRnMHeb1epaSkqKysTMnJyeGoC4hafr9fO3fuVFZWltWl4CfKysqUkpJidRlAVGhp9mBqCYgwLpdLxx9/vNVlIARCDBB5CDJABAq1JT4AIBh/WwIAANsiyAAAANsiyAAAANsiyAAt5PP5NGXKFKWnpys+Pl45OTlauXJls+fNmDFDDocj6OXxeMJQNdoaPxeAtdhHBmihG2+8UcuWLdOECRPUp08fvfTSS7riiiu0evVqnXfeec2ev2DBAiUmJjZ873K52rJchAk/F4C1CDJAC+Tl5Sk3N1ezZ8/WpEmTJEljxoxR//79NXnyZK1fv77ZawwbNkxdunRp61IRRvxcANZjaglogWXLlsnlcmncuHENbR6PR2PHjtWGDRu0e/fuZq9hGIa8Xq9asAclbIKfC8B6BBmgBTZt2qS+ffsG7S5Z/2DHzZs3N3uNrKwspaSkKCkpSaNHj1ZRUVFblIow4ucCsB5TS0AL7N27V2lpaUHt9W2FhYVNntupUyfdcccdOvvss+V2u/Xxxx/r2WefVV5enj799FMe+2Fj/FwA1iPIAC1QVVUlt9sd1F5/h0lVVVWT5951112Nvh86dKgGDRqkUaNGaf78+brvvvtat1iEDT8XgPWYWgJaID4+Xj6fL6i9urq6of9IjBw5Uj169NCqVatapT5Yg58LwHoEGaAF0tLStHfv3qD2+rb09PQjvmZmZqZKS0uPuTZYh58LwHoEGaAFsrOztX37dnm93kbtGzdubOg/EoZhaMeOHeratWtrlQgL8HMBWI8gA7TAsGHD5Pf7tXDhwoY2n8+nxYsXKycnR5mZmZKkXbt26euvv250bklJSdD1FixYoJKSEg0ZMqRtC0eb4ucCsB6LfYEWyMnJ0XXXXaf7779fxcXF6t27t15++WXt2LFDixYtajhuzJgxWrNmTaM9QXr16qXhw4drwIAB8ng8Wrt2rXJzc5Wdna3x48db8XHQSvi5AKxHkAFa6JVXXtG0adO0ZMkSHThwQAMHDtSKFSt0wQUXHPa8UaNGaf369Vq+fLmqq6vVq1cvTZ48WVOnTlVCQkKYqkdb4ecCsJbDaMF2kl6vVykpKSorK2NvAwAA0OZamj1YIwMAAGyLIAMAAGyLIAMAAGyLIAMAAGyLIAMAAGyLIAMAAGyLIAMAAGyLIANEGL/frxZs74QwMwxDdXV1VpcB4CcIMkAEqamp0eeff66tW7daXQp+4ptvvtGf//xnlZSUKBAIWF0OgO/xiALAQiUlJbrqqqskNf4X//XXX6+BAwdaWRp+Ys2aNXr++ef19NNPy+FwKCbG/Otz7ty5OuussyyuDohejMgAFuratas6duyo2traRtMWX331lYVVIZSNGzc2fG0Yhmpra+VwOHTqqadaWBUAggxgsVNOOSWobfv27aqpqbGgGoRSWVkZcrrvtNNOU1xcnAUVAahHkAEsFirI1NbW6ttvv7WgGoTy2Wefye/3B7Xn5ORYUA2AHyPIABYLFWQkppciyY+nlX6MIANYjyADWKxfv34h25v6nyfCLy8vL6itY8eO6tOnjwXVAPgxggxgsaSkJGVlZQW1r1+/XpWVlRZUhB8rLi7Wv/71r6D2QYMGyenkr1DAavwWAhHg4osvDmqrqanR2rVrLagGP7Zu3bqQ7UwrAZGBIANEgMGDB4dsX7VqVZgrwU+9/fbbIdsHDRoU5koAhEKQASJA79691bNnz6D2tWvXMr1koR07dmjLli1B7SeddJLS0tIsqAjATxFkgAjgcDhCjsowvWStt956K2R7/W7MAKxHkAEiBNNLkaWurk4rVqwIao+Li9OQIUMsqAhAKAQZIEL06dNHmZmZQe3r1q1TVVWVBRVFt3Xr1qm0tDSo/eKLL1ZycrIFFQEIhSADRIimppd8Pp9Wr15tQUVN8/l8mjJlitLT0xUfH6+cnBytXLmy2fNmzJghh8MR9PJ4PGGo+si8+eabIduvvvrqMFcC4HB4+jUQQQYPHqzFixcHtf/xj3/U5ZdfLofDYUFVwW688UYtW7ZMEyZMUJ8+ffTSSy/piiuu0OrVq3Xeeec1e/6CBQuUmJjY8L3L5WrLco/Yvn37Qq5NSk9P15lnnmlBRQCaQpABIkjfvn2VlZUVtAHb9u3b9fe//z0ibvnNy8tTbm6uZs+erUmTJkmSxowZo/79+2vy5Mlav359s9cYNmyYunTp0talHrV33nlHgUAgqP3KK69kEzwgwvAbCUQQh8Oh0aNHh+xbsmRJmKsJbdmyZXK5XBo3blxDm8fj0dixY7Vhwwbt3r272WsYhiGv1yvDMNqy1KNSU1Oj3NzcoHaHw6Err7zSgooAHA5BBogwQ4YMUefOnYPaN2zYoG+++caCihrbtGmT+vbtG7TgtX60aPPmzc1eIysrSykpKUpKStLo0aNVVFTUFqUelTfeeEPFxcVB7YMGDVKPHj0sqAjA4RBkgAgTFxenESNGhOz74x//GOZqgu3duzfkZnD1bYWFhU2e26lTJ91xxx16/vnntWzZMt1yyy1aunSpzj//fHm93jaruaVqampCrlGSpBtuuCHM1QBoCYIMEIGGDh2q+Pj4oPZ333035GhBOFVVVcntdge11995dLhbxe+66y7NmzdPI0eO1NChQzVnzhy9/PLL+uabbzR//vw2q7ml3njjDZWUlAS1n3zyyTr33HMtqAhAcwgyQARKTk4OeZuv3+8PuX4jnOLj4+Xz+YLaq6urG/qPxMiRI9WjRw/LN/473GjMuHHjIuaOMQCNEWSACDVy5MiQd8gsX77c0mmYtLQ07d27N6i9vi09Pf2Ir5mZmRly87lwYjQGsCeCDBCh0tPTQ26QV1FRoRdeeMGCikzZ2dnavn17UJjauHFjQ/+RMAxDO3bsUNeuXVurxCN2uNGY8ePHMxoDRDCCDBDBmroVe+nSpfr3v/8d5mpMw4YNk9/v18KFCxvafD6fFi9erJycnIbHLOzatUtff/11o3NDjXgsWLBAJSUllj6/6HCjMeecc44FFQFoKTbEAyLYySefrPPPP18ff/xxo3a/36+nn35ac+fODftoQU5Ojq677jrdf//9Ki4uVu/evfXyyy9rx44dWrRoUcNxY8aM0Zo1axrtFdOrVy8NHz5cAwYMkMfj0dq1a5Wbm6vs7GyNHz8+rJ+jXmVlpV588cWQfYzGAJGPIANEuIkTJ2rDhg2qq6tr1L5hwwatW7euRY8EaG2vvPKKpk2bpiVLlujAgQMaOHCgVqxYoQsuuOCw540aNUrr16/X8uXLVV1drV69emny5MmaOnWqEhISwlR9YwsXLtS+ffuC2hmNAezBYbRga02v16uUlBSVlZXx1FfAAnPmzAm5h0zPnj21dOlSxcbGWlCV/X3zzTcaNWpUyMcR/OEPf2CRL2ChlmYP1sgANnDLLbeoU6dOQe27du3S0qVLLajI/gKBgB577LGQIeaMM85gNAawCYIMYAOJiYn6r//6r5B9//M//2P5rct29Oabb2rLli1B7TExMbr//vtZGwPYBEEGsImrrrpKJ554YlB7RUWFnnnmGQsqsq/S0lLNmzcvZN+YMWN0/PHHh7cgAEeNIAPYhNPp1KRJk0L2vfXWW9qwYUOYK7KvuXPnhtxU8LjjjtPYsWMtqAjA0SLIADZy2mmn6dJLLw3ZN2PGDB08eDC8BdnQZ599phUrVoTsmzJlSsjnSAGIXAQZwGbuvPPOkP+z3b9/vx599FG14EbEqFVbW6vHH388ZN8vfvELFvgCNkSQAWymR48emjBhQsi+Dz74QG+//XZ4C7KRhQsXhtwROSEhQffcc48FFQE4VgQZwIaGDRvW5OjBk08+qT179oS5osiXl5enl156KWTfrbfeqm7duoW3IACtgiAD2JDD4dBDDz2kjh07BvVVVlZq2rRp8vv94S8sQh04cEDTpk0LOe3Wt29fDR8+3IKqALQGggxgU507d9a0adNC9m3ZsqXJ0YdoEwgENGPGDO3fvz+oLyYmRtOnT5fL5bKgMgCtgSAD2NiFF16oa665JmTf888/r6+++irMFUWe3NxcrVu3LmTfnXfeqZNOOinMFQFoTQQZwOYmTpyozMzMoPZAIKD7778/qm/Jzs/P19y5c0P2nXvuubrhhhvCXBGA1kaQAWwuISFBjzzyiJzO4F/nwsJCTZ48WbW1tRZUZq3Kyko98MADQU8Nl6QuXbpoxowZPIYAaAcIMkA70L9/f/3mN78J2ff555/riSeeiLr9ZR5//HHt3r07qN3hcOiRRx4J+RBOAPZDkAHaiZtvvlmnnXZayL433ngjqp6S/c477+idd94J2XfTTTfprLPOCnNFANoKQQZoJ1wul5544gmlpaWF7H/66af1ySefhLmq8Pvqq680c+bMkH0DBw7UuHHjwlwRgLZEkAHakdTUVD399NOKj48P6gsEArrvvvu0c+dOFRYW6vPPP9eBAwcsqLLtFBUVaeLEiaqpqQnqS0xM1MyZMxUTE2NBZQDaisNowcS51+tVSkqKysrKlJycHI66AByDNWvWaNKkSUHrYgzDkN/vb/ifeUJCgh5++GFdfPHFVpTZqiorK3XLLbdo+/btIfsff/xxDR48OMxVAThaLc0ejMgA7dCFF16o22+/vVGbYRj67rvv9M0332jXrl2SzP/5z5o1S5WVlVaU2WoCgYAefPDBJkPMiBEjCDFAO0WQAdqpG2+8UZdffrkkM8QUFhY27ClTUVHRcEfPwYMHm9wwzi7mzZunjz76KGTfOeeco4kTJ4a5IgDhQpAB2imHw6Fp06bppJNOUkFBgbxeb0NfTU2NSktLVVJSIsmcimpNhiHV1EiHDkkHDph/1tSY7a3tjTfe0JIlS0L2ZWVl6bHHHuMRBEA7xqo3oB2Li4tT165dVV1dLckcmfF4PKqrq5Pf71dJSYlcLpfWrVunurq6o1oIGwhIu3dL+fnma+tWafNmM7wYhvlyOMxXYqKUnS0NGCD162e+MjOlEHv5tcinn36qxx57LGRfp06dNGfOHHXo0OHoLg7AFggyQDu2bds2ffzxx8rMzNSOHTtUU1PTsNjX5XIpJiZGRUVFcrlc+vzzzzVo0KAWX7ugQFq+XMrNlYqKpO+zkgxDcrul2FgzvDidPwSa0lLpr3+V3n3XPNbjkbp3l0aMkIYOlTIyWv7Zdu3apcmTJ4d8yndcXJyeeuoppaent/yCAGyJIAO0Y/v27ZMkeTwede3aVQUFBQoEAjIMQzU1NaqtrVVcXJwKCwv1pz/9qdkgEwhIa9dKS5dKf/ubOeoSG2uOtKSkSEcyg+P3m+GnoEB67DFp3jzpssuk4cOl8847/CjNwYMHNWHChEbTZT82ffp0DRw4sOXFALAt1sgA7Vh2dnbD1Ernzp11/PHHKy4uTi6XSw6HoyHUSNJf/vKXw15r82bpyiulUaPMkRhJSk83R1Q6dDiyECOZx3foYJ5fP3CyfLl5/SuvNN8vlMrKSt15550Nd1791G9+8xsNGTLkyIoBYFsEGaAdS05O1tNPP90wxZKSkqLu3bsrEAgoLi5Obre74dhDhw6FvEZ1tfT009J110mffSYlJ5tTQB07Hv3alp9yOs3rZWSY1//8c/P9nn76hykryVykfM8992jbtm0hr3PJJZc0+cwpAO0TG+IBUcDv9ysvL09//etftXr1au3atath2qneSSedpM8++6xR2+bN0gMPSJs2metZUlPNdS9trX49TXW1dPrp0qxZ0oABfk2ZMkUffvhhyHNOOeUULVy4sFE4A2BfLc0eBBkgylRVVWnNmjX6/e9/r/Xr1ysQCKhnz55aunRpo3UlS5dK06ZJXq/Utau5gDfcfD6ppERKSjKUnb1c//734yGPO+644/Tiiy+qc+fOYa4QQFshyABoVlVVlfbt26fjjjtOzu/niQxDWrRImjnTXJDbrVt4RmGaYhjSrl1V8noPKC1tkTp3frNRPV26dNGiRYt03HHHWVckgFbHIwoANCs+Pl6ZmZkNIUYyQ8zvfmd+bXWIkerf39y4r7DwFu3ff3VDX3Jysp599llCDBDFCDIAGuTmmiMxTqfUpYv1Iaaey+VUTMxBORyG9u4dq9LSS+TxePSHP/xBJ5xwgtXlAbAQQQaAJHNh7/Tp5nRSpC016dKlsySHXK6DMgyn9u69XePGzdeAAQOsLg2AxQgyAFRdbd6d5PVGxnTST3k88TrhhCx16dJZ3bs7lZSUrtzcgY1uzQYQnQgyADR/vnmLddeukRdi6sXFudWtW3d1795N3bu7tGmTWTeA6EaQAaLcpk3SggXmPjF22YLF7TbrXbCg6R2AAUQHggwQxQIB6cEHpfJyc7M7O0lNNZ/1NHWq+TkARCeCDBDF1q6VtmwxF/dG6pRSUxwOM8xs2SKtW2d1NQCsQpABotjSpVJtrRQfb3UlRyc+3qw/N9fqSgBYhSADRKmCAulvf5MSE62u5NgkJkrvvWd+HgDRhyADRKnly801JnZ/6khysrnG5/XXra4EgBVirC4AQPgFAuZ0TGysuYtvKCUlC7Vv38KftDrlcqXI4zlJqak3KDHxnIaenTvHqbLyc0mSw+FR794rFBPTsaH/22+vVG3tXklSVtYyud3H/6SmGn3zzaUKBA41vFfv3v+n2Niuh/0sTqf5OV57TbrjjqY/D4D2iV95IArt3i0VFR3NtFJAfv8BVVRs0O7dd6m8fE3IowyjWqWlfzqiK1dUrPtRiDHfq7x8ZYvO7dDB/DxMLwHRhyADRKH8fHM3X4+nZcd36HCOevV6QRkZT8rt7vt9q6EDB5Y2ec6BA3+W31/e4prKyv4W1Ob1vteic+Pjzc+Tn9/itwPQThBkgChU/z98l6tlx8fEpCohIVtJSRepS5dbGtpra79r8pxAoEKlpS27nSgQqNShQx9//17dFR8/UJJUVfWlamoKmz2//nMQZIDoQ5ABotDWrZJhHPt1YmJCr1/xeE6WJB048JoCgcpmr1NevkaG4ZMkJSf/QsnJlzT0eb3BIzWhGIb5uQBEF4IMEGUMw9zW/0geR1BXV6rKys0qL/9Q+/a90NDeqdPQkMenpt4gpzNefr9XBw4sa/b6Pw4rSUmDlZT0C0mO7/taNr3kdpuPW2iNgAbAPggyQJSprTVvu46Nbfk5FRXrtXPnLSoomCSfb7tcrlSlpz+s5ORLQx7vcqWoY0cz5Ozf/0cFAr4mr+33e1VR8YkkKSamm+LjByg2tpvi4/tLkny+b+Tz/bvZGmNipIoKqa6u5Z8LgP0RZIAoU1NjjlocyyMJ/P4D8vn+edhjOnf+TzkccfL7S3XwYNObvHi978swzPSRlHSxHN8XlpQ0+EfHND+95HSat5XX1LTkEwBoLwgyQJSprT3yIJOS8kuddNJGZWbOk8PhkWRo//5XVF7+UZPnxMR0VseOv5Ik7d+/RIZRG/K4H08dHTiwVPn5Zyo//0wVF/8+5DFNcTjMz0WQAaILQQaIMrGxP/xP/0g4HC4lJp6tzp3HNLSVlDx32HM6d/61HI5Y1dUVq65uX1B/Xd0+VVZ+1ux719TsUnX114c9pj6cxcU1ezkA7Qg7+wJRJi7u6IJMvdTUEdq//xUZRrV8vu06dOgTJSb+POSxsbHdlZLy/3Tw4Bsh+73elZICkqQOHQYpMfGiRv1VVZu+P8bcZ8bjOanJugIB8zZsggwQXRiRAaJMbKy5o29t6JmeZrlcyerY8aqG70tLXzns8Z0736Sm/qr58ZRRp043KDX1+kYv81xTeflKGYdJX3V15g6/MfzzDIgqBBkgyjgcUna25Gv6RqJmpabeoPq/Pioq8lRd/Y8mj42LO04pKUOC2mtqClVVtfX7mjzq0CEn6BiPp69iY3tIMjffq6ra0uT7+HzSaacd2yJmAPbjMA73T5zveb1epaSkqKysTMl2f1QuAP3+99Ls2VJ6utWVtJ7CQmnyZGnCBKsrAdAaWpo9GJEBolC/fuaffr+1dbSW+s9R/7kARA+CDBCF+vUzHxhZXW11Ja2jqsr8PAQZIPoQZIAolJkpde9u7vDbHlRUmJ8nI8PqSgCEG0EGiEJOpzRihHnnUiBgdTXHJhAwP8cNN5ifC0B04dceiFJDh5q3YXu9VldybLxeKSlJuvZaqysBYAWCDBClMjKkyy6z//TSoUPSpZcyrQREK4IMEMWGDzc3yKuqsrqSo1NVZdY/YoTVlQCwCkEGiGLnnScNHCjt33/0jyywimFIpaVm/eeea3U1AKxCkAGimNMpzZplrjEpLbW6miNTWmqu8Xn0URb5AtGMX38gymVnS7fdZu4pcyyPLQgnn8+s97bbpFNPtboaAFYiyADQ7bebzykqKYn8KSbDkPbtk04/3awbQHQjyACQx2NO0SQnS8XFkRtmDMOsLynJnBLzeKyuCIDVCDIAJJlTTI88Irlc5uLfSLR/vxQTY9aZnW11NQAiAUEGQIPhw6UHH/xh+iZSRmZ+XM+DD5p1AoBEkAHwE2PHStOnm19HwjRT/XSSwyE99JB0883W1gMgshBkADTicJhh5oknpPh4qbDQuruZfD5pzx6zjscfN0OMw2FNLQAiE0EGQEjDh0t//rN5N9O+feHdNM8wzPfbt0864wyzDqaTAIRCkAHQpOxs6fXXpXvvNRfZFha2/eMMqqrM94mJMd93+XIW9gJoGkEGwGF5PNLdd0v/+7/m3i1er1RQIB08KAUCrfMegYB5vYIC8/qnn26+3913c4s1gMOLsboAAPaQnS29/ba0bp2Umyu99545chIbaz4qwOMxb91uKb/fHH2pqJBqa829YYYONR8Aee65PHYAQMsQZAC0mNMpnX+++SooMKedXntNKiqSysrMYwxDcrvNqSGn01ycaxjmqEtdnbmAt37BrscjZWRIN9wgXXut+TUAHAmHYTS/fM/r9SolJUVlZWVKTk4OR10AbCIQMENNfr752rJF2rzZHGkJBMwQ43CYoaZDB3Px8IABUr9+5isjg9EXAMFamj0YkQFwTJxOqWdP83XZZWabYZijLzU15isuznzFxHD7NIDWRZAB0OocDnPtTGysOQoDAG2FAV0AAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbMS05yDAMSZLX623TYgAAAKQfMkd9BmlKi4JMeXm5JCkzM/MYywIAAGi58vJypaSkNNnvMJqLOpICgYAKCwuVlJQkh8PRqgUCAAD8lGEYKi8vV3p6upzOplfCtCjIAAAARCIW+wIAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANv6/1cfA1AZsed5AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 700x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(' Drawing the default configs for')\n",
"print('- GRN')\n",
"print('- Enhancers')\n",
"print('- Binding regions')\n",
"print('- Target genes')\n",
"\n",
"config['lamb'] = hummus.config.get_grn_lamb(config, draw=True)\n",
"config['lamb'] = hummus.config.get_enhancers_lamb(config, draw=True)\n",
"config['lamb'] = hummus.config.get_binding_regions_lamb(config, draw=True)\n",
"config['lamb'] = hummus.config.get_target_genes_lamb(config, draw=True)\n",
"#config['lamb'] = hummus.config.get_max_lamb(config, draw=True) # probability matrix with all possible connexions"
]
},
{
"cell_type": "markdown",
"id": "4a7c88a9-bcd8-470d-8674-344b4d8ed83a",
"metadata": {},
"source": [
"## To add new omics, you have to: \n",
"1) Add the new multiplex\n",
"2) Add the new bipartite(s)\n",
"3) Modify eta (starting) probability\n",
"4) Modify lamb (transition) probability"
]
},
{
"cell_type": "markdown",
"id": "e9f761ec-8ec1-45d8-998e-2ac16c9e236b",
"metadata": {},
"source": [
"### 1) New multiplex"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "91e92396-2b05-4cfd-9aad-d350f3bd08c9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'RNA': {'graph_type': ['00'], 'layers': ['multiplex/RNA/GENIE3.tsv']},\n",
" 'TF': {'graph_type': ['00'], 'layers': ['multiplex/TF/PPI.tsv']},\n",
" 'peaks': {'graph_type': ['00'], 'layers': ['multiplex/peaks/Cicero.tsv']}}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['multiplex']"
]
},
{
"cell_type": "markdown",
"id": "86e103c4-54d6-4250-9b5a-5b9cbfcb1527",
"metadata": {},
"source": [
"**graph_type** is a list of strings containing two binary numbers (0 or 1) that indicates if the networks are weighted and/or directed.<br>\n",
"- The first number is directed(1) or not (0)<br>\n",
"- The second number is weighted(1) or not (0)<br>\n",
" e.g.: \"graph_type\": **'01'** is an **undirected and weighted network**\n",
"\n",
"**layers** contain the paths to the different networks composing the multiplex.\n",
"\n",
"There is usually one network only per multiplex, so list of one element each."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b627b699-9473-4a00-8896-d3a47dc12df3",
"metadata": {},
"outputs": [],
"source": [
"config['multiplex']['Proteins'] = {\n",
" 'layers': \"\",\n",
" 'graph_type': \"\"\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "de426764-bd41-4764-82cc-4b376d7fc7dc",
"metadata": {},
"source": [
"### 2) New bipartite"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fe49baeb-28c0-4e7c-9b98-5de40920c591",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'bipartite/atac_rna.tsv': {'graph_type': '00',\n",
" 'source': 'RNA',\n",
" 'target': 'peaks'},\n",
" 'bipartite/tf_rna.tsv': {'graph_type': '00',\n",
" 'source': 'peaks',\n",
" 'target': 'TF'}}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['bipartite']"
]
},
{
"cell_type": "markdown",
"id": "501183a0-7ac8-405e-bade-51e2c6feb36e",
"metadata": {},
"source": [
"A bipartite is here a single network. You can see that the key is here the path to the network directy and that it contains 3 items:<br>\n",
"- **source**: Which indicates the **multiplex** containing the source nodes (the left column)\n",
"- **target**: Which indicates the **multiplex** containing the target nodes (the right column)\n",
"- **graph_type**: Which indicates if the network is **directed/weighted**, the same way it's used in multiplexes."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ff6f5362-91b2-4d02-b1ce-9eb137253083",
"metadata": {},
"outputs": [],
"source": [
"config['bipartite'][\"rna_proteins_path\"] = {\n",
" 'source': \"RNA\",\n",
" 'target': \"Proteins\",\n",
" 'graph_type': \"00\"\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "e54c576e-ddf3-4af5-b447-8b1f8308a69a",
"metadata": {},
"source": [
"### 3) Modify eta"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f04b7a93-d609-46f3-8b01-8ce9fd431455",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RNA 0.0\n",
"TF 1.0\n",
"peaks 0.0\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['eta']"
]
},
{
"cell_type": "markdown",
"id": "4ec06417-9773-4095-aa9b-b5357cefa149",
"metadata": {},
"source": [
"**eta** is simply a pandas.Series object, that indicates wihat is **the probability to start the random walk from each of the multiplex** <br> (typically a vector of 0s with a single 1 value, for the multiplex containing the type of seeds we use (e.g.: TFs, or genes only)) <br>\n",
"**It is defined together with lamb**, depending of the regulations we want to study"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "868b1716-01fd-4b9f-9c2e-648fb85916fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TF 1\n",
"peaks 0\n",
"RNA 0\n",
"Proteins 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Here an example if we wanted to explore the multilayer from the TFs multiplex\n",
"config['eta'] = pd.Series([1, 0, 0, 0], index=['TF', 'peaks', 'RNA', 'Proteins'])\n",
"config['eta']"
]
},
{
"cell_type": "markdown",
"id": "1d2b5a0a-af64-4d2b-940d-fcb3b63b2238",
"metadata": {},
"source": [
"### 4) Modify lamb"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ff89040c-fd9c-49b0-a546-6ab9d8bbdbd5",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RNA</th>\n",
" <th>TF</th>\n",
" <th>peaks</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RNA</th>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TF</th>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peaks</th>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RNA TF peaks\n",
"RNA 0.5 0.0 0.5\n",
"TF 0.0 0.5 0.0\n",
"peaks 0.5 0.5 0.5"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['lamb']"
]
},
{
"cell_type": "markdown",
"id": "4b4414f1-2c85-4d0c-9381-108ac9ae4821",
"metadata": {},
"source": [
"**lamb** is a pandas.Dataframe object, that indicates wihat is **the probability to jump between the multiplexes** <br> (It corresponds to the **very good looking** NetworkX plots, that took me a long time to implement ^^) <br>\n",
"\n",
"**It is defined together with eta**, depending of the regulations we want to study\n",
"\n",
"#### !! lamb[i, j], corresponds to the probability to jump from **j** to **i** !\n",
"It is thus normalized per columns. You also need to have a bipartite to allows each non-zero value in lamb."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b332d3e5-24cf-4194-83ab-7e71a7f92e1b",
"metadata": {},
"outputs": [],
"source": [
"config['lamb'] = hummus.config.initialise_lamb(config, value=0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "225be310-7cd7-4ea6-aad5-8c3a4df4ffc9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>RNA</th>\n",
" <th>TF</th>\n",
" <th>peaks</th>\n",
" <th>Proteins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RNA</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TF</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peaks</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Proteins</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RNA TF peaks Proteins\n",
"RNA 0.0 0.0 0.0 0.0\n",
"TF 0.0 0.0 0.0 0.0\n",
"peaks 0.0 0.0 0.0 0.0\n",
"Proteins 0.0 0.0 0.0 0.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['lamb']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fa45d291-a3b4-446e-9f22-4970c5622327",
"metadata": {},
"outputs": [],
"source": [
"# Here we add the probability to go to each layer from another\n",
"# Lets imagine the following structure: TF --> peaks <--> RNA <--> Proteins\n",
"# If we keep all options equiprobable and without forgetting that we have a probability to STAY in the multiplex:\n",
"\n",
"config['lamb'].loc[\"TF\", \"TF\"] = 0.5 # probability to go to TF from TF\n",
"config['lamb'].loc[\"peaks\", \"TF\"] = 0.5 # probability to go to peaks from TF\n",
"\n",
"config['lamb'].loc[\"TF\", \"peaks\"] = 1/3 # probability to go to TF FROM peaks\n",
"config['lamb'].loc[\"peaks\", \"peaks\"] = 1/3 \n",
"config['lamb'].loc[\"RNA\", \"peaks\"] = 1/3 \n",
"\n",
"config['lamb'].loc[\"peaks\", \"RNA\"] = 1/3 # probability to go to peaks FROM RNA\n",
"config['lamb'].loc[\"RNA\", \"RNA\"] = 1/3 \n",
"config['lamb'].loc[\"Proteins\", \"RNA\"] = 1/3 \n",
"\n",
"config['lamb'].loc[\"RNA\", \"Proteins\"] = 1/2\n",
"config['lamb'].loc[\"Proteins\", \"Proteins\"] = 1/2\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "cd3ff3c1-d80a-40b3-be17-55b0a0a76219",
"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>RNA</th>\n",
" <th>TF</th>\n",
" <th>peaks</th>\n",
" <th>Proteins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RNA</th>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TF</th>\n",
" <td>0.000000</td>\n",
" <td>0.5</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>peaks</th>\n",
" <td>0.333333</td>\n",
" <td>0.5</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Proteins</th>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RNA TF peaks Proteins\n",
"RNA 0.333333 0.0 0.333333 0.5\n",
"TF 0.000000 0.5 0.333333 0.0\n",
"peaks 0.333333 0.5 0.333333 0.0\n",
"Proteins 0.333333 0.0 0.000000 0.5"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config['lamb']"
]
},
{
"cell_type": "markdown",
"id": "183c300d-affa-449d-993b-b5dae8196ae0",
"metadata": {},
"source": [
"#### Let's check the whole config after all these modifications !"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3f1f3bf6-68d4-428e-b5f9-c2dfdc4252d0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'bipartite': {'bipartite/atac_rna.tsv': {'graph_type': '00',\n",
" 'source': 'RNA',\n",
" 'target': 'peaks'},\n",
" 'bipartite/tf_rna.tsv': {'graph_type': '00',\n",
" 'source': 'peaks',\n",
" 'target': 'TF'},\n",
" 'rna_proteins_path': {'source': 'RNA',\n",
" 'target': 'Proteins',\n",
" 'graph_type': '00'}},\n",
" 'eta': TF 1\n",
" peaks 0\n",
" RNA 0\n",
" Proteins 0\n",
" dtype: int64,\n",
" 'lamb': RNA TF peaks Proteins\n",
" RNA 0.333333 0.0 0.333333 0.5\n",
" TF 0.000000 0.5 0.333333 0.0\n",
" peaks 0.333333 0.5 0.333333 0.0\n",
" Proteins 0.333333 0.0 0.000000 0.5,\n",
" 'multiplex': {'RNA': {'graph_type': ['00'],\n",
" 'layers': ['multiplex/RNA/GENIE3.tsv']},\n",
" 'TF': {'graph_type': ['00'], 'layers': ['multiplex/TF/PPI.tsv']},\n",
" 'peaks': {'graph_type': ['00'], 'layers': ['multiplex/peaks/Cicero.tsv']},\n",
" 'Proteins': {'layers': '', 'graph_type': ''}},\n",
" 'r': '0.7',\n",
" 'seed': 'seeds/seeds.txt',\n",
" 'self_loops': '0'}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config"
]
},
{
"cell_type": "markdown",
"id": "6455ddc6-7e61-4610-b38c-24b1f8f5d2a1",
"metadata": {},
"source": [
"### Save the config file"
]
},
{
"cell_type": "markdown",
"id": "675257e2-86fe-44e2-9162-c7f9b214fa9c",
"metadata": {},
"source": [
"We can now save the config file and explore the mutlilayer with this configuration. Usually, I save it in the config folder, itself located in the multilayer_folder (folder containing all the networks, config, etc)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "161bddad-97f1-42a8-987b-16080c57537f",
"metadata": {},
"outputs": [],
"source": [
"hummus.config.save_config(config, \"../HuMMuS/hummus_package/vignettes/test_multiplex_genes/config/extended_config.yaml\")"
]
},
{
"cell_type": "markdown",
"id": "0b4f31eb-4281-4d05-8c04-4f4065702607",
"metadata": {},
"source": [
"## Explore the multilayer"
]
},
{
"cell_type": "markdown",
"id": "4242465a-ae15-47dc-8b91-b0adc56cc1a5",
"metadata": {},
"source": [
"We will now use the second module of hummuspy : **hummuspy.explore_network** <br>\n",
"The most general functions here are **compute_RandomWalk** and **compute_multiple_RandomWalk**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48cececf-70aa-475c-be40-1157a9c42d8c",
"metadata": {},
"outputs": [],
"source": [
"mutlilayer_f = \"../HuMMuS/hummus_package/vignettes/test_multiplex_genes/\" # the general folder all the files\n",
"config_name = \"extended_config.yaml\" # the beautiful name you chose for your config\n",
"list_seeds = [] # Seeds from which we start the exploration. Generally all the nodes from the startin layer, indicated in 'eta' (e.g.: TFs) \n",
"\n",
"df = compute_multiple_RandomWalk(\n",
" multilayer_f,\n",
" config_name,\n",
" list_seeds,\n",
" config_folder='config',\n",
" save=False, # Do we want to save the results on disk\n",
" output_f=None, # Name of the result file IF save on disk\n",
" return_df=True, # return in console the results\n",
" spec_layer_result_saved='all', # Specify here if you want to keep only the scores of going to one of the layer \n",
" # (e.g.: If you're interested only in TF-genes interactions and not in peaks-related scores, put ['RNA']\n",
" njobs=1 # How many cores do you wanna use ?\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Datasets
Models
