run_sergio.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from SERGIO.sergio import sergio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate Clean Data _ Steady-State Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sim = sergio(number_genes=100, number_bins = 9, number_sc = 300, noise_params = 1, decays=0.8, sampling_state=15, noise_type='dpd')\n",
    "sim.build_graph(input_file_taregts ='data_sets/De-noised_100G_9T_300cPerT_4_DS1/Interaction_cID_4.txt', input_file_regs='data_sets/De-noised_100G_9T_300cPerT_4_DS1/Regs_cID_4.txt', shared_coop_state=2)\n",
    "sim.simulate()\n",
    "expr = sim.getExpressions()\n",
    "expr_clean = np.concatenate(expr, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Add Technical Noise _ Steady-State Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Add outlier genes\n",
    "\"\"\"\n",
    "expr_O = sim.outlier_effect(expr, outlier_prob = 0.01, mean = 0.8, scale = 1)\n",
    "\n",
    "\"\"\"\n",
    "Add Library Size Effect\n",
    "\"\"\"\n",
    "libFactor, expr_O_L = sim.lib_size_effect(expr_O, mean = 4.6, scale = 0.4)\n",
    "\n",
    "\"\"\"\n",
    "Add Dropouts\n",
    "\"\"\"\n",
    "binary_ind = sim.dropout_indicator(expr_O_L, shape = 6.5, percentile = 82)\n",
    "expr_O_L_D = np.multiply(binary_ind, expr_O_L)\n",
    "\n",
    "\"\"\"\n",
    "Convert to UMI count\n",
    "\"\"\"\n",
    "count_matrix = sim.convert_to_UMIcounts(expr_O_L_D)\n",
    "\n",
    "\"\"\"\n",
    "Make a 2d gene expression matrix\n",
    "\"\"\"\n",
    "count_matrix = np.concatenate(count_matrix, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate Clean Data _ differentiation Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data_sets/De-noised_100G_6T_300cPerT_dynamics_7_DS6/bMat_cID7.tab', sep='\\t', header=None, index_col=None)\n",
    "bMat = df.values\n",
    "\n",
    "sim = sergio(number_genes=100, number_bins = 6, number_sc = 300, noise_params = 0.2, decays=0.8, sampling_state = 1, noise_params_splice = 0.07, noise_type='dpd', dynamics=True, bifurcation_matrix= bMat)\n",
    "sim.build_graph(input_file_taregts ='data_sets/De-noised_100G_6T_300cPerT_dynamics_7_DS6/Interaction_cID_7.txt', input_file_regs='data_sets/De-noised_100G_6T_300cPerT_dynamics_7_DS6/Regs_cID_7.txt', shared_coop_state=2)\n",
    "sim.simulate_dynamics()\n",
    "exprU, exprS = sim.getExpressions_dynamics()\n",
    "exprU_clean = np.concatenate(exprU, axis = 1)\n",
    "exprS_clean = np.concatenate(exprS, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Technical Noise _ differentiation Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Add outlier genes\n",
    "\"\"\"\n",
    "exprU_O, exprS_O = sim.outlier_effect_dynamics(exprU, exprS, outlier_prob = 0.01, mean = 0.8, scale = 1)\n",
    "\n",
    "\"\"\"\n",
    "Add Library Size Effect\n",
    "\"\"\"\n",
    "libFactor, exprU_O_L, exprS_O_L = sim.lib_size_effect_dynamics(exprU_O, exprS_O, mean = 4.6, scale = 0.4)\n",
    "\n",
    "\"\"\"\n",
    "Add Dropouts\n",
    "\"\"\"\n",
    "binary_indU, binary_indS = sim.dropout_indicator_dynamics(exprU_O_L, exprS_O_L, shape = 6.5, percentile = 82)\n",
    "exprU_O_L_D = np.multiply(binary_indU, exprU_O_L)\n",
    "exprS_O_L_D = np.multiply(binary_indS, exprS_O_L)\n",
    "\n",
    "\"\"\"\n",
    "Convert to UMI count\n",
    "\"\"\"\n",
    "count_matrix_U, count_matrix_S = sim.convert_to_UMIcounts_dynamics(exprU_O_L_D, exprS_O_L_D)\n",
    "\n",
    "\"\"\"\n",
    "Make 2d spliced and unspliced expression matrices\n",
    "\"\"\"\n",
    "count_matrix_U = np.concatenate(count_matrix_U, axis = 1)\n",
    "count_matrix_S = np.concatenate(count_matrix_S, axis = 1)"
   ]
  }
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