NASC_seq_Jurkat_T_cell_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Scribe as sc\n",
    "from Scribe.Scribe import causal_net_dynamics_coupling as Scribe\n",
    "\n",
    "import scanpy as sp\n",
    "from anndata import AnnData\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "import networkx as nx \n",
    "nx.__version__\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(filename, fillna=False):\n",
    "    X = pd.read_csv(filename, index_col=0, delimiter=',')\n",
    "    if fillna: X.fillna(0, inplace=True)\n",
    "    split_array = [X.columns.str.split('_', n=5)[i] for i in range(len(X.columns))]\n",
    "    obs = pd.DataFrame(split_array, columns=['Plate', 'Well', 'Exp', 'CellType', 'Time', 'Treatment'])\n",
    "    obs['cell_id'] = X.columns\n",
    "    obs = obs.set_index('cell_id')\n",
    "#     print(X.index[:10])\n",
    "    \n",
    "    var = pd.DataFrame(X.index, columns=['Ensemble_id'])\n",
    "    return X.values.T, obs, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Transforming to str index.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Transforming to str index.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Transforming to str index.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ENSG00000223972', 'ENSG00000227232', 'ENSG00000278267',\n",
      "       'ENSG00000243485', 'ENSG00000284332', 'ENSG00000237613',\n",
      "       'ENSG00000268020', 'ENSG00000240361', 'ENSG00000186092',\n",
      "       'ENSG00000238009'],\n",
      "      dtype='object')\n",
      "AnnData object with n_obs × n_vars = 576 × 58428 \n",
      "    obs: 'CellType', 'Exp', 'Plate', 'Time', 'Treatment', 'Well', 'batch'\n",
      "    var: 'Ensemble_id'\n",
      "    layers: 'unspliced', 'spliced'\n"
     ]
    }
   ],
   "source": [
    "directory = '/home/xqiu/proj/Aristotle/scSLAM_seq_data/NASC_seq'\n",
    "adata = []\n",
    "for i in [2, 3, 4]:\n",
    "    N, obs, var = read_data(\"%s/GSE128273_exp%d_newcounts.csv\"%(directory, i))\n",
    "    O, _, _ = read_data(\"%s/GSE128273_exp%d_oldcounts.csv\"%(directory, i))\n",
    "    R, _, _ = read_data(\"%s/GSE128273_exp%d_readcounts.csv\"%(directory, i))\n",
    "    adata.append(AnnData(R, obs=obs, var=var, layers={'unspliced': N, 'spliced': O}))\n",
    "adata = AnnData.concatenate(*adata)\n",
    "print(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mygene\n",
    "mg = mygene.MyGeneInfo()\n",
    "geneList = adata.var.Ensemble_id\n",
    "geneSyms = mg.querymany(geneList , scopes='ensembl.gene', fields='symbol', species='human')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ensemble_id = [cur_dict['query'] for cur_dict in geneSyms]\n",
    "gene = [cur_dict['symbol'] if 'notfound' not in cur_dict.keys() else 'NA' for cur_dict in geneSyms]\n",
    "FD = pd.DataFrame({'Ensemble_id': Ensemble_id, \"gene_short_name\": gene}, columns=['Ensemble_id', 'gene_short_name'])\n",
    "FD = FD.set_index('Ensemble_id')\n",
    "\n",
    "tmp = FD.loc[adata.var.Ensemble_id, :]\n",
    "tmp = tmp.iloc[np.where(~ FD.index.duplicated())[0], :]\n",
    "adata.var = tmp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>gene_short_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensemble_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000120738</th>\n",
       "      <td>EGR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000170345</th>\n",
       "      <td>FOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gene_short_name\n",
       "Ensemble_id                    \n",
       "ENSG00000120738            EGR1\n",
       "ENSG00000170345             FOS"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var[adata.var.gene_short_name.isin(['EGR1','FOS'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xqiu/.local/lib/python3.6/site-packages/Scribe/causal_network.py:18: UserWarning:  WARNING: No expression data argument given. if you intend to load the data from a file, call the method 'read_expression_file'.\n",
      "  if expression==None: warnings.warn(\" WARNING: No expression data argument given. if you intend to load the data from a file, call the method 'read_expression_file'.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create causal_model successfully\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.viz_response(adata, np.array([['ENSG00000120738','ENSG00000170345']]),grid_num=25, log=True) # \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create causal_model successfully\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xqiu/.local/lib/python3.6/site-packages/Scribe/plot/heatmaps.py:394: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = np.exp(-dist_mat[i, 1:] / np.min(dist_mat[i, 1:]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f_ini_ind is  0   25\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.viz_causality(adata,np.array([['ENSG00000120738','ENSG00000170345']]),grid_num=25, log=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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_short_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensemble_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000158050</th>\n",
       "      <td>DUSP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000120738</th>\n",
       "      <td>EGR1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000170345</th>\n",
       "      <td>FOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gene_short_name\n",
       "Ensemble_id                    \n",
       "ENSG00000158050           DUSP2\n",
       "ENSG00000120738            EGR1\n",
       "ENSG00000170345             FOS"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var[adata.var.gene_short_name.isin(['EGR1','FOS', 'DUSP2'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create causal_model successfully\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xqiu/.local/lib/python3.6/site-packages/Scribe/plot/heatmaps.py:551: RuntimeWarning: invalid value encountered in true_divide\n",
      "  u = np.exp(-dist_mat[i, 1:] / np.min(dist_mat[i, 1:]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.viz_comb_logic(adata,np.array([['ENSG00000120738','ENSG00000170345', 'ENSG00000158050']]),grid_num=10, log=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xqiu/.local/lib/python3.6/site-packages/Scribe/information_estimators.py:171: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  information_samples[i] += -digamma(len(tree_yz.query_ball_point(data_yz[i], knn_dis[i], p=np.inf)) - 1)\n",
      "/home/xqiu/.local/lib/python3.6/site-packages/Scribe/information_estimators.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  information_samples[i] += -digamma(len(tree_xz.query_ball_point(data_xz[i], knn_dis[i], p=np.inf)) - 1)\n",
      "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:75: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims)\n"
     ]
    }
   ],
   "source": [
    "sp.pp.recipe_zheng17(adata, n_top_genes=25) #, min_counts=15, min_counts_u=10, n_top_genes=2500 n_top_genes can be tuned \n",
    "Scribe(adata, t0_key='spliced', t1_key='unspliced')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>ENSG00000198691</th>\n",
       "      <th>ENSG00000115919</th>\n",
       "      <th>ENSG00000154743</th>\n",
       "      <th>ENSG00000053108</th>\n",
       "      <th>ENSG00000101812</th>\n",
       "      <th>ENSG00000261026</th>\n",
       "      <th>ENSG00000236521</th>\n",
       "      <th>ENSG00000230417</th>\n",
       "      <th>ENSG00000255920</th>\n",
       "      <th>ENSG00000214851</th>\n",
       "      <th>...</th>\n",
       "      <th>ENSG00000231870</th>\n",
       "      <th>ENSG00000275302</th>\n",
       "      <th>ENSG00000266928</th>\n",
       "      <th>ENSG00000210082</th>\n",
       "      <th>gERCC.00002</th>\n",
       "      <th>gERCC.00043</th>\n",
       "      <th>gERCC.00092</th>\n",
       "      <th>gERCC.00136</th>\n",
       "      <th>gERCC.00145</th>\n",
       "      <th>gERCC.00170</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000198691</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020791</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inf</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.548182e-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004311</td>\n",
       "      <td>0.052951</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.302689</td>\n",
       "      <td>0.064591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000115919</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.257204e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.180066e-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036033</td>\n",
       "      <td>0.067136</td>\n",
       "      <td>-0.027034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300790</td>\n",
       "      <td>0.018590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000154743</th>\n",
       "      <td>0.019651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012259</td>\n",
       "      <td>0.002554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.000891</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ENSG00000198691  ENSG00000115919  ENSG00000154743  \\\n",
       "ENSG00000198691              NaN         0.020791              NaN   \n",
       "ENSG00000115919              NaN              NaN         0.000709   \n",
       "ENSG00000154743         0.019651              NaN              NaN   \n",
       "ENSG00000053108         0.014821         0.032511              NaN   \n",
       "ENSG00000101812              NaN         0.019407              NaN   \n",
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       "ENSG00000230417        -0.002121         0.001936              NaN   \n",
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       "ENSG00000214851              NaN              NaN              NaN   \n",
       "ENSG00000274012              NaN              NaN              NaN   \n",
       "ENSG00000184984              NaN        -0.001259         0.065801   \n",
       "ENSG00000109047         0.013792         0.019858         0.075646   \n",
       "ENSG00000239203         0.014317         0.069366         0.074483   \n",
       "ENSG00000231870              NaN              NaN              NaN   \n",
       "ENSG00000275302              NaN         0.000922        -0.000763   \n",
       "ENSG00000266928         0.000002         0.000041              NaN   \n",
       "ENSG00000210082              NaN              NaN         0.267152   \n",
       "gERCC.00002                  NaN              NaN              NaN   \n",
       "gERCC.00043                  NaN              NaN              NaN   \n",
       "gERCC.00092            -0.033698             -inf         0.255052   \n",
       "gERCC.00136             0.017113             -inf              NaN   \n",
       "gERCC.00145                  NaN              NaN              NaN   \n",
       "gERCC.00170                  NaN              NaN              NaN   \n",
       "\n",
       "                 ENSG00000053108  ENSG00000101812  ENSG00000261026  \\\n",
       "ENSG00000198691              NaN         0.000715              NaN   \n",
       "ENSG00000115919              NaN              NaN              NaN   \n",
       "ENSG00000154743         0.012259         0.002554              NaN   \n",
       "ENSG00000053108              NaN         0.000966              NaN   \n",
       "ENSG00000101812              NaN              NaN              NaN   \n",
       "ENSG00000261026              NaN              NaN              NaN   \n",
       "ENSG00000236521         0.000006              NaN              NaN   \n",
       "ENSG00000230417         0.000378         0.000099              NaN   \n",
       "ENSG00000255920              NaN         0.001162              NaN   \n",
       "ENSG00000214851         0.030864              NaN              NaN   \n",
       "ENSG00000274012              NaN              NaN              NaN   \n",
       "ENSG00000184984              NaN              NaN              NaN   \n",
       "ENSG00000109047         0.028652         0.001223              NaN   \n",
       "ENSG00000239203         0.029716         0.001160              inf   \n",
       "ENSG00000231870              NaN              NaN              NaN   \n",
       "ENSG00000275302              NaN              NaN              NaN   \n",
       "ENSG00000266928         0.000041         0.000013              NaN   \n",
       "ENSG00000210082         0.296134              NaN              NaN   \n",
       "gERCC.00002                  NaN              NaN              NaN   \n",
       "gERCC.00043             0.017448              NaN              NaN   \n",
       "gERCC.00092             0.043023         0.005656              NaN   \n",
       "gERCC.00136                 -inf         0.001470              NaN   \n",
       "gERCC.00145                  NaN              NaN              NaN   \n",
       "gERCC.00170                  NaN         0.006507              NaN   \n",
       "\n",
       "                 ENSG00000236521  ENSG00000230417  ENSG00000255920  \\\n",
       "ENSG00000198691              inf              NaN              NaN   \n",
       "ENSG00000115919         0.022573              NaN              NaN   \n",
       "ENSG00000154743              inf         0.000891              NaN   \n",
       "ENSG00000053108              NaN              NaN    -1.270485e-03   \n",
       "ENSG00000101812              inf              NaN              NaN   \n",
       "ENSG00000261026              inf              NaN              NaN   \n",
       "ENSG00000236521              NaN         0.000011    -4.512816e-05   \n",
       "ENSG00000230417              NaN              NaN              NaN   \n",
       "ENSG00000255920        -0.000045         0.001699              NaN   \n",
       "ENSG00000214851        -0.000018        -0.001153    -2.997941e-05   \n",
       "ENSG00000274012              inf              NaN              inf   \n",
       "ENSG00000184984             -inf              NaN     1.697690e-03   \n",
       "ENSG00000109047              NaN              NaN     1.203511e-06   \n",
       "ENSG00000239203        -0.000009         0.000004    -1.504318e-05   \n",
       "ENSG00000231870             -inf              NaN     5.321209e-07   \n",
       "ENSG00000275302              NaN         0.000001              NaN   \n",
       "ENSG00000266928              NaN         0.000025     2.390166e-07   \n",
       "ENSG00000210082              inf              NaN     6.209772e-01   \n",
       "gERCC.00002                  inf              NaN              NaN   \n",
       "gERCC.00043                  inf              NaN              NaN   \n",
       "gERCC.00092                  inf         0.005781              NaN   \n",
       "gERCC.00136             0.061204        -0.000374     2.316927e-03   \n",
       "gERCC.00145                  inf              NaN              NaN   \n",
       "gERCC.00170                  inf              NaN              NaN   \n",
       "\n",
       "                 ENSG00000214851  ...  ENSG00000231870  ENSG00000275302  \\\n",
       "ENSG00000198691              inf  ...              NaN     1.548182e-04   \n",
       "ENSG00000115919     2.257204e-02  ...    -6.180066e-04              NaN   \n",
       "ENSG00000154743              inf  ...     9.794088e-04              NaN   \n",
       "ENSG00000053108              NaN  ...    -6.111247e-04     2.586374e-04   \n",
       "ENSG00000101812              inf  ...              NaN    -8.183052e-06   \n",
       "ENSG00000261026              NaN  ...              NaN              NaN   \n",
       "ENSG00000236521    -1.805193e-05  ...             -inf     1.723874e-07   \n",
       "ENSG00000230417              NaN  ...     6.285138e-05              NaN   \n",
       "ENSG00000255920    -2.997941e-05  ...     5.321209e-07     8.806282e-04   \n",
       "ENSG00000214851              NaN  ...     1.800790e-07     8.681794e-04   \n",
       "ENSG00000274012              inf  ...              inf              NaN   \n",
       "ENSG00000184984             -inf  ...     1.715226e-03              NaN   \n",
       "ENSG00000109047              NaN  ...     3.091085e-05     8.990590e-04   \n",
       "ENSG00000239203    -6.006866e-06  ...     7.402827e-08     8.680986e-04   \n",
       "ENSG00000231870              NaN  ...              NaN              NaN   \n",
       "ENSG00000275302              NaN  ...     1.231016e-05              NaN   \n",
       "ENSG00000266928     7.930678e-08  ...     6.160201e-06              NaN   \n",
       "ENSG00000210082              NaN  ...              NaN              NaN   \n",
       "gERCC.00002                  inf  ...              NaN              NaN   \n",
       "gERCC.00043                  inf  ...              NaN              NaN   \n",
       "gERCC.00092                  inf  ...              NaN    -1.895486e-04   \n",
       "gERCC.00136                  NaN  ...     1.555993e-03    -5.719041e-05   \n",
       "gERCC.00145                  inf  ...              NaN              NaN   \n",
       "gERCC.00170                  inf  ...              NaN              NaN   \n",
       "\n",
       "                 ENSG00000266928  ENSG00000210082  gERCC.00002  gERCC.00043  \\\n",
       "ENSG00000198691              NaN         0.004311     0.052951          inf   \n",
       "ENSG00000115919              NaN         0.036033     0.067136    -0.027034   \n",
       "ENSG00000154743              0.0              NaN     0.132055    -0.083623   \n",
       "ENSG00000053108              NaN              NaN     0.021976          NaN   \n",
       "ENSG00000101812              NaN         0.012068     0.149963          inf   \n",
       "ENSG00000261026              NaN         0.362022     0.620648          inf   \n",
       "ENSG00000236521              0.0              NaN          NaN          NaN   \n",
       "ENSG00000230417              NaN         0.012068     0.015635          inf   \n",
       "ENSG00000255920              NaN              NaN     0.617652     0.001689   \n",
       "ENSG00000214851              NaN         0.003279          NaN          NaN   \n",
       "ENSG00000274012              NaN         2.038017     2.471263          NaN   \n",
       "ENSG00000184984              NaN         0.358483     0.614240          NaN   \n",
       "ENSG00000109047              NaN         0.360624     0.618928          NaN   \n",
       "ENSG00000239203              NaN         0.359815     0.616389          inf   \n",
       "ENSG00000231870              NaN         0.360050     0.615215    -0.000919   \n",
       "ENSG00000275302              0.0         0.003294     0.003444    -0.001054   \n",
       "ENSG00000266928              NaN         0.023390          NaN          NaN   \n",
       "ENSG00000210082              NaN              NaN          NaN          NaN   \n",
       "gERCC.00002                  0.0         2.432977          NaN          NaN   \n",
       "gERCC.00043                  0.0              NaN          NaN          NaN   \n",
       "gERCC.00092                  NaN              NaN     1.948602          NaN   \n",
       "gERCC.00136                  NaN              NaN     0.770506         -inf   \n",
       "gERCC.00145                  NaN              NaN          NaN          inf   \n",
       "gERCC.00170                  NaN              NaN          NaN          NaN   \n",
       "\n",
       "                 gERCC.00092  gERCC.00136  gERCC.00145  gERCC.00170  \n",
       "ENSG00000198691          NaN          NaN     0.302689     0.064591  \n",
       "ENSG00000115919          NaN          NaN     0.300790     0.018590  \n",
       "ENSG00000154743          NaN          NaN     0.153946     0.123731  \n",
       "ENSG00000053108          NaN          NaN     0.305708     0.030424  \n",
       "ENSG00000101812          NaN          NaN     0.297953          NaN  \n",
       "ENSG00000261026          NaN          NaN     0.566561     0.619008  \n",
       "ENSG00000236521          NaN          NaN          NaN          NaN  \n",
       "ENSG00000230417          NaN          NaN     0.301979     0.015827  \n",
       "ENSG00000255920          NaN          NaN     0.299555     0.619795  \n",
       "ENSG00000214851          NaN          inf          NaN          NaN  \n",
       "ENSG00000274012          NaN          NaN     2.130371     2.436804  \n",
       "ENSG00000184984          NaN          NaN     0.298836     0.618498  \n",
       "ENSG00000109047          NaN          NaN     0.298098     0.620217  \n",
       "ENSG00000239203          inf          inf     0.295477     0.618919  \n",
       "ENSG00000231870          NaN          NaN     0.298216     0.617925  \n",
       "ENSG00000275302          NaN          NaN     0.297068     0.003297  \n",
       "ENSG00000266928          inf          inf     0.604635     0.001485  \n",
       "ENSG00000210082          inf          NaN     2.091098     2.586495  \n",
       "gERCC.00002              NaN          NaN     2.599239     3.215007  \n",
       "gERCC.00043         0.249141          NaN          NaN     0.656263  \n",
       "gERCC.00092              NaN          NaN     1.092780     1.922442  \n",
       "gERCC.00136              NaN          NaN     1.276049     0.005058  \n",
       "gERCC.00145              NaN          NaN          NaN     2.599914  \n",
       "gERCC.00170              NaN          NaN          NaN          NaN  \n",
       "\n",
       "[24 rows x 24 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.uns['causal_net']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in subtract\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in less\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in subtract\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in less\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "df_mat = adata.uns['causal_net']\n",
    "ind_mat = np.where(df_mat.values - df_mat.T.values < 0)\n",
    "\n",
    "tmp = np.where(df_mat.values - df_mat.T.values < 0)\n",
    "\n",
    "for i in range(len(tmp[0])): \n",
    "    df_mat.iloc[tmp[0][i], tmp[1][i]] = np.nan\n",
    "\n",
    "df_mat = df_mat.stack().reset_index()\n",
    "df_mat.columns = ['source', 'target', 'weight']\n",
    "# df_mat = df_mat.iloc[df_mat['weight'] is not np.nan, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000198691</td>\n",
       "      <td>ENSG00000115919</td>\n",
       "      <td>0.020791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000198691</td>\n",
       "      <td>ENSG00000101812</td>\n",
       "      <td>0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000198691</td>\n",
       "      <td>ENSG00000236521</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000198691</td>\n",
       "      <td>ENSG00000214851</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000198691</td>\n",
       "      <td>ENSG00000274012</td>\n",
       "      <td>0.254174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            source           target    weight\n",
       "0  ENSG00000198691  ENSG00000115919  0.020791\n",
       "1  ENSG00000198691  ENSG00000101812  0.000715\n",
       "2  ENSG00000198691  ENSG00000236521       inf\n",
       "3  ENSG00000198691  ENSG00000214851       inf\n",
       "4  ENSG00000198691  ENSG00000274012  0.254174"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.020791127060745845,\n",
       " 0.0007151298207218018,\n",
       " 0.2541740435463706,\n",
       " 0.0001548182254552126,\n",
       " 0.004311273985621211,\n",
       " 0.06459131122399316,\n",
       " 0.0007092011558891739,\n",
       " 0.02257303111067522,\n",
       " 0.022572042173564153,\n",
       " 0.2466700259544954]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mat.source = FD.loc[df_mat.source, 'gene_short_name'].tolist()\n",
    "df_mat.target = FD.loc[df_mat.target, 'gene_short_name'].tolist()\n",
    "\n",
    "df_mat = df_mat.iloc[~np.isinf(df_mat.weight.tolist()), :]\n",
    "\n",
    "G = nx.from_pandas_edgelist(df_mat, source='source', target='target', edge_attr='weight', create_using=nx.DiGraph())\n",
    "G.nodes()\n",
    "W = []\n",
    "for n, nbrs in G.adj.items():\n",
    "    for nbr, eattr in nbrs.items():\n",
    "        W.append(eattr['weight'])\n",
    "        \n",
    "W[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " \n",
    "# Build a dataframe with your connections\n",
    " \n",
    "# The order of the node for networkX is the following order:\n",
    "G.nodes()\n",
    "# Thus, we cannot give directly the 'myvalue' column to netowrkX, we need to arrange the order!\n",
    " \n",
    "# Here is the tricky part: I need to reorder carac, to assign the good color to each node\n",
    "# df=df.reindex(G.nodes())\n",
    " \n",
    "# Plot it, providing a continuous color scale with cmap:\n",
    "# df_mat['weight'].tolist()\n",
    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=100, edge_color=W, width=1.0, edge_cmap=plt.cm.Blues)"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}