doc/analyses_from_paper/analysis_of_prior_literature.ipynb

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    "# Analysis of prior literature"
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    "Here, we look into CCAs reported in the literature. We analyze sample-size dependence of reported association strengths and estimate the corresopnding weight errors."
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    "## Setup"
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       "       style='height:15px; border-radius:12px; display: inline-block; float: left'></img>\n",
       "  \n",
       "\n",
       "\n",
       "</div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from gemmr.metrics import mk_weightError\n",
    "from gemmr.data import load_outcomes, print_ds_stats, load_metaanalysis, load_metaanalysis_outcomes\n",
    "\n",
    "import holoviews as hv\n",
    "from holoviews import opts, dim\n",
    "hv.extension('matplotlib')\n",
    "hv.renderer('matplotlib').param.set_param(dpi=120)\n",
    "\n",
    "from my_config import *\n",
    "\n",
    "import warnings\n",
    "# holoviews emits this for log-linear plots\n",
    "warnings.filterwarnings(\n",
    "    'ignore', 'aspect is not supported for Axes with xscale=log, yscale=linear', category=UserWarning\n",
    ") \n",
    "warnings.filterwarnings(\n",
    "    'ignore', 'aspect is not supported for Axes with xscale=linear, yscale=log', category=UserWarning\n",
    ")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will be populated below and then written to disk\n",
    "plotted_data = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCAs in literature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metaanalysis_data = load_metaanalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PMID', 'Title', 'Authors', 'Citation', 'First Author', 'Journal/Book',\n",
       "       'Publication Year', 'Create Date', 'PMCID', 'NIHMS ID', 'DOI',\n",
       "       'CCA variant', 'n', 'n unit', 'subject_groups', 'is_healthy', 'dataset',\n",
       "       'n_sites', 'brain-behavior association', 'X modality', 'Y modality',\n",
       "       'pX', 'pY', 'Comment', 'latent association stengths reported', 'r1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metaanalysis_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're extracting studies that used CCA to analyze brain-behavior relationships, and reported the used sample size, the number of features in the data and the estimated canonical correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def try_float(r):\n",
    "    try:\n",
    "        return float(r)\n",
    "    except:\n",
    "        return np.nan\n",
    "    \n",
    "    \n",
    "cond_cca_bba = (\n",
    "    (metaanalysis_data['CCA variant'] == 'CCA') & \n",
    "    (metaanalysis_data['brain-behavior association'] == 'yes')\n",
    ")\n",
    "cond_npr = (\n",
    "    np.isfinite(metaanalysis_data['n'].apply(try_float)) &\n",
    "    np.isfinite(metaanalysis_data['pX'].apply(try_float)) &\n",
    "    np.isfinite(metaanalysis_data['pY'].apply(try_float)) &\n",
    "    np.isfinite(metaanalysis_data['r1'].apply(try_float))\n",
    ")\n",
    "\n",
    "\n",
    "metaanalysis_data_ = metaanalysis_data[cond_cca_bba & cond_npr].copy()\n",
    "metaanalysis_data_.loc[:, 'r1'] = metaanalysis_data_['r1'].astype(float)\n",
    "metaanalysis_data_.loc[:, 'pX'] = metaanalysis_data_['pX'].astype(int)\n",
    "metaanalysis_data_.loc[:, 'pY'] = metaanalysis_data_['pY'].astype(int)\n",
    "metaanalysis_data_.loc[:, 'n'] = metaanalysis_data_['n'].astype(int)\n",
    "\n",
    "assert len(metaanalysis_data_['DOI']) == len(metaanalysis_data_['DOI'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How popular has CCA been in the brain-behavior literature over time? For this analysis only we consider all CCA studies, independent of if they reported sample size, number of features and canonical correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Histogram   [Year]   (Num CCA studies of\n",
       "brain behavior associations)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_years = metaanalysis_data[cond_cca_bba]['Publication Year'].groupby(metaanalysis_data[cond_cca_bba]['DOI']).mean().values\n",
    "hist, edges = np.histogram(pub_years, bins=np.arange(pub_years.min(), pub_years.max()+2))\n",
    "panel_nCCAStudies_by_year = hv.Histogram(\n",
    "    (edges, hist)\n",
    ").redim(\n",
    "    x='Year',\n",
    "    Frequency='Num CCA studies of\\nbrain behavior associations'\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Histogram(fig_inches=(1.7, None))\n",
    ")\n",
    "\n",
    "hv.save(panel_nCCAStudies_by_year, 'fig/figS_nCCAStudies_by_year.pdf')\n",
    "\n",
    "panel_nCCAStudies_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here on, we only consider studies that *do* report sample size, number of features and canonical correlation. How many are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_publications = 31\n",
      "n_CCAs = 100\n"
     ]
    }
   ],
   "source": [
    "print('n_publications =', len(metaanalysis_data_['DOI'].unique()))\n",
    "print('n_CCAs =', len(metaanalysis_data_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many samples per feature studies typically use (if a publication reports several CCAs with the same number of samples and the same number of features for both datasets we count it as one here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Histogram   [Samples per feature]   (Num. reported CCAs)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_per_ftrs = []\n",
    "for doi, df in metaanalysis_data_.groupby('DOI'):\n",
    "    n_per_ftrs += (df['n'] / (df['pX'] + df['pY'])).unique().tolist()\n",
    "n_per_ftrs = np.array(n_per_ftrs)\n",
    "\n",
    "# # Alternatively, count each reported CCA, even if it there are multiple ones from the same study\n",
    "# # with the same number of samples and the same number of features in both datasets\n",
    "# n_per_ftrs = (metaanalysis_data_['n'] / (metaanalysis_data_['pX'] + metaanalysis_data_['pY']))\n",
    "\n",
    "log_n_per_ftrs = np.log(n_per_ftrs)\n",
    "\n",
    "bins = np.sort(list(set(np.logspace(0, log_n_per_ftrs.max()*1.01, 10, base=np.exp(1), dtype=int).tolist())))\n",
    "bins = np.log(bins)\n",
    "counts, bins = np.histogram(log_n_per_ftrs, bins=bins, density=False)\n",
    "bins = np.exp(bins)\n",
    "\n",
    "panel_n_per_ftr_hist = hv.Histogram(\n",
    "    ((bins, counts))\n",
    ").redim(\n",
    "    x='Samples per feature',\n",
    "    Frequency='Num. reported CCAs'\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Histogram(logx=True, fig_inches=(1.7, None), xlim=(1, 100)),\n",
    ")\n",
    "\n",
    "# hv.save(panel_n_per_ftr_hist, 'fig/figS_metaanalysis_n_per_ftr_histogram.pdf')\n",
    "\n",
    "panel_n_per_ftr_hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also see how the reported canonical correlations relate to the samples-per-feature of the used data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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5rfHvmWiFBQUFDi8+1SqRSrVq2CWCxGYWEhQkJC0L17dxgYGKgcQ/EjwFeuXIGtra1QfuPGDQBA27ZtVa6zptm9ezf09CpuBr2dO3ciLi4OABAVFYX+/ftX2L5qJG13SfLz86lBgwbk6ekpd5Lv8OHDBIAOHTqksI1EIqGBAweSiYkJHT9+nI4ePUpGRkY0cODAd96jruzQQCqVUr169Wjs2LFy5V988QU5OjqWo3Wlqy6HBiWBCocBytYtXj8zM5NsbW2FMhcXF6VXjTQRR01VJf5Ftm7dSgCof//+FBYWRiEhIWRpaUm9evVSuAIgk8nI29ubTExMhMuXRCQkAx8fH5WvGhARrVu3jgDQjBkz6ODBg+Tn50cikYjCwsI01s63cSIofd3i9V+8eEGWlpZCmYODA2VnZ1dIHDVVlfkX2b9/P7Vp04YMDAyoQYMG5OfnV+JlpPXr19OxY8cUyo8ePUobN24sdT8lJQKiotuPmzZtSoaGhtSiRQvasWOH6g1RASeC0td9c30bGxu5HkFFxVFTqTVUGdMMXZnXoKzu3buHAwcOYNasWQCUnwBU588tKioKH374oVzZ1atXyzyJrKbiqM5UOlno6+v7znXWrl2rdjBMt/n7++P48eMYPnw4nJycNPZl8/T0lPusp6eHZs2aaaRuVkSl07y5ubnvfLGa6eDBgzh8+DBycnLw9ddfa7Ruc3NzTJgwQfj8008/qXR1qPhqA1B0WZp7A0po8jjj/Pnzmqyu2qsu5whkMhk1btxY7hi8LDd3qcrIyIgsLCxU3q5jx45ysYWHh2s8Nl2n1n0EeXl5CAsLQ2pqqlzZokWLkJOTU/7sxHSKSCRCnz59sG7dOgBAgwYN0Lp1a43vZ+/evXL3eZTVhx9+iPPnzwMA6tSpg86dO2s6NN2nTvaYNGkSOTo6kqurKzVp0oRat25NBgYGKg/kUdNVlx4BEdHz58/J2tqaAKj1FGpFysnJIScnJ7UeIKsp1LoVLCIiAsePH8euXbvQoUMHXLp0Cb/88otcD4HVLJaWlggODka3bt0wZswYbYcjx9jYGCtXrkTLli3h5+en7XCqJnWyh4mJCRUUFFBGRga1bNmSiIjS09OpYcOGGs1S1VV1evrwTTKZjO7fv6/tMEqUnJysUFaTJzV5k1o9Ag8PD6xfvx6mpqbIzMxESkoKXr58iaysLI0mqeoqMDAQRITExERth6JRIpEIjo6O2g6jRG8PrpucnIxBgwZpJ5gqRq1EsHz5cqxYsQIXL17E+PHj4erqig4dOigdyZaxqmr69On4+++/sXPnTm2HonUaubPw+PHjyMvLU7j7i5Wuut1ZqEsOHToEHx8fAEVP0966dQtmZmZajkp71OoR5OTkICAgQBh/4NatW7h27RrfqMEqXUFBgVrbLVmyRHifkpKC7du3ayoknaRWIvjiiy9w/vx54TZPd3d3bN68Gf/5z380Ghxj77J8+XLs2rVL5e1mzJghvLexscHo0aM1GZbuUecMo6WlJb148UKu7P79+2Rra1vus5c1SXW6j0AbHj58SKamptSwYUPKzMxUefv+/fsTANq8ebPmg9MxavUIjI2NFQ4DTE1NkZeXp4HUxFjZzJw5E1lZWXjy5AmCgoKUrpOenl7i9qtWrULPnj0xbty4igpRZ6iVCD755BMMGTIEx48fx6NHj3D+/HmMHTsW3t7emo6PMaXu3r0rN3bh6tWr8fr1a7l1UlJS0KlTJ7x8+VJpHS4uLjh06JBKYytWV2olguDgYHTo0AGDBg2Co6MjunbtCisrK6xevVrT8TGmlIuLi9xVqs8++0zhrP+sWbOQlJSE7777rsR63hystiYr9+XD9PR01K1bV24mW1Y2fPmwfG7fvg13d3cUFBTg8uXLaNWqlbDsxIkT8PLyAlA0fsG5c+fQvn17LUVa9an09OG0adOwevXqUgcoqekDk+Tl5aFx48ZITEyEhYWFtsOp1lxcXGBtbY2nT59i4cKF+OOPP4Rlb94kJJPJsGvXLk4EpVB5YJLi//LAJIqCgoLQqVMnPH78WNuh1AgbNmzA06dPAQB79uxBTEyMsGzOnDnC8Or6+voICAjQSow6Q8tXLZSaPHkyAaD8/HyN1/3bb7+VOHjpli1byM3NjYyMjKhZs2YqP1Z9+PBhOnDgQI2a+1CbpkyZIvfw1tKlS4Vlv/76q9yynTt3ajHSqk+tgUlyc3Oxfv163Lt3T+HOrvIeGhw9elQY4ELTHj16hGXLlildtmPHDowfPx5TpkxB3759ceHCBXz99dfIyckp841SPOlG5Zo/fz7++OMPvHz5Em5ubvD39xeWvd0r417aO6iTPYYNG0Z2dnY0YsQIGj9+vNyrPF6+fEn29vbCIBKa6hEcO3aMWrZsSfr6+gRAoUdQWFhIjo6ONHz4cLlyf39/MjMzo6ysLJX2B+4RVJq1a9cSAIqKipIrz87OJkdHRwJAbm5uCpP2Mnlqz4b86NEjTcdCY8aMoe7du9NPP/1UaiJ49uyZ0plucnJyKDU1VaE8MTGRgoODKTg4mNq1a6eQCC5cuEAAKDIyUq48KiqKANCRI0eIiGjhwoUkFosVXqGhoXLbcSKoPIWFhbRo0SKly/bv3680STBFaiWCFi1alOkPXRXh4eEkFovp3r17wsAdyhKBTCajDh06CFOiFyueGr1jx44qz3S0adMmAkAPHz6UK09JSVFreKt3JYLqNjCJVCql3377TdthKFVV46pq1LqhaNasWRg7diyuX7+O1NRUuZc60tLSMHnyZCxevBiNGzcudV2RSITly5cjLi4OQ4YMgVQqRW5uLj766CP8888/WLZsmcp3ihXfhmplZSVXXjxLcvGsyJpS3QYmWbJkCaZNmyZMGluVTJ48Wdsh6AS1ThZOnDgRAITHkEUiEYgIIpEIhYWFKtc3ZcoUuLu7l2kCFaBobPrDhw9jwIABGDp0KPLz83HmzBkcOXJEranR356JuVhxQilpRuaSUA16HPvBgwdYtGgR8vPzMW3aNERHR2s7pFLt27cPffv2rdFjDyijViJITk7WWAC7du3CkSNHcPbsWWRnZwOA8PBSdnY2DA0NYWRkpLDdBx98gPDwcPTt2xdA0dUGdZIA8L9f/oyMDLlbTot7AtbW1mrVWxN88803whD2MTEx2LdvH4YNG6blqJRLT0/HpEmTMHHiRLnxCJiazxo4OTnB1NQUp06dwr59+3Dp0iU0bNgQTk5OKtd19uxZ5ObmolWrVjAzM4OZmRmCg4MBABYWFhg5cqTS7aRSKVatWgWxWAwjIyOEhISo/fRj8e29V65ckSsv7uq2bdtWrXprgjcP5fT09NCoUSMtRlO6b7/9Fi9fvkRISAiuX7+u7XCqFnVOLFy8eJFsbGyoVatW1KdPH3J0dCQnJye6fPmyynUlJSXRyZMn5V6ff/45AaDjx4/T9evXFbaRSCQ0cOBAMjExoePHjwtTog8cOPCdo9IqO1kolUqpXr16NHbsWLnyL774ghwdHVVuU1lVh6sGWVlZZG9vTwBoypQp2g6nRGfPniWRSCScoO3Ro0eZtwWq/2zKarWoV69eFBISIle2cOFC6t69uyZieudVA29vbzIxMaHY2FihvDgZ+Pj4qHzVgIho3bp1BIBmzJhBBw8eJD8/PxKJRBQWFqaRNilTHRIBEdGePXvI2tqanj9/ru1QSnTr1i0yMDAQvsgjRowo87acCEpgbm6ucJNNVlYWmZiYaCSo0hIBEdH69evp2LFjCuVHjx6ljRs3llp3SYmAqOhSU9OmTcnQ0JBatGhBO3bsUD14FVSXREBEFB8fr+0Q3mnOnDkEgExNTVW6D4YTQQmaNWum8Md78+ZNsre310hQNUV1SgS6IDs7mxwcHGjx4sVy5TKZrNS7R2tCIlDrqsGUKVMwaNAgzJkzB66urrh//z6Cg4MxduxYdapjrFKYmJggLCwMnTp1kivftm0bEhISsGrVKoVtqIIvBSu756Wi96mUuhlk7dq15OHhQSYmJtS4cWMKCgqqkKcFqzPuEWhfRkYG2djYkL6+Pl26dElh+caNGwkAffDBBxWyf1SR3kb16+PoEE4E2jdt2jThC9ilSxe5E80vXrwQZngGUCHnjKpKIlDr0CA9PR0LFy5U+hjy4cOH1amSsUonlUqxefNm4fPp06dx5coVtG7dGgCwd+9euVGQf/3111LnP6gy3Xw1qHVD0aefforY2Fg0b94cnTp1knsxpisMDQ3xzTffCJ+9vb2FJAAAY8aMgYODg/D5hx9+qNT4KpNag5eam5vj/v37PCZfOfHgpdonkUjg7u6OJ0+e4Nq1a8Kdkur8uutyj0CtQwMXFxfk5uZyImA6z8jICCtXrkRCQsI7n3ytztRKBBMmTMDgwYMxd+5chQdyPD09NRKYruJRjHWPj49PmYaZe/bsGerXr6/RfVeVXoRaiWDatGkAoDCzkbqPIVcXQUFBOHjwII+Pp4Nq1Xr3V6FBgwalfkl15TBAGbVOFspkMqUvdZNAbm4uvvnmG9jb28PIyAhNmjTB7NmzkZmZqVZ9pVm3bh3s7e2VLtu6dSuaN28OY2NjuLq6qjxzk6enJ+bOnauJMBmrVGr1CICiLnBGRgaAop6ARCKBt7c3Ll++rHJdX331FbZv346ZM2eiU6dOuHr1Kn7++WfcuXMHBw8eVDdEBTyKMWMlUOfmg7CwMDIwMCA9PT25V5cuXVSuKz09nWrVqkXff/+9XPmcOXNIT09P5RGEleFRjJmqoORGHzW/LirvRxvUOjRYuHAh1q1bh61bt2LMmDFISEhA//79S5yaujSpqalo3769wq+pmZkZZDIZpFKp0m2Ulefm5iItLU2h3MbGBp9++ikWLFiAdu3aKSy/fPkyHjx4gPHjx8uV+/j44PXr1zh58iQAYNGiRTA1NVV41fRp3pj6qOjuXrmXtgJRmYmJCRUWFtLjx4/p/fffJ6Ki573btWtXrqyUl5dHL1++pKioKLK1tSVvb2+FdXgUY8Y0T60egZWVFe7evQtbW1vcv38fAODo6IibN2+WKylFRESgbt266NOnD6RSKebPn6+wDo9izJjmqXWycPLkyfDy8sKJEyfQtm1bDB8+HDKZDC1btixXMD169EB0dDTu3LmDFStWoFu3brhw4QKaNm0qtx6PYsx0SVW5V6A0avUIvv/+e+zbtw/W1tb47bffYGJiAkNDQ+zYsaNcwVhZWaFXr16YMmUKoqOjkZWVhS1btihdt3gU48OHDyMqKgoHDhzQyCjGb+JRjFlNoVYiAID3338fdevWhbW1NbZu3YqwsDC4uLioXM/SpUthbW2NZ8+eyZU7ODjA3Nwcz58/V7odj2LMmOaonAiCg4PRr18/4fO+ffvg4OCA8PBwtQJo2rQpnj9/jqioKLnyhIQEvHr1SumXUCqV4uOPP0ZsbCwiIyPx559/IiYmBkOHDlUrGXTu3Bn16tVDWFiYXPn27dvh6OiINm3aqFwnYzpFlTOLW7duJXt7ezp69KhQlpmZSWvXriVzc3NhslBVFBYWUps2bcjY2JiCgoJo//799Msvv5CNjQ01b96ccnJy5NbnUYyZrkEVuVegNCpF1LFjR6WjBxMRbd++Xe3hnB4/fkyjR48ma2trqlWrFtnZ2dGUKVMoLS1N6fo8ijFjmqXSeAR169ZFamoqateurbAsMzMTdnZ2eP36tUZ6KjUBj0fAqgqVzhHIZLIST95JJBIYGBhoJCjGdJFIJFJ46QqVEkG7du1KvES4b98+eHh4aCQoxljlUumGooCAAAwdOhTZ2dkYPXo0HBwckJqait27dyMoKAj79u2rqDgZYxVIpUTQv39/rFmzBt988w3mzZsnlIvFYixZskTusiJjTHeofIvxxIkT8cknn+D06dN48eIF6tWrh86dO0MsFldEfIyxSqDWswampqbo27evpmNhjGmJ2iMUMcbkqXAlvspR+1kDxlj1wYmAMcaHBoypQxfGGFAF9wgYY5wIGKsMu3btqtKT/3AiYKyCJSUlYcKECVizZo22QykRJwLGKtiMGTMgkUgwd+5chZG4qgpOBIypgco4H0FkZCT++usvAEVjYAYEBFRmmGXGiYCxClSnTp1SP1cVnAg0LC8vD/b29ps1zC8AAA4uSURBVAojIrOa6e2RtdesWVMlxyngRKBBQUFB6NSpE0+LznROlUgEhYWFWLx4MVxdXWFoaIh69erhs88+q5ATKzwtOmOKqsSdhXPmzMHSpUvh7+8PLy8vXL9+HcHBwbh9+zbi4+M11pXiadEZK4HWhk39/3JyckgsFtPEiRPlyjds2EAA6P/+7//KvQ+eFp1pE3RgOHOtHxrcuXMH2dnZCqMbde7cGQCUThTK06IzXUJVZerz0mg1DREJ06A/f/5crrx4qvI//vhDrpynRWdM87TeI7CwsEDv3r1haWkplEVFRWHWrFlwdnbGwIED5dbnadEZ07wqcbKwWFpaGubMmYPff/8dTk5OiIiIUDoWIk+LzphmVZlEEBYWBj8/P2RlZWH69OmYP38+TE1NS1y/eFr04rETjx49qpFp0Y2NjYVynhad1RRaPzQAgLVr12L06NFo3rw5rly5ghUrVpSaBACeFp0xTdJ6IsjMzMTs2bPRvXt3HD9+HG5ubu/chqdFZ0yztH5oEBUVhZycHPTv3x8xMTEKy11cXODi4iJ8JiIMGzZMSALdu3cHAPz5558YNGgQhg0bhoiICJVOGBoYGGDBggWYPHkyLC0t4eXlhejoaGzatAk7d+4sfyMZq+K0ngiSk5MBAN9++63S5XPnzkVgYKDwWSQSYdCgQZg5cya8vLyE8j59+uDPP//EgwcP1LoTcdKkSSAiLFu2DL/++iuaNGmC7du3Y9SoUSrXVVbF90LcvXu3wvbBqrcmTZrAyMio3PWoNC0606yIiAgMHjxY22EwHZaYmCic4yoPTgRalJGRgRMnTsDBwQGGhobw8PCQu7dAlc/F7+/evYvBgwfj4MGDcodU6nh7f+quV9JyZeVlaeOb77XR3netW5nt5R5BNSQSieTuQVDlc/H7a9euCX805f2leHt/6q5X0nJl5WVp45vvtdHed62rK+19k37gmwfgTOvePO+h6mcvLy+kpaVh7dq18PX1hY2NjcbjUXe9kpYrK39XG998r632vmtdXWlvMe4RVDMV9YtRVXF7NUPr9xEwxrSPDw2qIbFYDC8vL6XPaVRH3N7y40MDxhgfGjDGOBEwxsCJgDEGTgSMMXAiYIyBEwFjDJwIaoRXr16hZ8+e6NixI/bv36/tcCqNTCaDj4+PtsOoFGlpaejTpw+6du2KlStXqrw9J4IaYMOGDfDz88Pp06dVnsZNV925cwetWrUShpur7tasWYMZM2bg1KlTiIyMVDrvR2k4EdQA8fHx6NWrFwwMDGBlZYXMzExth1ThXFxc8O+//8LJyUnboVQKT09PYfBePT09lYfs40Sgo5RNvx4WFoYmTZrA0dERo0aNQlZWFoCicSHNzc0BAObm5nj9+rVWYi4vVdosEolgZGRUJacgLytV2jtw4ECYmpoiMDAQtra2MDMzU2lfnAh0kLLp15OTkzFz5kzExMTg/v37MDExwcKFCwEAZmZmyM7OBgBkZWXBwsJCK3GXh6pt1nWqtjc3NxcDBgyAVCrFpk2bVN4fJwIdpGz69fDwcHh7e8PZ2RkikQiTJk1CeHg4AKBDhw44duwYcnNzkZOTo5MP56jaZl2nantXrlyJQYMGITg4GPr6+irvT+uDlzLVKZt+/d69e2jUqJHw2cnJCQ8fPgRQNDDrkCFDEBQUhPnz51danJqkapt1nartPXXqFF69eoU9e/YAKEoab04j+C6cCKoJkUiE2rVrC5/19PRQq1bR/14rKyvExcVpK7QKU1qbi0VHR1d2WBWmtPZGRkaWq24+NKgmnJyc8ODBA+Hzw4cPyz2YZ1VX09pcke3lRFBNfPzxx4iIiEBqaiqICKtWrarQORmqgprW5opsLyeCaqJJkyaYN28eunbtikaNGsHQ0BDTp0/XdlgVqqa1uSLbyyMUMca4R8AY40TAGAMnAsYYOBEwxsCJgDEGTgSMMXAiYIyBEwFjDJwIGGPgRMAYAycCnfPq1Sv4+fnB0dERxsbGcHFxQUBAQKWPQxgYGIh+/fpV6j7L4+bNm3Bzc4OBgUG563J2dsbu3bs1EFXVwYlAx3z55Zd4/Pgxzpw5g8zMTBw4cACnTp3C2LFjtR1alVFQUKBQdvbsWWRnZyM3N1cLERVRFldVwYlAx/z999+YPHky7O3tUbt2bbRs2RJr1qyR+6XbuHEjXFxcYGRkhMaNG2PFihXCMmdnZ/zwww9o164dTExMMGLECOzbtw/u7u6wtLTE5MmTUVhYCKBoIIyQkBA0b94c5ubmGD58ONLT05XGtWfPHnh4eMDExATdunXDhQsXhGVLliyBg4MD6tSpgw8++ABXr15VWoeXlxdmz56NHj16QCwWo3Pnzrh06ZKw/PLly+jevTvMzMzg5uaGsLAwuXYFBgaiRYsWCAgIUIhtwoQJePToEQwNDQEAOTk5mDp1Kho2bAhLS0v4+voK4zoWFhZi+vTpqF+/PkxMTNC+fXtERUUBKBr27f79+xg9ejSWL1+OLVu2wM3NTW5/pqamOH78eIlxxcbGomPHjhCLxWjbtq1Qt1YR0ykDBgwgFxcXWr58OZ07d46kUqnc8ocPH5KxsTGdOXOG8vPzKTo6mgDQvXv3iIjIycmJmjZtSnfv3qWkpCQSi8XUqlUrevDgAT148IAsLCzo0KFDREQEgDp16kRPnjyh58+fU79+/ejjjz8mIqK5c+dS3759iYgoKiqK6tevT/Hx8fT69WtavHgxWVlZUW5uLp08eZIsLCwoOTmZcnNz6fvvv6cWLVoobVv37t3J0tKSzp8/TxKJhH788UdycHCg7OxsSktLI0tLS9q2bRvl5OTQkSNHyNTUlM6ePSu0y9XVlW7duqW07s2bN5Orq6vwefTo0TR06FBKT0+nBw8eUJcuXcjX15eIiHbs2EHu7u704MEDkkqlFBQURA4ODsK2Tk5OtGvXLqX1EhGJxWKKjY1VGtf169epTp06dOTIEcrJyaFt27aRsbExPXr0qLT/7RWOE4GOef36NS1ZsoT69OlD5ubmZGhoSD169KBTp04Jyy9fvkwymYxSUlIoOjqa9PT06J9//iGioj/MZcuWCfU1b96cVq5cKXxu3749rV27loiKEkF4eLiwLDY2lmrVqkV5eXlyicDb25uCg4Pl4nR2dqaIiAiKi4uj2rVrU2hoKKWkpFBBQQGlp6crbVv37t3J399f+CyVSql27doUGxtLS5cuFfZXbNy4cfT1118L7QoNDS3x3+3NL2xaWhoBoJSUFLm21a1bl4iIHj9+TMnJyZSfn0///e9/afHixWRoaCisq2oieDMuPz8/mjx5skK7Q0JCSoy9MvChgQ4hIhgbG2PWrFk4evQoXr58iX///RfNmjVDv3798OrVKxQWFmLx4sWws7ODj48P9uzZA5lMJlePra2t8F5PT0+Y86BY8aEBALkJQpo0aYKCggKFw4OkpCT89NNPMDIyEl6PHj3Co0eP0K1bN4SFheHIkSNwdXVFy5YthQE2lXlzfwYGBnBwcEBKSgqSkpIQHR0tt4+wsDC54b7LOlhncnIygKJue3Fd/fr1Q2ZmJrKzs/Hy5UuMGzcOzs7OGDNmDM6dO1emeou9/e/9ZlxJSUnYtGmTXDvOnDkj1w5t4ESgQyIiItCgQQPhD00kEsHDwwOhoaHIycnBrVu3sHz5cty7dw/Jyck4f/48fv75Z4V6VJn0480x8pKTk2FoaAgrKyu5dWxtbbF8+XJIJBLhde7cOXz66ae4cuUKHBwc8Ndff+HFixcIDg6Gr68vkpKS3rk/iUSCp0+fws7ODra2thg8eLDcPm7cuIElS5aUuS1vxgsUzRdYXFdqairOnj0LsVgMX19ftGvXDo8ePcLJkycxevToEuvS09OT++JnZGSUekLS1tYW06dPl2vHpUuXMHPmTJXboUmcCHRI7969YWhoiIkTJyIpKQn5+fm4f/8+fvjhB1hbW8Pd3R15eXkgIkgkEty/fx9ff/019PT0VJ4Lr9iKFSuQmpqKlJQUfPfddxgyZIjCJbjx48cjNDQUt2/fhkQiwbp169CzZ08UFBTg0qVLGDFiBG7evAkiQn5+PoyMjBR6IcV2796NK1euIDs7GwEBAahXrx7ee+89jBw5EjExMYiJiUFeXh5OnjwJT09P3Lp1S+U22dvbo1evXvjhhx+QlZWFlJQUjBo1SjipmpeXh4KCAkilUly6dAnBwcGQyWTIz88HANSqVQtpaWkA/jeg6NOnTwEAq1atKnXf48aNw86dO5GQkACpVIqIiAh4enri+fPnKrdDo7R6YMJUlpycTGPHjiV7e3syNDQkBwcHmjhxIt2+fZuIiFJTU6lXr15kZGRErq6utHPnTho5ciSZmZkRkfzxLRGRu7s7bd68Wfjcvn17Wr16NREVnSP4z3/+Q05OTmRmZkajRo2ijIwMIpI/WUhEtHLlSmrUqBEZGxuTp6cnnT59moiICgsLaebMmWRra0vGxsbUsmVL+uuvv5S2rXv37jRu3Djq0KEDGRsbU9euXenq1avC8ujoaGrbti0ZGRmRi4sLbdiwQVj2drve9vax/LNnz2jkyJFkYWFBlpaWNG7cOHr16hUREcXHx1OLFi3IyMiIunbtSidPnqQmTZqQt7c3EREFBASQgYEBrV69mmQyGc2ePZucnZ2pTZs2NGfOHLKzs5M7R/B2XLt27SI3NzcyMjIiDw8PioiIKDHuysJjFrISiUQi/PPPP3jvvfcqZX9eXl7o168fvv3220rZH/sfPjRgjHEiYIzxcOaMMXCPgDEGTgSMMXAiYIyBEwFjDJwIGGPgRMAYAycCxhg4ETDGwImAMQZOBIwxAP8P1wpYEwXQFVgAAAAASUVORK5CYII=' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .Scatter.I  :Scatter   [Samples per feature]   (Canonical corr.)\n",
       "   .Scatter.II :Scatter   [Samples per feature]   (Canonical corr.)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_healthy_marker(s):\n",
    "    if s == 'yes':\n",
    "        return 's'\n",
    "    # elif s == 'no':\n",
    "    #     return 'x'\n",
    "    else:\n",
    "        return 'd'\n",
    "marker = metaanalysis_data_['is_healthy'].apply(is_healthy_marker)\n",
    "\n",
    "metaanalysis_n_per_ftr = (metaanalysis_data_['n'].astype(float) / (metaanalysis_data_['pX'].astype(float) + metaanalysis_data_['pY'].astype(float)))\n",
    "\n",
    "panel_metaanalysis_rObs_nPerFtr = hv.Overlay()\n",
    "for my_marker in np.unique(marker):\n",
    "    mask = (marker == my_marker)\n",
    "    panel_metaanalysis_rObs_nPerFtr *= hv.Scatter(\n",
    "        (metaanalysis_n_per_ftr[mask], metaanalysis_data_['r1'][mask]),\n",
    "        kdims=['Samples per feature'],\n",
    "        vdims='Canonical corr.',\n",
    "        # label='Literature',\n",
    "    ).opts(*fig_opts).opts(\n",
    "        logx=True, logy=True, color='black', s=10, fig_inches=(1.7, None), marker=my_marker\n",
    "    )\n",
    "\n",
    "# save plotted data\n",
    "_plotted_data = pd.DataFrame(\n",
    "    metaanalysis_data_['r1'][mask].values,\n",
    "    index=metaanalysis_n_per_ftr[mask].values\n",
    ")\n",
    "_plotted_data.columns = ['r']\n",
    "_plotted_data.index.names = ['n_per_ftr']\n",
    "plotted_data['CCAs_in_literature'] = _plotted_data\n",
    "\n",
    "panel_metaanalysis_rObs_nPerFtr.opts(*fig_opts).opts(\n",
    "    fig_inches=(1.7, None)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty linear. What's the corresponding $R^2$ value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.833416333963024"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.log(metaanalysis_n_per_ftr.values.reshape(-1, 1))\n",
    "y = np.log(metaanalysis_data_['r1'].values)\n",
    "\n",
    "lm = LinearRegression(fit_intercept=True).fit(X, y)\n",
    "log_nPerFtr_obsCorr_R2 = lm.score(X, y)\n",
    "log_nPerFtr_obsCorr_R2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's the relation between canonical correlation and samples per feature in synthetic data with known true correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from subfolder 'gemmr_latest'\n"
     ]
    }
   ],
   "source": [
    "data_home = None\n",
    "ds_cca = load_outcomes('sweep_cca_cca_0+0_litanaDenseSweep', data_home=data_home)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's in the simulated outcome data file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_modes\t\t 1\n",
      "n_rep\t\t 100\n",
      "n_perm\t\t 10\n",
      "n_per_ftr\t [  3   4   6   8  12  16  24  32  48  64  96 100]\n",
      "r\t\t [0.1 0.3 0.5]\n",
      "px\t\t [  2   4   8  16  32  64 128]\n",
      "ax+ay range\t(0.00, 0.00)\n",
      "py\t\t== px\n",
      "\n",
      "<xarray.DataArray 'n_Sigmas' (px: 7, r: 3)>\n",
      "array([[10, 10, 10],\n",
      "       [10, 10, 10],\n",
      "       [10, 10, 10],\n",
      "       [10, 10, 10],\n",
      "       [10, 10, 10],\n",
      "       [10, 10, 10],\n",
      "       [10, 10, 10]])\n",
      "Coordinates:\n",
      "  * r        (r) float64 0.1 0.3 0.5\n",
      "  * px       (px) int64 2 4 8 16 32 64 128\n",
      "\n",
      "power\t\tnot calculated\n"
     ]
    }
   ],
   "source": [
    "print_ds_stats(ds_cca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the relation between observed canonical correlation and samples per feature in synthetic data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .Curve.Perm                   :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_1 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_3 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_5 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Text.I                       :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.II                      :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.III                     :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.IV                      :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.V                       :Text   [Samples per feature,Observed correlation]"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel_synthetic = hv.Overlay()\n",
    "_r_clrs = ['grey'] + hv.Palette(cmap_r, samples=3).values\n",
    "\n",
    "_plotted_data = {}\n",
    "\n",
    "_ds_perm = ds_cca.between_assocs_perm \\\n",
    "    .mean('perm').mean('rep').mean('px').mean('Sigma_id').mean('r')\n",
    "_plotted_data['perm'] = _ds_perm.to_dataframe()['between_assocs_perm']\n",
    "panel_synthetic *= hv.Curve(\n",
    "    (_ds_perm.n_per_ftr, _ds_perm.values), label='perm'\n",
    ").opts(\n",
    "    logx=True, logy=True\n",
    ")\n",
    "\n",
    "for r in ds_cca.r.values:\n",
    "    _ds = ds_cca.between_assocs.sel(r=r).mean('rep').mean('px').mean('Sigma_id')\n",
    "    _plotted_data[r] = _ds.drop('r').to_dataframe()['between_assocs']\n",
    "    panel_synthetic *= hv.Curve(\n",
    "        (_ds.n_per_ftr, _ds.values), \n",
    "        label='$r=%.1f$' % r\n",
    "    ).opts(\n",
    "        logx=True, logy=True\n",
    "    )\n",
    "    \n",
    "# save plotted data\n",
    "_plotted_data = pd.concat(_plotted_data, axis=1)\n",
    "_plotted_data.columns.names = ['r_true']\n",
    "plotted_data['simulated_observed_correlations'] = _plotted_data\n",
    "\n",
    "    \n",
    "panel_synthetic *= hv.Text(1, 0.1, '0.5', halign='left', valign='bottom', fontsize=8).opts(color=_r_clrs[-1])\n",
    "panel_synthetic *= hv.Text(1, 0.1*1.3, '0.3', halign='left', valign='bottom', fontsize=8).opts(color=_r_clrs[-2])\n",
    "panel_synthetic *= hv.Text(1, 0.1*1.3*1.3, '0.1', halign='left', valign='bottom', fontsize=8).opts(color=_r_clrs[-3])\n",
    "panel_synthetic *= hv.Text(1, 0.1*1.3*1.3*1.3, 'perm', halign='left', valign='bottom', fontsize=8).opts(color=_r_clrs[-4])\n",
    "panel_synthetic *= hv.Text(1, 0.1*1.3*1.3*1.3*1.3, '$r_\\mathrm{true}=$', halign='left', valign='bottom', fontsize=8)\n",
    "\n",
    "panel_synthetic = panel_synthetic.redim(\n",
    "    x='Samples per feature',\n",
    "    y='Observed correlation'\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Curve(color=hv.Cycle(_r_clrs)),\n",
    "    opts.Overlay(logx=True, logy=True, show_legend=False, fig_inches=(1.7, None), yticks=3),\n",
    ")\n",
    "panel_synthetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's compare literature to synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .Curve.Perm                   :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_1 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_3 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Curve.R_equals_0_full_stop_5 :Curve   [Samples per feature]   (Observed correlation)\n",
       "   .Text.I                       :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.II                      :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.III                     :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.IV                      :Text   [Samples per feature,Observed correlation]\n",
       "   .Text.V                       :Text   [Samples per feature,Observed correlation]\n",
       "   .Scatter.I                    :Scatter   [Samples per feature]   (Canonical corr.)\n",
       "   .Scatter.II                   :Scatter   [Samples per feature]   (Canonical corr.)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = panel_synthetic * panel_metaanalysis_rObs_nPerFtr\n",
    "fig.opts(*fig_opts).opts(fig_inches=(1.7, None), show_legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate weight errors for reported CCAs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To estimate the weight errors for a reported CCA we use the number of features of the reported CCA, repeatedly generate synthetic data of the same sample size as the reported CCA, and sweep through the assumed true correlations. For each synthetic dataset we perform CCA. Whenever the estimated canonical correlation in the synthetic data matches the reported canonical correlation we calculate and record the weight error in the synthetic dataset. We then use the collection of recorded synthetic weight errors as an estimate for the weight error in the reported CCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](docfig/figS_metaanalysis_estimated_weight_error.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the description above, we have generate synthetic data for each combination of $p_X, p_Y$ and $n$ in our database and analyzed it with *gemmr*. The resulting datasets look, for example, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_modes\t\t 1\n",
      "n_rep\t\t 100\n",
      "n_per_ftr\t [6]\n",
      "r\t\t [0.   0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1  0.11 0.12 0.13\n",
      " 0.14 0.15 0.16 0.17 0.18 0.19 0.2  0.21 0.22 0.23 0.24 0.25 0.26 0.27\n",
      " 0.28 0.29 0.3  0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4  0.41\n",
      " 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5  0.51 0.52 0.53 0.54 0.55\n",
      " 0.56 0.57 0.58 0.59 0.6  0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69\n",
      " 0.7  0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8  0.81 0.82 0.83\n",
      " 0.84 0.85 0.86 0.87 0.88 0.89 0.9  0.91 0.92 0.93 0.94 0.95 0.96 0.97\n",
      " 0.98 0.99]\n",
      "px\t\t [8]\n",
      "ax+ay range\t(0.00, 0.00)\n",
      "py\t\t!= px\n",
      "\n",
      "<xarray.DataArray 'n_Sigmas' (px: 1, r: 100)>\n",
      "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n",
      "Coordinates:\n",
      "  * r        (r) float64 0.0 0.01 0.02 0.03 0.04 ... 0.95 0.96 0.97 0.98 0.99\n",
      "  * px       (px) int64 8\n",
      "\n",
      "power\t\tnot calculated\n"
     ]
    }
   ],
   "source": [
    "print_ds_stats(load_metaanalysis_outcomes(px=8, py=7, n=95, data_home=data_home))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following piece of code performs the second part of the analysis: it finds synthetic datasets with estimated canonical correlation matching the empirical one and calculates the corresponding weight error in the synthetic dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_error(n, px, py, target_corr, calc_error_fun=mk_weightError, error_kind=.975,\n",
    "              corr_bw=0.1, qs=(.025, .5, .975), verbose=False):\n",
    "    \n",
    "    if (px == 1) or (py == 1):\n",
    "        return np.nan\n",
    "    \n",
    "    ds = load_metaanalysis_outcomes(px, py, n)\n",
    "\n",
    "    corr_cond = np.abs(ds.between_assocs / target_corr - 1) < corr_bw\n",
    "    if corr_cond.sum() == 0:\n",
    "        return np.nan\n",
    "    \n",
    "    if verbose:\n",
    "        print(n, px, py, target_corr, 'n_corr_cond =', int(corr_cond.sum()))\n",
    "\n",
    "    error = calc_error_fun(ds)\n",
    "    conditional_error = error.where(corr_cond)\n",
    "    \n",
    "    if error_kind == 'distribution':\n",
    "        return conditional_error\n",
    "    \n",
    "    else:\n",
    "        error_stats = xr.concat([\n",
    "            conditional_error.quantile(qs),\n",
    "            xr.DataArray([conditional_error.mean()], dims=('quantile',), coords=dict(quantile=['mean'])),\n",
    "        ],\n",
    "            'quantile'\n",
    "        )\n",
    "        res = error_stats.sel(quantile=error_kind)\n",
    "        if res.ndim == 0:\n",
    "            res = float(res)\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_errors = [\n",
    "    get_error(row[1]['n'], row[1]['pX'], row[1]['pY'], row[1]['r1'],\n",
    "              calc_error_fun=mk_weightError, error_kind='mean')\n",
    "    for row in metaanalysis_data_.iterrows()\n",
    "]\n",
    "metaanalysis_data_['mean_estimated_weight_error'] = weight_errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's overlay the estimated weight errors on the observed-correlation vs samples-per-feature plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .Scatter.I   :Scatter   [Samples per feature]   (Observed correlation,Estim weight error)\n",
       "   .Scatter.II  :Scatter   [Samples per feature]   (Observed correlation,Estim weight error)\n",
       "   .Scatter.III :Scatter   [Samples per feature]   (Observed correlation,Estim weight error)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_healthy_marker(s):\n",
    "    if s == 'yes':\n",
    "        return 's'\n",
    "    elif s == 'no':\n",
    "        return 'd'\n",
    "    else:\n",
    "        return 'x'\n",
    "marker = metaanalysis_data_['is_healthy'].apply(is_healthy_marker)\n",
    "\n",
    "panel_metaanalysis_rObs_nPerFtr = hv.Overlay()\n",
    "for my_marker in np.unique(marker):\n",
    "    mask = (marker == my_marker)\n",
    "    panel_metaanalysis_rObs_nPerFtr *= hv.Scatter(\n",
    "        (metaanalysis_n_per_ftr[mask], metaanalysis_data_['r1'][mask], \n",
    "        metaanalysis_data_['mean_estimated_weight_error'][mask].rename('Estim_weight_error')),\n",
    "        kdims=['Samples per feature'],\n",
    "        vdims=['Observed correlation', 'Estim weight error'],\n",
    "    ).opts(*fig_opts).opts(\n",
    "        fig_inches=(1.7, None), s=20, edgecolor='black', linewidth=.25, logx=True, logy=True, \n",
    "        color=dim('Estim_weight_error'), cmap='PuRd', colorbar=True, clim=(.0, 1), clabel='Estim. weight error', marker=my_marker\n",
    "    )\n",
    "\n",
    "panel_metaanalysis_rObs_nPerFtr.opts(*fig_opts).opts(\n",
    "    fig_inches=(5, None)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Scatter   [Samples per feature]   (Observed correlation,Estim weight error)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel_metaanalysis_rObs_nPerFtr = hv.Scatter(\n",
    "    (metaanalysis_n_per_ftr, metaanalysis_data_['r1'], \n",
    "     metaanalysis_data_['mean_estimated_weight_error'].rename('Estim_weight_error')),\n",
    "    kdims=['Samples per feature'],\n",
    "    vdims=['Observed correlation', 'Estim weight error'],\n",
    "    label='Analysis of prior literature',\n",
    ").opts(*fig_opts).opts(\n",
    "    fig_inches=(1.7, None), s=10, marker='o', edgecolor='black', linewidth=.25, logx=True, logy=True, \n",
    "    color=dim('Estim_weight_error'), cmap='PuRd', colorbar=True, clim=(.0, 1), clabel='Estim. weight error'\n",
    ")\n",
    "\n",
    "panel_metaanalysis_rObs_nPerFtr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to look at two more aspects of the estimated weight errors:\n",
    "\n",
    "* What's the distribution of estimated weight errors for a given reported CCA?\n",
    "* It appears that the weight errors are smaller the farther a point (i.e. a reported CCA) lies away from the top-left to bottom-right diagonal in the plot. We'll look more closely at this relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of weight errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 3 3 0.43 n_corr_cond = 1234\n",
      "80 5 3 0.446 n_corr_cond = 1599\n",
      "80 3 3 0.415 n_corr_cond = 1230\n",
      "449 96 32 0.959 n_corr_cond = 2146\n",
      "602 9 6 0.257 n_corr_cond = 813\n",
      "602 9 6 0.332 n_corr_cond = 759\n",
      "602 12 6 0.23 n_corr_cond = 1270\n",
      "602 16 6 0.363 n_corr_cond = 872\n",
      "602 3 6 0.345 n_corr_cond = 745\n",
      "602 3 6 0.315 n_corr_cond = 644\n",
      "602 6 6 0.211 n_corr_cond = 776\n",
      "602 6 6 0.284 n_corr_cond = 673\n",
      "95 8 7 0.64 n_corr_cond = 2167\n",
      "95 4 7 0.61 n_corr_cond = 1673\n",
      "678 68 14 0.54 n_corr_cond = 1761\n",
      "678 68 14 0.56 n_corr_cond = 1688\n",
      "678 68 14 0.54 n_corr_cond = 1761\n",
      "678 68 14 0.54 n_corr_cond = 1761\n",
      "461 100 100 0.87 n_corr_cond = 9131\n",
      "183 55 4 0.68 n_corr_cond = 5286\n",
      "220 176 17 0.95 n_corr_cond = 6158\n",
      "187 150 17 0.99 n_corr_cond = 5183\n",
      "7577 200 200 0.67 n_corr_cond = 1484\n",
      "89 22 22 0.91 n_corr_cond = 9297\n",
      "818 100 100 0.7 n_corr_cond = 6680\n",
      "818 100 100 0.77 n_corr_cond = 2729\n",
      "1094 100 100 0.667 n_corr_cond = 3374\n",
      "89 4 2 0.52 n_corr_cond = 1206\n",
      "89 4 2 0.55 n_corr_cond = 1273\n",
      "440 6 11 0.31 n_corr_cond = 1065\n",
      "306 25 25 0.62 n_corr_cond = 2595\n",
      "101 8 8 0.565685424949238 n_corr_cond = 2959\n",
      "35 2 7 0.67 n_corr_cond = 3249\n",
      "30 2 7 0.52 n_corr_cond = 1100\n",
      "386 6 9 0.34 n_corr_cond = 941\n",
      "819 100 100 0.73 n_corr_cond = 4505\n",
      "819 100 100 0.72 n_corr_cond = 5876\n",
      "819 100 100 0.77 n_corr_cond = 2729\n",
      "819 100 100 0.73 n_corr_cond = 4505\n",
      "819 100 100 0.79 n_corr_cond = 2635\n",
      "819 100 100 0.72 n_corr_cond = 5876\n",
      "819 100 100 0.77 n_corr_cond = 2729\n",
      "819 100 100 0.67 n_corr_cond = 6102\n",
      "819 100 100 0.67 n_corr_cond = 6102\n",
      "819 100 100 0.69 n_corr_cond = 6508\n",
      "819 100 100 0.8 n_corr_cond = 2608\n",
      "819 100 100 0.73 n_corr_cond = 4505\n",
      "819 100 100 0.77 n_corr_cond = 2729\n",
      "819 100 100 0.67 n_corr_cond = 6102\n",
      "790 100 100 0.81 n_corr_cond = 7481\n",
      "819 100 100 0.72 n_corr_cond = 5876\n",
      "441 100 100 0.84 n_corr_cond = 8333\n",
      "441 100 100 0.86 n_corr_cond = 8881\n",
      "441 100 100 0.85 n_corr_cond = 8621\n",
      "441 100 100 0.88 n_corr_cond = 9377\n",
      "441 100 100 0.88 n_corr_cond = 9377\n",
      "441 100 100 0.88 n_corr_cond = 9377\n",
      "441 100 100 0.86 n_corr_cond = 8881\n",
      "441 100 100 0.87 n_corr_cond = 9131\n",
      "441 100 100 0.87 n_corr_cond = 9131\n",
      "1001 100 100 0.636766605160632 n_corr_cond = 5919\n",
      "1001 100 100 0.674568583927832 n_corr_cond = 2843\n",
      "1001 100 100 0.704669479965598 n_corr_cond = 2362\n",
      "1001 100 100 0.724108749953004 n_corr_cond = 2331\n",
      "1001 100 100 0.648504792418602 n_corr_cond = 5510\n",
      "1001 100 100 0.63235781355741 n_corr_cond = 5867\n",
      "1001 100 100 0.585758401125084 n_corr_cond = 4948\n",
      "1001 100 100 0.619523275363749 n_corr_cond = 5634\n",
      "1001 100 100 0.648510909243646 n_corr_cond = 5509\n",
      "1001 100 100 0.642638757202139 n_corr_cond = 5845\n",
      "1001 100 100 0.608879999788517 n_corr_cond = 5423\n",
      "1001 100 100 0.597502705208098 n_corr_cond = 5184\n",
      "23 6 13 0.98 n_corr_cond = 6170\n",
      "193 30 30 0.75 n_corr_cond = 7111\n",
      "466 9 174 0.74 n_corr_cond = 5301\n",
      "466 9 174 0.73 n_corr_cond = 4611\n",
      "466 9 174 0.74 n_corr_cond = 5301\n",
      "466 9 174 0.75 n_corr_cond = 5768\n",
      "466 9 174 0.8 n_corr_cond = 7380\n",
      "466 9 174 0.7 n_corr_cond = 928\n",
      "466 9 174 0.67 n_corr_cond = 1\n",
      "498 100 100 0.97 n_corr_cond = 3736\n",
      "498 100 100 0.91 n_corr_cond = 10000\n",
      "413 6 11 0.32 n_corr_cond = 1091\n",
      "413 6 11 0.31 n_corr_cond = 1163\n",
      "22 4 3 0.60249481 n_corr_cond = 2291\n",
      "597 31 7 0.37 n_corr_cond = 1612\n",
      "443 16 7 0.34 n_corr_cond = 1337\n",
      "454 11 7 0.32 n_corr_cond = 1027\n",
      "640 6 7 0.22 n_corr_cond = 816\n",
      "198 9 2 0.47 n_corr_cond = 1136\n"
     ]
    }
   ],
   "source": [
    "weight_error_distribs = [\n",
    "    get_error(row[1]['n'], row[1]['pX'], row[1]['pY'], row[1]['r1'],\n",
    "              calc_error_fun=mk_weightError, error_kind=slice(None), qs=(.025, .975), verbose=True)\n",
    "    for row in metaanalysis_data_.iterrows()\n",
    "]\n",
    "\n",
    "assert isinstance(weight_error_distribs[0], xr.DataArray)\n",
    "for i in range(len(weight_error_distribs)):\n",
    "    if not isinstance(weight_error_distribs[i], xr.DataArray):\n",
    "        weight_error_distribs[i] = xr.full_like(weight_error_distribs[0], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = xr.concat(weight_error_distribs, 'iter')\n",
    "e = e.sortby(e.sel(quantile='mean'))\n",
    "\n",
    "error_bars = xr.concat([\n",
    "    e.iter,\n",
    "    e.sel(quantile='mean', drop=True),\n",
    "    e.sel(quantile='mean', drop=True) - e.sel(quantile=.025, drop=True),\n",
    "    e.sel(quantile=.975, drop=True) - e.sel(quantile='mean', drop=True),\n",
    "],\n",
    "    'errorbar_feature',\n",
    ").T\n",
    "\n",
    "error_bars = error_bars.assign_coords(errorbar_feature=['i', 'mean', 'upper', 'lower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .ErrorBars.I :ErrorBars   [Reported CCA]   (Estim. weight error,yerrneg,yerrpos)\n",
       "   .Scatter.I   :Scatter   [Reported CCA]   (Estim. weight error)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel_estimated_weight_errors = (\n",
    "    hv.ErrorBars(error_bars.values, vdims=['y', 'yerrneg', 'yerrpos']) * \n",
    "    hv.Scatter(\n",
    "        (error_bars.sel(errorbar_feature='i'), error_bars.sel(errorbar_feature='mean'))\n",
    "    )\n",
    ").redim(\n",
    "    x='Reported CCA',\n",
    "    y='Estim. weight error',\n",
    ").opts(\n",
    "    opts.ErrorBars(alpha=.3),\n",
    "    opts.Scatter(s=5, color='black', marker='.'),\n",
    "    opts.Overlay(xticks=0, padding=0.02, fig_inches=(1.7, None))\n",
    ")\n",
    "\n",
    "plotted_data['estimated_weight_errors'] = error_bars.sel(errorbar_feature=['mean', 'upper', 'lower']).to_pandas().dropna(how='all')\n",
    "\n",
    "panel_estimated_weight_errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation of weight error to distance from diagonal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, it appears that, in the observed canonical correlation vs samples per features plot, the distance from the top-left-to-bottom-right diagonal is related to the mean estimated weight error. To investigate that let's first fit a linear model to the synthetic permutation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear model:\n",
      "intercept: 0.16136524728100143\n",
      "slope: [-0.49059251]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Overlay\n",
       "   .Scatter.I :Scatter   [log(samples per feature)]   (log(observed canonical correlation))\n",
       "   .Curve.I   :Curve   [log(samples per feature)]   (log(observed canonical correlation))"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit linear model to (log(n_per_ftr), log(observed correlation)) for permuted data\n",
    "_ds_perm = ds_cca.between_assocs_perm \\\n",
    "    .mean('perm').mean('rep').stack(it=('Sigma_id', 'px', 'n_per_ftr', 'r'))\n",
    "\n",
    "X = np.log(_ds_perm.n_per_ftr.values.reshape(-1, 1))\n",
    "y = np.log(_ds_perm.values)\n",
    "lm = LinearRegression(fit_intercept=True).fit(X, y)\n",
    "print('Linear model:')\n",
    "print('intercept:', lm.intercept_)\n",
    "print('slope:', lm.coef_)\n",
    "\n",
    "_test_x = np.array([0, np.log(100)])\n",
    "(\n",
    "    hv.Scatter((X[:, 0], y)) \n",
    "    * hv.Curve((_test_x, lm.predict(_test_x.reshape(-1, 1))))\n",
    ").redim(\n",
    "    x='log(samples per feature)',\n",
    "    y='log(observed canonical correlation)'\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Scatter(s=10),\n",
    "    opts.Overlay(fig_inches=(1.7, None))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, calculate distane from the fitted line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal vector: [0.44044415 0.89778001]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anchor = np.array([0, lm.intercept_])\n",
    "v = np.array([1, lm.coef_[0]])\n",
    "\n",
    "vN = -np.array([lm.coef_[0], -1])\n",
    "vN = vN / np.linalg.norm(vN)\n",
    "\n",
    "assert np.dot(v, vN) == 0\n",
    "\n",
    "print('normal vector:', vN)\n",
    "\n",
    "###\n",
    "\n",
    "mask = np.isfinite(metaanalysis_data_['mean_estimated_weight_error'].values)\n",
    "\n",
    "Xlit = np.log(metaanalysis_n_per_ftr).values.reshape(-1, 1)\n",
    "ylit = np.log(metaanalysis_data_['r1'].values)\n",
    "xylit = np.c_[Xlit, ylit.reshape(-1, 1)]\n",
    "dist_to_null = (xylit - anchor).dot(vN)\n",
    "len(dist_to_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Scatter   [Distance to perm]   (Mean estimated\n",
       "weight error)"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hook_ticklabelsize(plot, element):\n",
    "    ax = plot.handles['axis']\n",
    "    ax.tick_params(axis='both', which='both', labelsize=7)\n",
    "\n",
    "\n",
    "fig_dist_to_null_weight_error = hv.Scatter(\n",
    "    (dist_to_null[mask], metaanalysis_data_['mean_estimated_weight_error'].values[mask])\n",
    ").redim(\n",
    "    x='Distance to perm',\n",
    "    y='Mean estimated\\nweight error'\n",
    ").opts(\n",
    "    *fig_opts\n",
    ").opts(\n",
    "    fig_inches=(1.7, None), logx=False, logy=True, ylim=(None, 1), padding=.02, s=5, xticks=4,\n",
    "    hooks=[hook_ticklabelsize]\n",
    ")\n",
    "\n",
    "fig_dist_to_null_weight_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assemble summary figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Layout\n",
       "   .Overlay.I  :Overlay\n",
       "      .Curve.Perm                           :Curve   [Samples per feature]   (Observed correlation)\n",
       "      .Curve.R_equals_0_full_stop_1         :Curve   [Samples per feature]   (Observed correlation)\n",
       "      .Curve.R_equals_0_full_stop_3         :Curve   [Samples per feature]   (Observed correlation)\n",
       "      .Curve.R_equals_0_full_stop_5         :Curve   [Samples per feature]   (Observed correlation)\n",
       "      .Text.I                               :Text   [Samples per feature,Observed correlation]\n",
       "      .Text.II                              :Text   [Samples per feature,Observed correlation]\n",
       "      .Text.III                             :Text   [Samples per feature,Observed correlation]\n",
       "      .Text.IV                              :Text   [Samples per feature,Observed correlation]\n",
       "      .Text.V                               :Text   [Samples per feature,Observed correlation]\n",
       "      .Scatter.Analysis_of_prior_literature :Scatter   [Samples per feature]   (Observed correlation,Estim weight error)\n",
       "      .Text.VI                              :Text   [x,y]\n",
       "      .Text.VII                             :Text   [x,y]\n",
       "   .Overlay.II :Overlay\n",
       "      .ErrorBars.I :ErrorBars   [Reported CCA]   (Estim. weight error,yerrneg,yerrpos)\n",
       "      .Scatter.I   :Scatter   [Reported CCA]   (Estim. weight error)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hook_logy_formatter(plot, element):\n",
    "    ax = plot.handles['axis']\n",
    "    \n",
    "    x_minor_ticks = np.r_[np.arange(3, 10), np.arange(20, 100, 10)]\n",
    "    ax.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator(x_minor_ticks))\n",
    "    ax.xaxis.set_major_locator(matplotlib.ticker.FixedLocator([2, 5, 10, 50, 100]))\n",
    "    ax.xaxis.set_major_formatter(matplotlib.ticker.FixedFormatter([2, 5, 10, 50, '']))\n",
    "    \n",
    "    ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%.1f'))\n",
    "    ax.yaxis.set_minor_formatter(matplotlib.ticker.FixedFormatter(['']*9))\n",
    "    ax.yaxis.set_minor_locator(matplotlib.ticker.FixedLocator(np.arange(.2, 1.0, .1)))\n",
    "    ax.yaxis.set_major_locator(matplotlib.ticker.FixedLocator([.1, .5, 1]))\n",
    "    ax.tick_params(axis='y', which='both', labelsize=7)\n",
    "    \n",
    "    \n",
    "def hook_cbar(plot, element):\n",
    "    fig = plot.handles['fig']\n",
    "    cbar_ax = fig.axes[-1]\n",
    "    cbar_ax.yaxis.label.set_fontsize(8)\n",
    "    cbar_ax.tick_params(axis='y', which='both', labelsize=7)\n",
    "    \n",
    "    \n",
    "fig = (\n",
    "    (panel_synthetic.opts(show_legend=True)\n",
    "     * panel_metaanalysis_rObs_nPerFtr.opts(s=15, clim=(0,1))\n",
    "     * hv.Text(80, 1.25, 'reported CCAs', halign='right', valign='top', fontsize=8)\n",
    "     * hv.Text(100, .95, '$R^2=%.2f$' % log_nPerFtr_obsCorr_R2, halign='right', valign='top', fontsize=8)\n",
    "    ).opts(\n",
    "        show_legend=False, hooks=[hook_logy_formatter, hook_cbar], sublabel_position=(-.17, .92), \n",
    "        xlim=(None, None), ylim=(None, 1.05), padding=.0,\n",
    "    )\n",
    "    + panel_estimated_weight_errors.opts(opts.Overlay(sublabel_position=(-.17, .92), yticks=6, ylabel='', padding=0, ylim=(0, 1)), \n",
    "                                         opts.ErrorBars(linewidth=.5))\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Layout(aspect_weight=1, hspace=.7, fig_inches=(3.42, 3.42/2/1.6*5*1.2), \n",
    "                sublabel_size=10), \n",
    ")\n",
    "\n",
    "hv.save(fig, 'fig/fig_metaanalysis.pdf')\n",
    "save_source_data(plotted_data, 'fig6')\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supplementary figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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' style='max-width:100%; margin: auto; display: block; '/>"
      ],
      "text/plain": [
       ":Layout\n",
       "   .Histogram.I :Histogram   [Samples per feature]   (Num. reported CCAs)\n",
       "   .Scatter.I   :Scatter   [Distance to perm]   (Mean estimated\n",
       "weight error)"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {}
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hook_xticks(plot, element):\n",
    "    ax = plot.handles['axis']\n",
    "    ax.xaxis.set_minor_locator(matplotlib.ticker.LogLocator(subs='auto', numticks=5))\n",
    "    ax.xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "    \n",
    "    \n",
    "figS = (\n",
    "    panel_n_per_ftr_hist.opts(hooks=[Format_log_axis('x', major_numticks=4, minor_numticks=5)])\n",
    "    + fig_dist_to_null_weight_error\n",
    ").opts(*fig_opts).opts(\n",
    "    opts.Layout(hspace=.65, sublabel_position=(-.45, .95), fig_inches=(3.42, 1.))\n",
    ")\n",
    "\n",
    "hv.save(figS, 'fig/figS_metaanalysis.pdf')\n",
    "\n",
    "figS"
   ]
  }
 ],
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