Generating Synthetic Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "0bce85fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run synthetic_fit_funcs.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6238d03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4844b95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unweight(net,thr):\n",
    "    return np.sign(net)*(abs(net) >= thr)\n",
    "\n",
    "def topQnet(net_o,q=0.9):\n",
    "    net = net_o.copy()\n",
    "    if isinstance(net,pd.DataFrame):\n",
    "        net = net.values\n",
    "    qstr = np.quantile(np.abs(np.tril(net,k=-1).flatten()),q)\n",
    "    net = net - np.diag(np.diag(net))\n",
    "    return unweight(net,qstr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eed8ac",
   "metadata": {},
   "source": [
    "# Generating Synthetic Data From Log-Normal Distribution with Error-Free Simulated Reads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4bf9ff",
   "metadata": {},
   "source": [
    "We start with setting up our parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f50cdabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "network_methods = [\"Log Covariance\",\"CLR Mixed\",\"CLR Split\",\"SparCC\",\"GLASSO Mixed\",\"GLASSO Split\"]\n",
    "\n",
    "ns = 2 # 2 for data split in half. Can also split more times...\n",
    "gsp = 0.05 #approximate graph sparsity desired\n",
    "\n",
    "grmod = \"PL\"#power-law clustering graph\n",
    "\n",
    "\n",
    "mean_spread = 4 #for choosing the mean of the log-data higher for larger spread (e.g. more rare & dominant taxa)\n",
    "mean_center = 0 #to center things around 1\n",
    "\n",
    "min_var = 0.3 #minimum variance of log-abundance for each taxa\n",
    "max_var = 2 #maximum variance of log-abundance for each taxa\n",
    "\n",
    "simulate_reads_tasks = 6 #how many cores to use for parallel computing\n",
    "\n",
    "avgdpth = 100000 #average read depth\n",
    "stddpth = 10000 #standard deviation in read depth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41ee136",
   "metadata": {},
   "source": [
    "Including number of taxa and number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8a52ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = 150 ###Samples\n",
    "N = 500 ###Taxa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "70090be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_adjust = {\"Kingdoms\":(0,1),\"SparsityRatio\":0.1}\n",
    "bias_blocks = [(0,1,-1)] #cross kingdom interactions more positive\n",
    "bias_str = 0.9 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396276fd",
   "metadata": {},
   "source": [
    "## A function to do it all:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29dc89e",
   "metadata": {},
   "source": [
    "The ```generate_synthetic_data``` function creates the synthetic data set that can be fed into network algorithms such as SparCC or GLASSO (one can use SpiecEasi: https://github.com/zdk123/SpiecEasi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "19c7880e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run synthetic_fit_funcs.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9caba93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█         | 35/317 [00:00<00:00, 343.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[create_groundtruth_covariance] Generating cholesky decomposition\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 317/317 [00:03<00:00, 83.52it/s] \n",
      "100%|██████████| 183/183 [00:11<00:00, 15.42it/s]\n",
      "  2%|▏         | 3/150 [00:00<00:06, 21.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[generate_synthetic_samples] Drawing 150 Samples for 500 taxa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 102/150 [00:04<00:02, 21.23it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-88-3da662dd4fd5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;34m'truth_bias_structure'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbias_blocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m }\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0msynthetic_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_synthetic_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Documents/transkingdom/synthetic_transkingdom_data/synthetic_fit_funcs.py\u001b[0m in \u001b[0;36mgenerate_synthetic_data\u001b[0;34m(num_taxa, num_samples, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovar_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mabsolute_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcovar_matrix_done\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_synthetic_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcovar_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmean_log_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmean_lr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmean_log_center\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmean_lc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/transkingdom/synthetic_transkingdom_data/synthetic_fit_funcs.py\u001b[0m in \u001b[0;36mgenerate_synthetic_samples\u001b[0;34m(NumSmp, covar, mean_log_range, mean_log_center)\u001b[0m\n\u001b[1;32m    372\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[generate_synthetic_samples] Drawing {} Samples for {} taxa\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNumSmp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNumSmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0msynthetic_truth\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"S{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrvs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msynthetic_truth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/stats/_multivariate.py\u001b[0m in \u001b[0;36mrvs\u001b[0;34m(self, size, random_state)\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrvs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 771\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrvs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mentropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/stats/_multivariate.py\u001b[0m in \u001b[0;36mrvs\u001b[0;34m(self, mean, cov, size, random_state)\u001b[0m\n\u001b[1;32m    664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultivariate_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_squeeze_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.multivariate_normal\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msvd\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv, hermitian)\u001b[0m\n\u001b[1;32m   1658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m         \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->DdD'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->ddd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1660\u001b[0;31m         \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1661\u001b[0m         \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1662\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_realType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "kwargs = {\n",
    "    'max_variance':max_var,\n",
    "    'min_variance':min_var,\n",
    "    'data_types':ns,\n",
    "    'graph_model':grmod,\n",
    "    'sparsity':gsp,\n",
    "    'mean_log_range':mean_spread,\n",
    "    'mean_log_center':mean_center,\n",
    "    'simulate_reads_tasks':simulate_reads_tasks,\n",
    "    'average_read_depth':avgdpth, \n",
    "    'std_of_read_depth':stddpth,\n",
    "    'truth_sparsity_adjustment':sparse_adjust,\n",
    "    'truth_bias_strength':bias_str,\n",
    "    'truth_bias_structure':bias_blocks\n",
    "}\n",
    "synthetic_data = generate_synthetic_data(N,M,**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a6f7fe",
   "metadata": {},
   "source": [
    "### What does it give us?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46b1a3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['GroundTruthCovariance', 'GraphSparsity', 'AbsoluteSamples', 'CombinedReads', 'SeparatedReads', 'GroupOrder', 'ReadDepths', 'TotalRatioVariance', 'DataSplit'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e155be4",
   "metadata": {},
   "source": [
    "The ``DataSplit`` field tells us where our first group (e.g. bacteria) ends our second begins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0540d138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0, 253, 500])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['DataSplit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44567480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Set1', 'Set2'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data[\"SeparatedReads\"].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f60681",
   "metadata": {},
   "source": [
    "The ```GroundTruthCovariance``` field gives the covariance matrix used to generate the data. Below, we show the positive/negative entry pattern for this matrix, with the diagonal removed (and ignoring magnitude). Blue dots represent positive interactions and red represent negative. The black lines indicate the two data sets, so the top left box is intra-kingdom 1, the bottome right is intra-kingdom 2, and the off-diagonal blocks are cross-kingddom edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3143017f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(topQnet(synthetic_data[\"GroundTruthCovariance\"].round(8),q = 0),cmap = \"bwr_r\",cbar = False,ax = ax,center = 0)\n",
    "ax.plot([0,1000],[synthetic_data['DataSplit'][1],synthetic_data['DataSplit'][1]],color = 'k')\n",
    "ax.plot([synthetic_data['DataSplit'][1],synthetic_data['DataSplit'][1]],[0,1000],color = 'k')\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ca102e",
   "metadata": {},
   "source": [
    "We can also check for bias in edges in groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5204a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Block', ylabel='Edge'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kingdom1 = synthetic_data[\"GroundTruthCovariance\"][:synthetic_data['DataSplit'][1],:synthetic_data['DataSplit'][1]].flatten()\n",
    "kingdom2 = synthetic_data[\"GroundTruthCovariance\"][synthetic_data['DataSplit'][1]:,synthetic_data['DataSplit'][1]:].flatten()\n",
    "Xkingdom = synthetic_data[\"GroundTruthCovariance\"][:synthetic_data['DataSplit'][1],synthetic_data['DataSplit'][1]:].flatten()\n",
    "alledges = pd.DataFrame(np.array([np.concatenate([kingdom1,kingdom2,Xkingdom]).round(5),[\"Kingdom 1\"]*len(kingdom1)+[\"Kingdom 2\"]*len(kingdom2) + [\"Cross Kingdom\"]*len(Xkingdom)]).T)\n",
    "alledges.columns = [\"Edge\",\"Block\"]\n",
    "alledges['Edge'] = alledges['Edge'].astype(float)\n",
    "sb.boxplot(data = alledges[alledges[\"Edge\"] != 0],y = \"Edge\",x = \"Block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "56b71d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5184760156070691, 0.6165863066538091, 1.0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(kingdom1.round(8)>0)/sum(kingdom1.round(8) != 0),sum(kingdom2.round(8)>0)/sum(kingdom2.round(8) != 0),sum(Xkingdom.round(8)>0)/sum(Xkingdom.round(8) != 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161b2d16",
   "metadata": {},
   "source": [
    "The ```GraphSparsity``` field tells us the proportion of nonzero entries in the ground truth covariance matrix. This represents the proportion of pairs of taxa that interact in our simulated environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b1c32bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.041162324649298594"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['GraphSparsity']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13b6929",
   "metadata": {},
   "source": [
    "The ```AbsoluteSamples``` field contains the table of synthetic absolute abundances of each taxa (rows) in each sample (columns) that was simulated. These are drawn from a log-normal distribution, meaning that all taxa are present in every sample, but many at a very low abundance. This data is not possible to collect in the real world. Note that taxa are simply numbered from 0 to N-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "01e01b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S0</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>S7</th>\n",
       "      <th>S8</th>\n",
       "      <th>S9</th>\n",
       "      <th>...</th>\n",
       "      <th>S140</th>\n",
       "      <th>S141</th>\n",
       "      <th>S142</th>\n",
       "      <th>S143</th>\n",
       "      <th>S144</th>\n",
       "      <th>S145</th>\n",
       "      <th>S146</th>\n",
       "      <th>S147</th>\n",
       "      <th>S148</th>\n",
       "      <th>S149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.206851</td>\n",
       "      <td>0.319479</td>\n",
       "      <td>0.144049</td>\n",
       "      <td>0.266253</td>\n",
       "      <td>0.241467</td>\n",
       "      <td>0.126798</td>\n",
       "      <td>0.169195</td>\n",
       "      <td>0.226380</td>\n",
       "      <td>0.278721</td>\n",
       "      <td>0.121094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235664</td>\n",
       "      <td>0.187843</td>\n",
       "      <td>0.221304</td>\n",
       "      <td>0.138918</td>\n",
       "      <td>0.369146</td>\n",
       "      <td>0.311293</td>\n",
       "      <td>0.189208</td>\n",
       "      <td>0.150510</td>\n",
       "      <td>0.163964</td>\n",
       "      <td>0.302255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.324924</td>\n",
       "      <td>2.126087</td>\n",
       "      <td>0.262886</td>\n",
       "      <td>0.145616</td>\n",
       "      <td>0.175187</td>\n",
       "      <td>0.159738</td>\n",
       "      <td>0.368684</td>\n",
       "      <td>1.979624</td>\n",
       "      <td>0.571089</td>\n",
       "      <td>0.038247</td>\n",
       "      <td>...</td>\n",
       "      <td>1.743859</td>\n",
       "      <td>0.515653</td>\n",
       "      <td>0.575315</td>\n",
       "      <td>0.190190</td>\n",
       "      <td>4.484271</td>\n",
       "      <td>0.289501</td>\n",
       "      <td>4.217794</td>\n",
       "      <td>0.493455</td>\n",
       "      <td>1.114790</td>\n",
       "      <td>0.350141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.557841</td>\n",
       "      <td>1.032205</td>\n",
       "      <td>1.824755</td>\n",
       "      <td>0.020507</td>\n",
       "      <td>0.309658</td>\n",
       "      <td>1.297989</td>\n",
       "      <td>0.458350</td>\n",
       "      <td>3.561440</td>\n",
       "      <td>1.295318</td>\n",
       "      <td>0.614865</td>\n",
       "      <td>...</td>\n",
       "      <td>3.203597</td>\n",
       "      <td>0.064004</td>\n",
       "      <td>0.046146</td>\n",
       "      <td>0.140067</td>\n",
       "      <td>0.616171</td>\n",
       "      <td>0.312452</td>\n",
       "      <td>1.527052</td>\n",
       "      <td>0.424610</td>\n",
       "      <td>0.150366</td>\n",
       "      <td>2.809827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.731986</td>\n",
       "      <td>0.737452</td>\n",
       "      <td>1.510675</td>\n",
       "      <td>1.161495</td>\n",
       "      <td>1.236112</td>\n",
       "      <td>0.630246</td>\n",
       "      <td>1.391427</td>\n",
       "      <td>0.409618</td>\n",
       "      <td>0.670082</td>\n",
       "      <td>0.878800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635783</td>\n",
       "      <td>0.814339</td>\n",
       "      <td>0.777925</td>\n",
       "      <td>1.265082</td>\n",
       "      <td>1.158828</td>\n",
       "      <td>0.676163</td>\n",
       "      <td>0.436419</td>\n",
       "      <td>0.546633</td>\n",
       "      <td>1.502213</td>\n",
       "      <td>0.672692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.660323</td>\n",
       "      <td>2.327800</td>\n",
       "      <td>0.605367</td>\n",
       "      <td>5.649679</td>\n",
       "      <td>4.407956</td>\n",
       "      <td>0.514480</td>\n",
       "      <td>5.166895</td>\n",
       "      <td>4.473462</td>\n",
       "      <td>3.526346</td>\n",
       "      <td>3.868011</td>\n",
       "      <td>...</td>\n",
       "      <td>1.870919</td>\n",
       "      <td>1.761595</td>\n",
       "      <td>2.811904</td>\n",
       "      <td>3.842246</td>\n",
       "      <td>2.315103</td>\n",
       "      <td>1.092174</td>\n",
       "      <td>3.243532</td>\n",
       "      <td>0.910014</td>\n",
       "      <td>1.165841</td>\n",
       "      <td>3.246331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.408149</td>\n",
       "      <td>0.195403</td>\n",
       "      <td>0.255106</td>\n",
       "      <td>2.106130</td>\n",
       "      <td>0.260838</td>\n",
       "      <td>1.590099</td>\n",
       "      <td>0.298289</td>\n",
       "      <td>1.698537</td>\n",
       "      <td>0.210876</td>\n",
       "      <td>1.024898</td>\n",
       "      <td>...</td>\n",
       "      <td>0.278303</td>\n",
       "      <td>0.117710</td>\n",
       "      <td>0.415849</td>\n",
       "      <td>5.730109</td>\n",
       "      <td>0.072340</td>\n",
       "      <td>2.270446</td>\n",
       "      <td>0.119889</td>\n",
       "      <td>0.444134</td>\n",
       "      <td>18.281873</td>\n",
       "      <td>0.121075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.971491</td>\n",
       "      <td>1.127244</td>\n",
       "      <td>0.225044</td>\n",
       "      <td>0.029557</td>\n",
       "      <td>0.746274</td>\n",
       "      <td>2.646748</td>\n",
       "      <td>0.097425</td>\n",
       "      <td>2.572064</td>\n",
       "      <td>9.733374</td>\n",
       "      <td>1.223701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103394</td>\n",
       "      <td>1.135847</td>\n",
       "      <td>2.659265</td>\n",
       "      <td>8.568529</td>\n",
       "      <td>1.658016</td>\n",
       "      <td>0.793231</td>\n",
       "      <td>1.775258</td>\n",
       "      <td>18.942158</td>\n",
       "      <td>1.702085</td>\n",
       "      <td>0.505739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.238982</td>\n",
       "      <td>0.175299</td>\n",
       "      <td>0.285807</td>\n",
       "      <td>0.421842</td>\n",
       "      <td>0.119519</td>\n",
       "      <td>0.368724</td>\n",
       "      <td>0.213593</td>\n",
       "      <td>0.069048</td>\n",
       "      <td>0.302275</td>\n",
       "      <td>0.211513</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122463</td>\n",
       "      <td>0.218448</td>\n",
       "      <td>0.626824</td>\n",
       "      <td>0.169915</td>\n",
       "      <td>0.399856</td>\n",
       "      <td>0.342429</td>\n",
       "      <td>0.338090</td>\n",
       "      <td>0.213452</td>\n",
       "      <td>0.163807</td>\n",
       "      <td>0.266714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.077119</td>\n",
       "      <td>0.195498</td>\n",
       "      <td>0.173053</td>\n",
       "      <td>2.000147</td>\n",
       "      <td>0.245640</td>\n",
       "      <td>0.635885</td>\n",
       "      <td>0.540666</td>\n",
       "      <td>0.108509</td>\n",
       "      <td>0.024470</td>\n",
       "      <td>0.503231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075568</td>\n",
       "      <td>1.629381</td>\n",
       "      <td>0.114987</td>\n",
       "      <td>0.453151</td>\n",
       "      <td>0.035092</td>\n",
       "      <td>0.829881</td>\n",
       "      <td>0.139668</td>\n",
       "      <td>0.579028</td>\n",
       "      <td>0.037032</td>\n",
       "      <td>1.879051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.407110</td>\n",
       "      <td>0.043738</td>\n",
       "      <td>0.414788</td>\n",
       "      <td>7.587801</td>\n",
       "      <td>0.166095</td>\n",
       "      <td>0.042423</td>\n",
       "      <td>9.541775</td>\n",
       "      <td>4.852974</td>\n",
       "      <td>0.709608</td>\n",
       "      <td>4.774640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197581</td>\n",
       "      <td>0.123505</td>\n",
       "      <td>0.368903</td>\n",
       "      <td>1.478493</td>\n",
       "      <td>1.908562</td>\n",
       "      <td>0.280668</td>\n",
       "      <td>0.493051</td>\n",
       "      <td>0.453865</td>\n",
       "      <td>0.269652</td>\n",
       "      <td>1.802635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          S0        S1        S2        S3        S4        S5        S6  \\\n",
       "0   0.206851  0.319479  0.144049  0.266253  0.241467  0.126798  0.169195   \n",
       "1   1.324924  2.126087  0.262886  0.145616  0.175187  0.159738  0.368684   \n",
       "2   0.557841  1.032205  1.824755  0.020507  0.309658  1.297989  0.458350   \n",
       "3   0.731986  0.737452  1.510675  1.161495  1.236112  0.630246  1.391427   \n",
       "4   1.660323  2.327800  0.605367  5.649679  4.407956  0.514480  5.166895   \n",
       "5   3.408149  0.195403  0.255106  2.106130  0.260838  1.590099  0.298289   \n",
       "6  30.971491  1.127244  0.225044  0.029557  0.746274  2.646748  0.097425   \n",
       "7   0.238982  0.175299  0.285807  0.421842  0.119519  0.368724  0.213593   \n",
       "8   1.077119  0.195498  0.173053  2.000147  0.245640  0.635885  0.540666   \n",
       "9   1.407110  0.043738  0.414788  7.587801  0.166095  0.042423  9.541775   \n",
       "\n",
       "         S7        S8        S9  ...      S140      S141      S142      S143  \\\n",
       "0  0.226380  0.278721  0.121094  ...  0.235664  0.187843  0.221304  0.138918   \n",
       "1  1.979624  0.571089  0.038247  ...  1.743859  0.515653  0.575315  0.190190   \n",
       "2  3.561440  1.295318  0.614865  ...  3.203597  0.064004  0.046146  0.140067   \n",
       "3  0.409618  0.670082  0.878800  ...  0.635783  0.814339  0.777925  1.265082   \n",
       "4  4.473462  3.526346  3.868011  ...  1.870919  1.761595  2.811904  3.842246   \n",
       "5  1.698537  0.210876  1.024898  ...  0.278303  0.117710  0.415849  5.730109   \n",
       "6  2.572064  9.733374  1.223701  ...  0.103394  1.135847  2.659265  8.568529   \n",
       "7  0.069048  0.302275  0.211513  ...  0.122463  0.218448  0.626824  0.169915   \n",
       "8  0.108509  0.024470  0.503231  ...  0.075568  1.629381  0.114987  0.453151   \n",
       "9  4.852974  0.709608  4.774640  ...  0.197581  0.123505  0.368903  1.478493   \n",
       "\n",
       "       S144      S145      S146       S147       S148      S149  \n",
       "0  0.369146  0.311293  0.189208   0.150510   0.163964  0.302255  \n",
       "1  4.484271  0.289501  4.217794   0.493455   1.114790  0.350141  \n",
       "2  0.616171  0.312452  1.527052   0.424610   0.150366  2.809827  \n",
       "3  1.158828  0.676163  0.436419   0.546633   1.502213  0.672692  \n",
       "4  2.315103  1.092174  3.243532   0.910014   1.165841  3.246331  \n",
       "5  0.072340  2.270446  0.119889   0.444134  18.281873  0.121075  \n",
       "6  1.658016  0.793231  1.775258  18.942158   1.702085  0.505739  \n",
       "7  0.399856  0.342429  0.338090   0.213452   0.163807  0.266714  \n",
       "8  0.035092  0.829881  0.139668   0.579028   0.037032  1.879051  \n",
       "9  1.908562  0.280668  0.493051   0.453865   0.269652  1.802635  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['AbsoluteSamples'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaaed599",
   "metadata": {},
   "source": [
    "The ```CombinedReads``` field contains a single table of simulated read counts for the entire combined (paired) data-set. This would be the result of concatenating e.g. the outputs of 16S sequencing and ITS sequencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "058b139f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>S0</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
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       "      <th>S7</th>\n",
       "      <th>S8</th>\n",
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       "      <th>S140</th>\n",
       "      <th>S141</th>\n",
       "      <th>S142</th>\n",
       "      <th>S143</th>\n",
       "      <th>S144</th>\n",
       "      <th>S145</th>\n",
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       "      <th>S147</th>\n",
       "      <th>S148</th>\n",
       "      <th>S149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>192</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>293</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>35</td>\n",
       "      <td>84</td>\n",
       "      <td>30</td>\n",
       "      <td>319</td>\n",
       "      <td>37</td>\n",
       "      <td>449</td>\n",
       "      <td>83</td>\n",
       "      <td>142</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>104</td>\n",
       "      <td>96</td>\n",
       "      <td>7</td>\n",
       "      <td>46</td>\n",
       "      <td>184</td>\n",
       "      <td>37</td>\n",
       "      <td>526</td>\n",
       "      <td>159</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>216</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>187</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>73</td>\n",
       "      <td>158</td>\n",
       "      <td>175</td>\n",
       "      <td>72</td>\n",
       "      <td>148</td>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>120</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>72</td>\n",
       "      <td>116</td>\n",
       "      <td>242</td>\n",
       "      <td>97</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>115</td>\n",
       "      <td>218</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>133</td>\n",
       "      <td>210</td>\n",
       "      <td>24</td>\n",
       "      <td>692</td>\n",
       "      <td>706</td>\n",
       "      <td>74</td>\n",
       "      <td>532</td>\n",
       "      <td>646</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>149</td>\n",
       "      <td>465</td>\n",
       "      <td>638</td>\n",
       "      <td>153</td>\n",
       "      <td>135</td>\n",
       "      <td>343</td>\n",
       "      <td>171</td>\n",
       "      <td>168</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>288</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>244</td>\n",
       "      <td>33</td>\n",
       "      <td>208</td>\n",
       "      <td>23</td>\n",
       "      <td>262</td>\n",
       "      <td>25</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>79</td>\n",
       "      <td>1010</td>\n",
       "      <td>10</td>\n",
       "      <td>290</td>\n",
       "      <td>13</td>\n",
       "      <td>75</td>\n",
       "      <td>2582</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2608</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>379</td>\n",
       "      <td>10</td>\n",
       "      <td>387</td>\n",
       "      <td>1200</td>\n",
       "      <td>159</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>419</td>\n",
       "      <td>1399</td>\n",
       "      <td>124</td>\n",
       "      <td>95</td>\n",
       "      <td>201</td>\n",
       "      <td>3642</td>\n",
       "      <td>226</td>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>54</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>89</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>49</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>93</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>239</td>\n",
       "      <td>35</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>148</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>17</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>260</td>\n",
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       "      <th>9</th>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>918</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>971</td>\n",
       "      <td>711</td>\n",
       "      <td>82</td>\n",
       "      <td>563</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>246</td>\n",
       "      <td>145</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>92</td>\n",
       "      <td>43</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     S0   S1  S2   S3   S4   S5   S6   S7    S8   S9  ...  S140  S141  S142  \\\n",
       "0    18   32  10   38   37   11   28   33    34   16  ...    23    20    42   \n",
       "1   110  192  14   13   31   20   36  293    65   10  ...   109    35    84   \n",
       "2    51  104  96    7   46  184   37  526   159   61  ...   216     4     5   \n",
       "3    54   56  73  158  175   72  148   65    88  120  ...    41    72   116   \n",
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       "5   288   14   6  244   33  208   23  262    25  123  ...    17     9    79   \n",
       "6  2608  106  18    1  116  379   10  387  1200  159  ...     3    86   419   \n",
       "7    20   16  15   54   22   43   28    9    28   32  ...    15    15    89   \n",
       "8    93   17   7  239   35   80   61   17     3   62  ...     4   148    20   \n",
       "9   105    4  26  918   29    4  971  711    82  563  ...    13     8    59   \n",
       "\n",
       "   S143  S144  S145  S146  S147  S148  S149  \n",
       "0    29    31    34    18    32    29    48  \n",
       "1    30   319    37   449    83   142    51  \n",
       "2    21    40    40   187    77    19   464  \n",
       "3   242    97    91    53   115   218    99  \n",
       "4   638   153   135   343   171   168   469  \n",
       "5  1010    10   290    13    75  2582    27  \n",
       "6  1399   124    95   201  3642   226    79  \n",
       "7    33    24    49    31    54    25    33  \n",
       "8    80     4   108    17   109     2   260  \n",
       "9   246   145    38    65    92    43   282  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['CombinedReads'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5a0264",
   "metadata": {},
   "source": [
    "The ```SeparatedReads``` field contains a dictionary of the separate read counts for the two sets of taxa (e.g. bacteria and fungi). These would be the result of separate 16S and ITS sequencing. Note that taxa labels are consistent, so the first set is numbered 0 to M-1 and the second is numbered M to N, where M is the number of taxa in the first set and N is the total number of taxa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "cd1f38a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Set1', 'Set2'])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['SeparatedReads'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5f8154ca",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     S0   S1  S2   S3   S4   S5   S6   S7    S8   S9  ...  S140  S141  S142  \\\n",
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       "\n",
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       "3   242    97    91    53   115   218    99  \n",
       "4   638   153   135   343   171   168   469  \n",
       "5  1010    10   290    13    75  2582    27  \n",
       "6  1399   124    95   201  3642   226    79  \n",
       "7    33    24    49    31    54    25    33  \n",
       "8    80     4   108    17   109     2   260  \n",
       "9   246   145    38    65    92    43   282  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['SeparatedReads']['Set1'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32a88c90",
   "metadata": {},
   "outputs": [
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       "      <td>74</td>\n",
       "      <td>169</td>\n",
       "      <td>...</td>\n",
       "      <td>1258</td>\n",
       "      <td>1285</td>\n",
       "      <td>2</td>\n",
       "      <td>743</td>\n",
       "      <td>419</td>\n",
       "      <td>530</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>5687</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>53</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1002</td>\n",
       "      <td>360</td>\n",
       "      <td>406</td>\n",
       "      <td>653</td>\n",
       "      <td>49</td>\n",
       "      <td>426</td>\n",
       "      <td>721</td>\n",
       "      <td>1349</td>\n",
       "      <td>4502</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>647</td>\n",
       "      <td>3241</td>\n",
       "      <td>385</td>\n",
       "      <td>262</td>\n",
       "      <td>805</td>\n",
       "      <td>1337</td>\n",
       "      <td>656</td>\n",
       "      <td>2882</td>\n",
       "      <td>519</td>\n",
       "      <td>1663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>804</td>\n",
       "      <td>636</td>\n",
       "      <td>2335</td>\n",
       "      <td>236</td>\n",
       "      <td>5252</td>\n",
       "      <td>2355</td>\n",
       "      <td>892</td>\n",
       "      <td>697</td>\n",
       "      <td>512</td>\n",
       "      <td>411</td>\n",
       "      <td>...</td>\n",
       "      <td>168</td>\n",
       "      <td>103</td>\n",
       "      <td>279</td>\n",
       "      <td>513</td>\n",
       "      <td>265</td>\n",
       "      <td>3004</td>\n",
       "      <td>3277</td>\n",
       "      <td>1413</td>\n",
       "      <td>361</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>540</td>\n",
       "      <td>264</td>\n",
       "      <td>273</td>\n",
       "      <td>113</td>\n",
       "      <td>1426</td>\n",
       "      <td>440</td>\n",
       "      <td>576</td>\n",
       "      <td>4525</td>\n",
       "      <td>13</td>\n",
       "      <td>190</td>\n",
       "      <td>...</td>\n",
       "      <td>266</td>\n",
       "      <td>586</td>\n",
       "      <td>405</td>\n",
       "      <td>1210</td>\n",
       "      <td>2161</td>\n",
       "      <td>5349</td>\n",
       "      <td>465</td>\n",
       "      <td>800</td>\n",
       "      <td>2160</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>181</td>\n",
       "      <td>255</td>\n",
       "      <td>9</td>\n",
       "      <td>163</td>\n",
       "      <td>112</td>\n",
       "      <td>19</td>\n",
       "      <td>96</td>\n",
       "      <td>246</td>\n",
       "      <td>1374</td>\n",
       "      <td>223</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>456</td>\n",
       "      <td>73</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>530</td>\n",
       "      <td>53</td>\n",
       "      <td>3991</td>\n",
       "      <td>166</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>419</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>107</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>48</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>15</td>\n",
       "      <td>63</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>335</td>\n",
       "      <td>407</td>\n",
       "      <td>253</td>\n",
       "      <td>944</td>\n",
       "      <td>1101</td>\n",
       "      <td>1236</td>\n",
       "      <td>708</td>\n",
       "      <td>656</td>\n",
       "      <td>784</td>\n",
       "      <td>576</td>\n",
       "      <td>...</td>\n",
       "      <td>742</td>\n",
       "      <td>259</td>\n",
       "      <td>228</td>\n",
       "      <td>497</td>\n",
       "      <td>230</td>\n",
       "      <td>2889</td>\n",
       "      <td>512</td>\n",
       "      <td>735</td>\n",
       "      <td>337</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       S0    S1    S2   S3    S4    S5   S6    S7    S8   S9  ...  S140  S141  \\\n",
       "289  2214  2210    12  237    21   110  210    56    74  169  ...  1258  1285   \n",
       "290    21    35    26   51    22    26   23    12    15   31  ...    17    26   \n",
       "291  1002   360   406  653    49   426  721  1349  4502   58  ...   647  3241   \n",
       "292   804   636  2335  236  5252  2355  892   697   512  411  ...   168   103   \n",
       "293   540   264   273  113  1426   440  576  4525    13  190  ...   266   586   \n",
       "294   181   255     9  163   112    19   96   246  1374  223  ...    27   456   \n",
       "295   419    14    31  107     6    65   22    31    17   18  ...    45    77   \n",
       "296    16    20    11   48    21    25   10     9    14   23  ...    23    29   \n",
       "297    15    63    34    0    51    17   10     7     3   21  ...    18     5   \n",
       "298   335   407   253  944  1101  1236  708   656   784  576  ...   742   259   \n",
       "\n",
       "     S142  S143  S144  S145  S146  S147  S148  S149  \n",
       "289     2   743   419   530    56    63  5687   873  \n",
       "290    19    40    16    97    28    13    53    16  \n",
       "291   385   262   805  1337   656  2882   519  1663  \n",
       "292   279   513   265  3004  3277  1413   361   279  \n",
       "293   405  1210  2161  5349   465   800  2160    94  \n",
       "294    73    64    58   530    53  3991   166   397  \n",
       "295     3     9    32    31    17    59     3     2  \n",
       "296    18    32     3    23    33    37    15    33  \n",
       "297     9    19     8    20    50    51     3    10  \n",
       "298   228   497   230  2889   512   735   337  1585  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['SeparatedReads']['Set2'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4806ce79",
   "metadata": {},
   "source": [
    "The ```GroupOrder``` field contains a list of the data ranges for each set, in order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "752b44c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 289), (289, 500)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['GroupOrder']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e9c3fb",
   "metadata": {},
   "source": [
    "The ```ReadDepths``` field contains a dictionary of arrays that tell us the total number of reads used for each sample for each data set. Note that this can also be obtained by ```synthetic_data['SeparatedReads']['Set1'].sum()```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce57e212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Set1', 'Set2'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['ReadDepths'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9575898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100556,  83408,  95764,  94447,  89670,  99276, 110791,  97765,\n",
       "       108342,  94356, 108152, 106211, 102671,  91856,  89436,  91827,\n",
       "        98290,  86616, 108619, 116247,  97304,  93325, 110823,  90344,\n",
       "       112823,  91685,  87262,  97419,  94724, 104961, 113926,  97472,\n",
       "       107211, 106576, 110312, 103340, 100536,  94843,  84703, 110298,\n",
       "        99214, 109402,  97679, 112853,  84702, 110742,  92355, 116448,\n",
       "       114844,  78012,  87779, 100366, 106154,  84694,  94185, 100385,\n",
       "       109784,  96418, 103037,  88047, 106962, 103130,  96585,  86637,\n",
       "       109265,  95193,  92409,  97329, 101282, 110262, 102114,  94592,\n",
       "       112847,  93855, 106244, 105576,  91886, 103293, 119527,  94773,\n",
       "        93561, 107198,  93631, 123951, 104291, 108323,  95682, 105024,\n",
       "       100161, 102140,  88099,  97125,  89011,  91187, 110124,  80593,\n",
       "        92845,  88865,  98241, 105101,  86930, 105049, 116006, 113055,\n",
       "       102473, 108726,  91735,  98740,  91813,  91004, 106004, 109785,\n",
       "        93573, 104689, 109584,  84086, 111028, 104905,  98640,  97695,\n",
       "       101506, 101784,  95425,  93650,  96379,  85419,  91447, 105486,\n",
       "       111328,  97958,  94411,  99133,  94781, 113134,  96515, 116507,\n",
       "       101097,  99068,  98032, 111800,  91101, 106592, 115435, 105621,\n",
       "        81845,  89666,  87773, 102564, 113148, 106145])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['ReadDepths'][\"Set1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d56a1c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([103428,  79316, 104891, 103981,  92478, 100087,  91900,  88957,\n",
       "        92002,  90433, 106886, 118381,  97833,  93737, 103229, 114683,\n",
       "        95685, 106316, 108539, 106980, 100103,  86865, 103616, 105131,\n",
       "       105500, 106553,  89874,  91342,  94519, 108881, 103530,  97336,\n",
       "        80539,  89879,  82280,  85308, 108803, 108576,  92946, 113753,\n",
       "       111785, 107142,  94622, 107977, 107051,  99262, 120454, 105561,\n",
       "       123296, 114425, 109240,  98467, 109512, 112592, 103129, 103217,\n",
       "       102319, 110055, 113515,  94558,  99875, 109595,  85875,  83359,\n",
       "       109069, 103322, 103169, 107407, 107857, 107536,  91751, 101002,\n",
       "        96638,  94982, 109254, 102538, 103002,  88090, 101005, 107055,\n",
       "       107204, 122157, 102411, 102913,  91616, 103424, 106929,  95509,\n",
       "        98863, 107197,  91008, 106785,  88738, 101296, 109401, 121217,\n",
       "        98359, 106235,  83447, 107122, 110008,  93024,  94472,  95188,\n",
       "        86214, 107689, 111878,  98250,  97878,  86574,  98452,  89272,\n",
       "        96747,  91471, 101739,  78789, 106073, 105023, 121179, 114637,\n",
       "       105246,  98337,  94688,  87257, 105541,  96645,  96619, 108165,\n",
       "       121195,  98729,  98697, 125605,  95682, 102677, 105582, 111230,\n",
       "       111315,  96093, 101195, 104867, 122383,  96440,  84008, 101605,\n",
       "       107543, 102043,  93222, 109094,  94676,  88264])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['ReadDepths'][\"Set2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5da5350",
   "metadata": {},
   "source": [
    "The ```TotalRatioVariance``` field contains the variance of the ratio of (total absolute abundance in kingdom 1)/(total absolute abundance in kingdom 2) across samples. This quantity plays a large role in the error of network inference with concatenated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a42dbe51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.375108787486058"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data['TotalRatioVariance']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6b9dc2",
   "metadata": {},
   "source": [
    "## What's going on under the hood?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732b58c1",
   "metadata": {},
   "source": [
    "To demonstrate how the data is generated, the following is a step-by-step version of what the ```generate_synthetic_data``` does."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b84c990",
   "metadata": {},
   "source": [
    "The first task is to generate a covariance matrix. That process can be broken into a few steps:\n",
    "\n",
    "First, we use NetworkX to generate a graph structure for the community. This will be the sparsity (0 entries) structure of our covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "3cf7c60f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nedges = gsp*(N**2)\n",
    "p = 0.3\n",
    "K = max(int(nedges/(N*(1+p))),1)\n",
    "graph = nx.powerlaw_cluster_graph(N, K, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "a1b5c360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(nx.to_numpy_array(graph),cmap = \"bwr_r\",cbar = False,center = 0)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f961ed9b",
   "metadata": {},
   "source": [
    "Next, we shuffle the labels of this graph, so that edges and clusters are evenly distributed between the two kingdoms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "23ae8986",
   "metadata": {},
   "outputs": [],
   "source": [
    "Adj = nx.to_numpy_array(graph)\n",
    "sh = np.random.permutation(N)\n",
    "shffld = Adj[sh][:,sh]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "de63152c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(shffld,cmap = \"bwr_r\",cbar = False,center = 0)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbc7aff",
   "metadata": {},
   "source": [
    "Next we choose which taxa belong to which kingdom. Notice that our model assumes that covariances across kingdoms and within kingdoms are evenly distributed. In other words, it assumes that the only distinction between kingdoms is in how abundances are measured. To change this, one could use a different method of constructing the underlying covariance matrix (e.g. by choosing one kingdom's intra-kingdom edges to be denser than the others). We have not implemented this functionality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "abdae4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "minsplit = int(0.2*N)\n",
    "maxsplit = int(0.8*N)\n",
    "splits = np.sort(np.random.choice(range(minsplit,maxsplit),size = ns-1,replace = False))\n",
    "\n",
    "spl_i = np.concatenate([[0],splits,[N]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e3c4a83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0, 170, 500])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spl_i"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5660368b",
   "metadata": {},
   "source": [
    "If we want more or less cross-kingdom edges (regardless of sign), we can add or remove edges now. We are going to design a method to give us a desired sparsity relative to the interkingdom edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bfce13e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_adjust = {\"Kingdoms\":(0,1),\"SparsityRatio\":0.1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "7ec4832a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shffld = adjust_sparsity(sparse_adjust,shffld,spl_i)\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(shffld,cmap = \"bwr_r\",cbar = False,center = 0)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6d5a9d",
   "metadata": {},
   "source": [
    "And compute the sparsity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "81b88596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07206412825651302"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sp = shffld.sum()/(N*(N-1))\n",
    "Sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fafd3eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f032f28b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "69c0a78b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run synthetic_fit_funcs.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5a8ca0",
   "metadata": {},
   "source": [
    "Next we generate a positive definite matrix with the same 0 pattern and both positive and negative real-valued (non-integer) values. To do so, we iterate through the nodes of the above graph choosing a random vector for each. For each node, we choose a random vector that is perpendicular to the vector for previous nodes which do not have an edge to the current node. We call this collection ```Q```. Then the product ```QQ^T``` is a positive definite matrix with the desired sparsity pattern.\n",
    "\n",
    "We can furthermore choose vectors with only negative/positive interactions, or biased toward positive/negative interactions, by computing the dual cone of the vectors we want a positive interaction with (or polar cone of those we want a negative interaction with)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "46e4b87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 169/169 [00:01<00:00, 162.38it/s]\n"
     ]
    }
   ],
   "source": [
    "Q = (min_var + (max_var-min_var))*np.random.rand()*np.eye(N)\n",
    "\n",
    "for i in tqdm(range(1,spl_i[1])):\n",
    "    Q[i] = rand_in_perp(Q[:i][~shffld[i,:i].astype(bool)],mxvars = max_var,minvars = min_var)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "0109fdef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 500)"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q[:spl_i[1]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "8e035c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(Q[:spl_i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "ee948478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "e7be5f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 330/330 [00:19<00:00, 17.18it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(spl_i[1],N)):\n",
    "    r1 = rand_in_perp(Q[:i][~shffld[i,:i].astype(bool)],mxvars = max_var,minvars = min_var,bias = 1, neg = Q[:spl_i[1]][shffld[i,:spl_i[1]].astype(bool)])\n",
    "    r2 = rand_in_perp(Q[:i],mxvars = max_var,minvars = min_var,bias = 1, neg = Q[:spl_i[1]][shffld[i,:spl_i[1]].astype(bool)])\n",
    "    Q[i] = r1 + 2*r2\n",
    "covar = np.dot(Q,Q.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "f77c2276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(covar,tol = 10**-8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "437420f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.352022199894568e-06"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(np.linalg.svd(covar)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "939e3ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(3, 0), dtype=float64)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la.null_space(np.random.rand(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "6f30d04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "2d05b0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(topQnet(covar.round(11),q = 0),cmap = \"bwr_r\",cbar = False,ax = ax,center = 0)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "0226eb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47192982456140353"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(covar[:spl_i[1],:spl_i[1]].round(8) < 0).sum()/(covar[:spl_i[1],:spl_i[1]].round(8) != 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "8db90155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37834489593657084"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(covar[spl_i[1]:,spl_i[1]:].round(8) < 0).sum()/(covar[spl_i[1]:,spl_i[1]:].round(8) != 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "1d050794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(covar[:spl_i[1],spl_i[1]:].round(8) < 0).sum()/(covar[:spl_i[1],spl_i[1]:].round(8) != 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "28d48f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.737500505756549e-16"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(np.linalg.svd(covar)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8888ca74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(covar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ee0be6",
   "metadata": {},
   "source": [
    "The function that combines these steps is ```create_groundtruth_covariance```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3acf613f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run synthetic_fit_funcs.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "eea196e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 48/237 [00:00<00:00, 474.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[create_groundtruth_covariance] Generating cholesky decomposition\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 237/237 [00:01<00:00, 145.15it/s]\n",
      "100%|██████████| 263/263 [00:06<00:00, 39.70it/s]\n"
     ]
    }
   ],
   "source": [
    "covar_matrix, actual_sparsity = create_groundtruth_covariance(N,grmod,gsp,spl_i,mxvars=max_var,minvars=min_var,bias_blocks=None,bias_str=0.8,sparse_adjust = sparse_adjust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9646fc00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "ax.xaxis.set_tick_params(labelbottom=False)\n",
    "ax.yaxis.set_tick_params(labelleft=False)\n",
    "\n",
    "sb.heatmap(topQnet(covar_matrix.round(8),q = 0),cmap = \"bwr_r\",cbar = False,ax = ax,center = 0)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9f736547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Block', ylabel='Edge'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kingdom1 = covar_matrix[:spl_i[1],:spl_i[1]].flatten()\n",
    "kingdom2 = covar_matrix[spl_i[1]:,spl_i[1]:].flatten()\n",
    "Xkingdom = covar_matrix[:spl_i[1],spl_i[1]:].flatten()\n",
    "alledges = pd.DataFrame(np.array([np.concatenate([kingdom1,kingdom2,Xkingdom]).round(5),[\"Kingdom 1\"]*len(kingdom1)+[\"Kingdom 2\"]*len(kingdom2) + [\"Cross Kingdom\"]*len(Xkingdom)]).T)\n",
    "alledges.columns = [\"Edge\",\"Block\"]\n",
    "alledges['Edge'] = alledges['Edge'].astype(float)\n",
    "sb.boxplot(data = alledges[alledges[\"Edge\"] != 0],y = \"Edge\",x = \"Block\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd4fc66",
   "metadata": {},
   "source": [
    "Next we generate synthetic \"true\" taxa abundance for each sample, simply by drawing from a log-normal distribution. This is achieved by the ```generate_synthetic_samples``` function, which also randomly draws a vector of mean abundances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bcbc415a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/150 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[generate_synthetic_samples] Drawing 150 Samples for 500 taxa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 150/150 [00:07<00:00, 20.79it/s]\n"
     ]
    }
   ],
   "source": [
    "absolute_abundance,_ = generate_synthetic_samples(M,covar_matrix,mean_log_range = mean_spread,mean_log_center = mean_center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfe31a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>0.379329</td>\n",
       "      <td>2.125491</td>\n",
       "      <td>2.087712</td>\n",
       "      <td>2.870420</td>\n",
       "      <td>1.025765</td>\n",
       "      <td>8.565928</td>\n",
       "      <td>3.916336</td>\n",
       "      <td>3.530386</td>\n",
       "      <td>1.530494</td>\n",
       "      <td>0.846550</td>\n",
       "      <td>...</td>\n",
       "      <td>3.794360</td>\n",
       "      <td>2.912683</td>\n",
       "      <td>3.019321</td>\n",
       "      <td>1.806374</td>\n",
       "      <td>0.900212</td>\n",
       "      <td>0.673878</td>\n",
       "      <td>1.187524</td>\n",
       "      <td>1.358361</td>\n",
       "      <td>2.597537</td>\n",
       "      <td>1.725046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.606003</td>\n",
       "      <td>0.472983</td>\n",
       "      <td>0.748652</td>\n",
       "      <td>0.844993</td>\n",
       "      <td>0.811071</td>\n",
       "      <td>0.254297</td>\n",
       "      <td>0.499367</td>\n",
       "      <td>0.768882</td>\n",
       "      <td>1.135604</td>\n",
       "      <td>0.800742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215083</td>\n",
       "      <td>0.286355</td>\n",
       "      <td>0.796666</td>\n",
       "      <td>0.956499</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>1.484827</td>\n",
       "      <td>0.373214</td>\n",
       "      <td>1.004456</td>\n",
       "      <td>0.679736</td>\n",
       "      <td>0.395961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.107036</td>\n",
       "      <td>0.102662</td>\n",
       "      <td>1.922574</td>\n",
       "      <td>1.897140</td>\n",
       "      <td>0.682537</td>\n",
       "      <td>3.549243</td>\n",
       "      <td>0.688963</td>\n",
       "      <td>2.903978</td>\n",
       "      <td>1.154619</td>\n",
       "      <td>1.427763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.464980</td>\n",
       "      <td>0.327778</td>\n",
       "      <td>0.429561</td>\n",
       "      <td>0.518806</td>\n",
       "      <td>0.361605</td>\n",
       "      <td>0.840107</td>\n",
       "      <td>1.703427</td>\n",
       "      <td>1.425120</td>\n",
       "      <td>0.348373</td>\n",
       "      <td>0.317244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.692157</td>\n",
       "      <td>4.491721</td>\n",
       "      <td>3.877759</td>\n",
       "      <td>1.356590</td>\n",
       "      <td>0.385702</td>\n",
       "      <td>0.841109</td>\n",
       "      <td>29.725924</td>\n",
       "      <td>7.645719</td>\n",
       "      <td>6.129338</td>\n",
       "      <td>19.808591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.530737</td>\n",
       "      <td>6.102039</td>\n",
       "      <td>0.667189</td>\n",
       "      <td>4.684959</td>\n",
       "      <td>2.038564</td>\n",
       "      <td>2.115949</td>\n",
       "      <td>0.459974</td>\n",
       "      <td>168.848143</td>\n",
       "      <td>3.779399</td>\n",
       "      <td>6.046556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.709248</td>\n",
       "      <td>0.022348</td>\n",
       "      <td>1.264393</td>\n",
       "      <td>3.315478</td>\n",
       "      <td>2.114878</td>\n",
       "      <td>3.942100</td>\n",
       "      <td>1.389129</td>\n",
       "      <td>10.902011</td>\n",
       "      <td>3.216001</td>\n",
       "      <td>46.565404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218428</td>\n",
       "      <td>5.136035</td>\n",
       "      <td>0.334521</td>\n",
       "      <td>3.747675</td>\n",
       "      <td>1.484503</td>\n",
       "      <td>1.304837</td>\n",
       "      <td>2.944983</td>\n",
       "      <td>11.822386</td>\n",
       "      <td>0.050060</td>\n",
       "      <td>1.116997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.425714</td>\n",
       "      <td>0.534419</td>\n",
       "      <td>0.194598</td>\n",
       "      <td>0.019526</td>\n",
       "      <td>0.216504</td>\n",
       "      <td>0.078877</td>\n",
       "      <td>0.200825</td>\n",
       "      <td>0.253237</td>\n",
       "      <td>0.075903</td>\n",
       "      <td>0.303022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456816</td>\n",
       "      <td>0.088400</td>\n",
       "      <td>0.318658</td>\n",
       "      <td>0.305282</td>\n",
       "      <td>0.408376</td>\n",
       "      <td>0.041530</td>\n",
       "      <td>0.104344</td>\n",
       "      <td>0.335817</td>\n",
       "      <td>0.379958</td>\n",
       "      <td>0.046544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.437613</td>\n",
       "      <td>0.429766</td>\n",
       "      <td>1.107128</td>\n",
       "      <td>0.024244</td>\n",
       "      <td>0.474435</td>\n",
       "      <td>0.052009</td>\n",
       "      <td>0.212094</td>\n",
       "      <td>0.153046</td>\n",
       "      <td>0.685267</td>\n",
       "      <td>0.613585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054376</td>\n",
       "      <td>0.031470</td>\n",
       "      <td>0.348879</td>\n",
       "      <td>0.211941</td>\n",
       "      <td>0.387865</td>\n",
       "      <td>0.014087</td>\n",
       "      <td>0.253319</td>\n",
       "      <td>0.281008</td>\n",
       "      <td>0.148084</td>\n",
       "      <td>0.003836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.061665</td>\n",
       "      <td>0.070004</td>\n",
       "      <td>0.169772</td>\n",
       "      <td>0.049300</td>\n",
       "      <td>0.306526</td>\n",
       "      <td>0.123650</td>\n",
       "      <td>0.170888</td>\n",
       "      <td>0.355494</td>\n",
       "      <td>0.085903</td>\n",
       "      <td>0.138304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116080</td>\n",
       "      <td>0.268594</td>\n",
       "      <td>0.147052</td>\n",
       "      <td>0.447942</td>\n",
       "      <td>0.113942</td>\n",
       "      <td>0.430175</td>\n",
       "      <td>0.493937</td>\n",
       "      <td>0.182145</td>\n",
       "      <td>0.343693</td>\n",
       "      <td>0.212337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.342766</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.625034</td>\n",
       "      <td>0.354473</td>\n",
       "      <td>1.126406</td>\n",
       "      <td>0.244788</td>\n",
       "      <td>0.432171</td>\n",
       "      <td>1.099352</td>\n",
       "      <td>0.428398</td>\n",
       "      <td>0.383964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.435375</td>\n",
       "      <td>0.499546</td>\n",
       "      <td>0.489049</td>\n",
       "      <td>0.832218</td>\n",
       "      <td>0.295888</td>\n",
       "      <td>1.440390</td>\n",
       "      <td>0.688297</td>\n",
       "      <td>0.777424</td>\n",
       "      <td>0.822837</td>\n",
       "      <td>0.497085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>46.255221</td>\n",
       "      <td>7.305433</td>\n",
       "      <td>2.136083</td>\n",
       "      <td>94.906857</td>\n",
       "      <td>1.707727</td>\n",
       "      <td>105.494660</td>\n",
       "      <td>5.501264</td>\n",
       "      <td>7.603232</td>\n",
       "      <td>11.526338</td>\n",
       "      <td>106.255588</td>\n",
       "      <td>...</td>\n",
       "      <td>2.722999</td>\n",
       "      <td>12.253605</td>\n",
       "      <td>3.576081</td>\n",
       "      <td>0.804224</td>\n",
       "      <td>10.448227</td>\n",
       "      <td>59.698819</td>\n",
       "      <td>41.118459</td>\n",
       "      <td>3.446718</td>\n",
       "      <td>8.427583</td>\n",
       "      <td>5.241904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          S0        S1        S2         S3        S4          S5         S6  \\\n",
       "0   0.379329  2.125491  2.087712   2.870420  1.025765    8.565928   3.916336   \n",
       "1   0.606003  0.472983  0.748652   0.844993  0.811071    0.254297   0.499367   \n",
       "2   4.107036  0.102662  1.922574   1.897140  0.682537    3.549243   0.688963   \n",
       "3   6.692157  4.491721  3.877759   1.356590  0.385702    0.841109  29.725924   \n",
       "4  16.709248  0.022348  1.264393   3.315478  2.114878    3.942100   1.389129   \n",
       "5   0.425714  0.534419  0.194598   0.019526  0.216504    0.078877   0.200825   \n",
       "6   0.437613  0.429766  1.107128   0.024244  0.474435    0.052009   0.212094   \n",
       "7   0.061665  0.070004  0.169772   0.049300  0.306526    0.123650   0.170888   \n",
       "8   0.342766  0.255247  0.625034   0.354473  1.126406    0.244788   0.432171   \n",
       "9  46.255221  7.305433  2.136083  94.906857  1.707727  105.494660   5.501264   \n",
       "\n",
       "          S7         S8          S9  ...      S140       S141      S142  \\\n",
       "0   3.530386   1.530494    0.846550  ...  3.794360   2.912683  3.019321   \n",
       "1   0.768882   1.135604    0.800742  ...  0.215083   0.286355  0.796666   \n",
       "2   2.903978   1.154619    1.427763  ...  0.464980   0.327778  0.429561   \n",
       "3   7.645719   6.129338   19.808591  ...  0.530737   6.102039  0.667189   \n",
       "4  10.902011   3.216001   46.565404  ...  0.218428   5.136035  0.334521   \n",
       "5   0.253237   0.075903    0.303022  ...  0.456816   0.088400  0.318658   \n",
       "6   0.153046   0.685267    0.613585  ...  0.054376   0.031470  0.348879   \n",
       "7   0.355494   0.085903    0.138304  ...  0.116080   0.268594  0.147052   \n",
       "8   1.099352   0.428398    0.383964  ...  0.435375   0.499546  0.489049   \n",
       "9   7.603232  11.526338  106.255588  ...  2.722999  12.253605  3.576081   \n",
       "\n",
       "       S143       S144       S145       S146        S147      S148      S149  \n",
       "0  1.806374   0.900212   0.673878   1.187524    1.358361  2.597537  1.725046  \n",
       "1  0.956499   0.507093   1.484827   0.373214    1.004456  0.679736  0.395961  \n",
       "2  0.518806   0.361605   0.840107   1.703427    1.425120  0.348373  0.317244  \n",
       "3  4.684959   2.038564   2.115949   0.459974  168.848143  3.779399  6.046556  \n",
       "4  3.747675   1.484503   1.304837   2.944983   11.822386  0.050060  1.116997  \n",
       "5  0.305282   0.408376   0.041530   0.104344    0.335817  0.379958  0.046544  \n",
       "6  0.211941   0.387865   0.014087   0.253319    0.281008  0.148084  0.003836  \n",
       "7  0.447942   0.113942   0.430175   0.493937    0.182145  0.343693  0.212337  \n",
       "8  0.832218   0.295888   1.440390   0.688297    0.777424  0.822837  0.497085  \n",
       "9  0.804224  10.448227  59.698819  41.118459    3.446718  8.427583  5.241904  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "absolute_abundance.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c393ffdb",
   "metadata": {},
   "source": [
    "Next we choose which taxa belong to which kingdom. Notice that our model assumes that covariances across kingdoms and within kingdoms are evenly distributed. In other words, it assumes that the only distinction between kingdoms is in how abundances are measured. To change this, one could use a different method of constructing the underlying covariance matrix (e.g. by choosing one kingdom's intra-kingdom edges to be denser than the others). We have not implemented this functionality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3395211c",
   "metadata": {},
   "outputs": [],
   "source": [
    "minsplit = int(0.2*N)\n",
    "maxsplit = int(0.8*N)\n",
    "splits = np.sort(np.random.choice(range(minsplit,maxsplit),size = ns-1,replace = False))\n",
    "\n",
    "spl_i = np.concatenate([[0],splits,[N]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d58d49e",
   "metadata": {},
   "source": [
    "Finally, we simulate generating reads for each data type. This process is slow, so we run in parallel with the ```simulate_all_reads``` function. Each read for each sample for each data type is accomplished by drawing from the distribution defined by the true relative abundances (within a kingdom) of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dce03e2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[simulate_all_reads] Drawing Read Assignments for 133 taxa, 150 samples.\n",
      "[simulate_all_reads] Drawing Read Assignments for 367 taxa, 150 samples.\n"
     ]
    }
   ],
   "source": [
    "read_depths = {}\n",
    "sep_reads = {}\n",
    "reads_order = []\n",
    "combined_reads = pd.DataFrame()\n",
    "for j in range(1,ns+1):\n",
    "    dpths = np.random.normal(avgdpth,stddpth,size = M).astype(int)\n",
    "    read_depths[\"Set{}\".format(j)] = dpths\n",
    "    rd = simulate_all_reads(absolute_abundance.iloc[spl_i[j-1]:spl_i[j]],dpths,nj = simulate_reads_tasks)\n",
    "    sep_reads[\"Set{}\".format(j)] = rd\n",
    "    reads_order += [(spl_i[j-1],spl_i[j])]\n",
    "    combined_reads = pd.concat([combined_reads,rd])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a968ca42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S0</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>S7</th>\n",
       "      <th>S8</th>\n",
       "      <th>S9</th>\n",
       "      <th>...</th>\n",
       "      <th>S140</th>\n",
       "      <th>S141</th>\n",
       "      <th>S142</th>\n",
       "      <th>S143</th>\n",
       "      <th>S144</th>\n",
       "      <th>S145</th>\n",
       "      <th>S146</th>\n",
       "      <th>S147</th>\n",
       "      <th>S148</th>\n",
       "      <th>S149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>669</td>\n",
       "      <td>524</td>\n",
       "      <td>385</td>\n",
       "      <td>70</td>\n",
       "      <td>745</td>\n",
       "      <td>988</td>\n",
       "      <td>414</td>\n",
       "      <td>247</td>\n",
       "      <td>143</td>\n",
       "      <td>...</td>\n",
       "      <td>406</td>\n",
       "      <td>408</td>\n",
       "      <td>778</td>\n",
       "      <td>171</td>\n",
       "      <td>194</td>\n",
       "      <td>98</td>\n",
       "      <td>391</td>\n",
       "      <td>148</td>\n",
       "      <td>610</td>\n",
       "      <td>430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>154</td>\n",
       "      <td>208</td>\n",
       "      <td>110</td>\n",
       "      <td>58</td>\n",
       "      <td>20</td>\n",
       "      <td>140</td>\n",
       "      <td>76</td>\n",
       "      <td>173</td>\n",
       "      <td>184</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>210</td>\n",
       "      <td>88</td>\n",
       "      <td>133</td>\n",
       "      <td>212</td>\n",
       "      <td>134</td>\n",
       "      <td>100</td>\n",
       "      <td>170</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>734</td>\n",
       "      <td>31</td>\n",
       "      <td>487</td>\n",
       "      <td>240</td>\n",
       "      <td>40</td>\n",
       "      <td>267</td>\n",
       "      <td>140</td>\n",
       "      <td>334</td>\n",
       "      <td>184</td>\n",
       "      <td>256</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>45</td>\n",
       "      <td>86</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>118</td>\n",
       "      <td>613</td>\n",
       "      <td>173</td>\n",
       "      <td>77</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1189</td>\n",
       "      <td>1435</td>\n",
       "      <td>1006</td>\n",
       "      <td>176</td>\n",
       "      <td>28</td>\n",
       "      <td>58</td>\n",
       "      <td>7414</td>\n",
       "      <td>929</td>\n",
       "      <td>1012</td>\n",
       "      <td>3737</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>829</td>\n",
       "      <td>185</td>\n",
       "      <td>437</td>\n",
       "      <td>489</td>\n",
       "      <td>351</td>\n",
       "      <td>149</td>\n",
       "      <td>18581</td>\n",
       "      <td>925</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2936</td>\n",
       "      <td>9</td>\n",
       "      <td>323</td>\n",
       "      <td>444</td>\n",
       "      <td>177</td>\n",
       "      <td>319</td>\n",
       "      <td>332</td>\n",
       "      <td>1299</td>\n",
       "      <td>515</td>\n",
       "      <td>9126</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>679</td>\n",
       "      <td>79</td>\n",
       "      <td>342</td>\n",
       "      <td>312</td>\n",
       "      <td>198</td>\n",
       "      <td>1002</td>\n",
       "      <td>1275</td>\n",
       "      <td>8</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>73</td>\n",
       "      <td>151</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>67</td>\n",
       "      <td>27</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>105</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86</td>\n",
       "      <td>134</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>51</td>\n",
       "      <td>21</td>\n",
       "      <td>106</td>\n",
       "      <td>121</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>19</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>53</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
       "      <td>174</td>\n",
       "      <td>21</td>\n",
       "      <td>99</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>155</td>\n",
       "      <td>59</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>105</td>\n",
       "      <td>139</td>\n",
       "      <td>68</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>67</td>\n",
       "      <td>127</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>200</td>\n",
       "      <td>232</td>\n",
       "      <td>81</td>\n",
       "      <td>191</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8136</td>\n",
       "      <td>2259</td>\n",
       "      <td>554</td>\n",
       "      <td>13114</td>\n",
       "      <td>110</td>\n",
       "      <td>8760</td>\n",
       "      <td>1383</td>\n",
       "      <td>889</td>\n",
       "      <td>1814</td>\n",
       "      <td>20672</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>1617</td>\n",
       "      <td>910</td>\n",
       "      <td>83</td>\n",
       "      <td>2279</td>\n",
       "      <td>9024</td>\n",
       "      <td>14239</td>\n",
       "      <td>376</td>\n",
       "      <td>2099</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     S0    S1    S2     S3   S4    S5    S6    S7    S8     S9  ...  S140  \\\n",
       "0    80   669   524    385   70   745   988   414   247    143  ...   406   \n",
       "1   110   154   208    110   58    20   140    76   173    184  ...    19   \n",
       "2   734    31   487    240   40   267   140   334   184    256  ...    55   \n",
       "3  1189  1435  1006    176   28    58  7414   929  1012   3737  ...    69   \n",
       "4  2936     9   323    444  177   319   332  1299   515   9126  ...    24   \n",
       "5    73   151    52      4   18    10    54    28    11     55  ...    45   \n",
       "6    86   134   296      4   41     8    51    21   106    121  ...     5   \n",
       "7    17    21    51      6   18    11    53    42     8     29  ...    11   \n",
       "8    65    88   155     59   84    22   105   139    68    104  ...    46   \n",
       "9  8136  2259   554  13114  110  8760  1383   889  1814  20672  ...   253   \n",
       "\n",
       "   S141  S142  S143  S144  S145   S146   S147  S148  S149  \n",
       "0   408   778   171   194    98    391    148   610   430  \n",
       "1    40   210    88   133   212    134    100   170   105  \n",
       "2    45    86    47    71   118    613    173    77    87  \n",
       "3   829   185   437   489   351    149  18581   925  1504  \n",
       "4   679    79   342   312   198   1002   1275     8   303  \n",
       "5    11    67    27   104     1     48     49   105    12  \n",
       "6     0    99    19    81     3     99     22    36     0  \n",
       "7    30    35    28    32    59    174     21    99    60  \n",
       "8    67   127    92    78   200    232     81   191   113  \n",
       "9  1617   910    83  2279  9024  14239    376  2099  1300  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep_reads[\"Set1\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c1d3bc77",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11</td>\n",
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       "      <th>135</th>\n",
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       "      <td>22</td>\n",
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       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>431</td>\n",
       "      <td>73</td>\n",
       "      <td>184</td>\n",
       "      <td>319</td>\n",
       "      <td>226</td>\n",
       "      <td>812</td>\n",
       "      <td>141</td>\n",
       "      <td>233</td>\n",
       "      <td>201</td>\n",
       "      <td>475</td>\n",
       "      <td>...</td>\n",
       "      <td>1912</td>\n",
       "      <td>314</td>\n",
       "      <td>516</td>\n",
       "      <td>241</td>\n",
       "      <td>361</td>\n",
       "      <td>640</td>\n",
       "      <td>230</td>\n",
       "      <td>576</td>\n",
       "      <td>134</td>\n",
       "      <td>121</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>78</td>\n",
       "      <td>122</td>\n",
       "      <td>83</td>\n",
       "      <td>275</td>\n",
       "      <td>102</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>137</td>\n",
       "      <td>132</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>71</td>\n",
       "      <td>108</td>\n",
       "      <td>153</td>\n",
       "      <td>84</td>\n",
       "      <td>116</td>\n",
       "      <td>153</td>\n",
       "      <td>43</td>\n",
       "      <td>92</td>\n",
       "      <td>108</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>56</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>125</td>\n",
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       "      <td>24</td>\n",
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       "      <td>7</td>\n",
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       "      <td>56</td>\n",
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       "      <td>15</td>\n",
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       "      <td>12</td>\n",
       "      <td>20</td>\n",
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       "      <th>139</th>\n",
       "      <td>314</td>\n",
       "      <td>13</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>116</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>77</td>\n",
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       "      <td>30</td>\n",
       "      <td>47</td>\n",
       "      <td>179</td>\n",
       "      <td>178</td>\n",
       "      <td>81</td>\n",
       "      <td>13</td>\n",
       "      <td>74</td>\n",
       "      <td>47</td>\n",
       "      <td>312</td>\n",
       "      <td>57</td>\n",
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       "      <th>140</th>\n",
       "      <td>26</td>\n",
       "      <td>116</td>\n",
       "      <td>84</td>\n",
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       "      <td>38</td>\n",
       "      <td>45</td>\n",
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       "      <td>33</td>\n",
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       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>225</td>\n",
       "      <td>269</td>\n",
       "      <td>57</td>\n",
       "      <td>32</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>72</td>\n",
       "      <td>262</td>\n",
       "      <td>241</td>\n",
       "      <td>305</td>\n",
       "      <td>81</td>\n",
       "      <td>284</td>\n",
       "      <td>302</td>\n",
       "      <td>189</td>\n",
       "      <td>108</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
       "      <td>186</td>\n",
       "      <td>96</td>\n",
       "      <td>143</td>\n",
       "      <td>119</td>\n",
       "      <td>366</td>\n",
       "      <td>301</td>\n",
       "      <td>207</td>\n",
       "      <td>281</td>\n",
       "      <td>56</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>105</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S0   S1   S2   S3   S4   S5   S6   S7   S8   S9  ...  S140  S141  S142  \\\n",
       "133   76   24   56   66   64   34   26   86   25  106  ...    61    21    43   \n",
       "134    4   43    0    4    4    8   12   33   19  287  ...    18     7     9   \n",
       "135   46   19    8   52   13    7    8    3    4   11  ...    14    22    22   \n",
       "136  431   73  184  319  226  812  141  233  201  475  ...  1912   314   516   \n",
       "137   78  122   83  275  102   49   66  137  132  109  ...   145    71   108   \n",
       "138   56  111    3   66   36    6  125    7   24  139  ...   265     7   130   \n",
       "139  314   13   92   85   33  116   56   35   22   77  ...    30    47   179   \n",
       "140   26  116   84  117  275   38   45   56   33   12  ...   127    60    68   \n",
       "141   72  262  241  305   81  284  302  189  108  126  ...   186    96   143   \n",
       "142    0   29   19    0    6    1  187   19    0   28  ...    21     1     2   \n",
       "\n",
       "     S143  S144  S145  S146  S147  S148  S149  \n",
       "133    38   101    12    25    58     7    38  \n",
       "134     2    11     1    20    10     6     3  \n",
       "135    17     0    73    56    14     7    26  \n",
       "136   241   361   640   230   576   134   121  \n",
       "137   153    84   116   153    43    92   108  \n",
       "138    56   129    15     1    51    12    20  \n",
       "139   178    81    13    74    47   312    57  \n",
       "140   225   269    57    32   112    21    50  \n",
       "141   119   366   301   207   281    56   208  \n",
       "142    12     1     5    67   105   128     1  \n",
       "\n",
       "[10 rows x 150 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep_reads[\"Set2\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba69e442",
   "metadata": {},
   "outputs": [],
   "source": []
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