project/10_2_ald_compare_methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9acfeaca-5f8c-42f3-9a8e-83ed737eb35c",
   "metadata": {},
   "source": [
    "# Compare outcomes from differential analysis based on different imputation methods\n",
    "\n",
    "- load scores based on `10_1_ald_diff_analysis`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eec6f931-c04d-428c-b2b1-0424c50e6cd2",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "\n",
    "import pimmslearn\n",
    "import pimmslearn.databases.diseases\n",
    "\n",
    "logger = pimmslearn.logging.setup_nb_logger()\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (2, 2)\n",
    "fontsize = 5\n",
    "pimmslearn.plotting.make_large_descriptors(fontsize)\n",
    "logging.getLogger('fontTools').setLevel(logging.ERROR)\n",
    "\n",
    "# catch passed parameters\n",
    "args = None\n",
    "args = dict(globals()).keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f5f5d5",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "Default and set parameters for the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4bf65da-0569-4a21-ba20-9cae7d3679e7",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "folder_experiment = 'runs/appl_ald_data/plasma/proteinGroups'\n",
    "\n",
    "target = 'kleiner'\n",
    "model_key = 'VAE'\n",
    "baseline = 'RSN'\n",
    "out_folder = 'diff_analysis'\n",
    "selected_statistics = ['p-unc', '-Log10 pvalue', 'qvalue', 'rejected']\n",
    "\n",
    "disease_ontology = 5082  # code from https://disease-ontology.org/\n",
    "# split diseases notebook? Query gene names for proteins in file from uniprot?\n",
    "annotaitons_gene_col = 'PG.Genes'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c645fc-c7eb-4c69-a7aa-084fc733258f",
   "metadata": {},
   "source": [
    "Add set parameters to configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "978876d0-b3cc-4847-8eab-dc0b89ddbbcd",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "params = pimmslearn.nb.get_params(args, globals=globals())\n",
    "args = pimmslearn.nb.Config()\n",
    "args.folder_experiment = Path(params[\"folder_experiment\"])\n",
    "args = pimmslearn.nb.add_default_paths(args,\n",
    "                                 out_root=(\n",
    "                                     args.folder_experiment\n",
    "                                     / params[\"out_folder\"]\n",
    "                                     / params[\"target\"]\n",
    "                                     / f\"{params['baseline']}_vs_{params['model_key']}\"))\n",
    "args.update_from_dict(params)\n",
    "args.scores_folder = scores_folder = (args.folder_experiment\n",
    "                                      / params[\"out_folder\"]\n",
    "                                      / params[\"target\"]\n",
    "                                      / 'scores')\n",
    "args.freq_features_observed = args.folder_experiment / 'freq_features_observed.csv'\n",
    "args"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc184dea",
   "metadata": {},
   "source": [
    "### Excel file for exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8016d79-e41a-40a2-bcbf-e11711c33b7d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "files_out = dict()\n",
    "writer_args = dict(float_format='%.3f')\n",
    "\n",
    "fname = args.out_folder / 'diff_analysis_compare_methods.xlsx'\n",
    "files_out[fname.name] = fname\n",
    "writer = pd.ExcelWriter(fname)\n",
    "logger.info(\"Writing to excel file: %s\", fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770d1f76-e86f-4ae3-9d7b-ceef9b9e9a22",
   "metadata": {},
   "source": [
    "## Load scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b108869",
   "metadata": {},
   "source": [
    "### Load baseline model scores\n",
    "Show all statistics, later use selected statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97221134-5f61-4158-bfc5-ea30077140b8",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "fname = args.scores_folder / f'diff_analysis_scores_{args.baseline}.pkl'\n",
    "scores_baseline = pd.read_pickle(fname)\n",
    "scores_baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49a8da2",
   "metadata": {},
   "source": [
    "### Load selected comparison model scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0635e4d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "fname = args.scores_folder / f'diff_analysis_scores_{args.model_key}.pkl'\n",
    "scores_model = pd.read_pickle(fname)\n",
    "scores_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b7e883",
   "metadata": {},
   "source": [
    "### Combined scores\n",
    "show only selected statistics for comparsion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "373fdf65",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores = scores_model.join(scores_baseline, how='outer')[[args.baseline, args.model_key]]\n",
    "scores = scores.loc[:, pd.IndexSlice[scores.columns.levels[0].to_list(),\n",
    "                                     args.selected_statistics]]\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84a6e5a",
   "metadata": {},
   "source": [
    "Models in comparison (name mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34d243d1-3ab4-40e7-9eb8-f9efc828b82d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "models = pimmslearn.nb.Config.from_dict(\n",
    "    pimmslearn.pandas.index_to_dict(scores.columns.get_level_values(0)))\n",
    "vars(models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7a560d",
   "metadata": {},
   "source": [
    "## Describe scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fee8f5d-fa52-4369-a1f9-fcfd518ab6bd",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ecc596",
   "metadata": {},
   "source": [
    "### One to one comparison of by feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6e5a0a6-343b-4f07-8d9d-2cd5cf95ae1f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores = scores.loc[pd.IndexSlice[:, args.target], :]\n",
    "scores.to_excel(writer, 'scores', **writer_args)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e14580",
   "metadata": {},
   "source": [
    "And the descriptive statistics\n",
    "of the numeric values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e45e80a-32d8-4c6c-b0a4-5ce8b7f9e121",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e520d6dc",
   "metadata": {},
   "source": [
    "and the boolean decision values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53bd5597-221c-4d54-abf2-82956db42594",
   "metadata": {
    "lines_to_next_cell": 2,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores.describe(include=['bool', 'O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef83742-05e5-473b-892e-dd286cb3a31f",
   "metadata": {},
   "source": [
    "## Load frequencies of observed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a926ba1-0f3b-4089-a349-b6d66128cf37",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "freq_feat = pd.read_csv(args.freq_features_observed, index_col=0)\n",
    "freq_feat.columns = pd.MultiIndex.from_tuples([('data', 'frequency'),])\n",
    "freq_feat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408eacfe-770f-42ff-9057-2a98274e1ae3",
   "metadata": {},
   "source": [
    "## Compare shared features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b2dfb0f-195b-4044-a228-2d784ea2a458",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores_common = (scores\n",
    "                 .dropna()\n",
    "                 .reset_index(-1, drop=True)\n",
    "                 ).join(\n",
    "    freq_feat, how='left'\n",
    ")\n",
    "scores_common"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a9eefd",
   "metadata": {},
   "source": [
    "### Annotate decisions in Confusion Table style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80cf4145-070d-457a-bb74-ee64299809e7",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def annotate_decision(scores, model, model_column):\n",
    "    return scores[(model_column, 'rejected')].replace({False: f'{model} (no) ', True: f'{model} (yes)'})\n",
    "\n",
    "\n",
    "annotations = None\n",
    "for model, model_column in models.items():\n",
    "    if annotations is not None:\n",
    "        annotations += ' - '\n",
    "        annotations += annotate_decision(scores_common,\n",
    "                                         model=model, model_column=model_column)\n",
    "    else:\n",
    "        annotations = annotate_decision(\n",
    "            scores_common, model=model, model_column=model_column)\n",
    "annotations.name = 'Differential Analysis Comparison'\n",
    "annotations.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0942d395",
   "metadata": {},
   "source": [
    "### List different decisions between models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cda5ffa4-9a97-4a49-aaba-34e83ef7940a",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "mask_different = (\n",
    "    (scores_common.loc[:, pd.IndexSlice[:, 'rejected']].any(axis=1))\n",
    "    & ~(scores_common.loc[:, pd.IndexSlice[:, 'rejected']].all(axis=1))\n",
    ")\n",
    "_to_write = scores_common.loc[mask_different]\n",
    "_to_write.to_excel(writer, 'differences', **writer_args)\n",
    "logger.info(\"Writen to Excel file under sheet 'differences'.\")\n",
    "_to_write"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8832084",
   "metadata": {},
   "source": [
    "## Plot qvalues of both models with annotated decisions\n",
    "\n",
    "Prepare data for plotting (qvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b2e5341-b054-40c3-b45a-44ae6ca46cfb",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "var = 'qvalue'\n",
    "to_plot = [scores_common[v][var] for v in models.values()]\n",
    "for s, k in zip(to_plot, models.keys()):\n",
    "    s.name = k.replace('_', ' ')\n",
    "to_plot.append(scores_common['data'])\n",
    "to_plot.append(annotations)\n",
    "to_plot = pd.concat(to_plot, axis=1)\n",
    "to_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92517a4-8456-4663-b719-4abd997fbb1c",
   "metadata": {
    "tags": []
   },
   "source": [
    "List of features with the highest difference in qvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7af4a70-aa43-4772-af00-d425f5ed249f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# should it be possible to run not only RSN?\n",
    "to_plot['diff_qvalue'] = (to_plot[str(args.baseline)] - to_plot[str(args.model_key)]).abs()\n",
    "to_plot.loc[mask_different].sort_values('diff_qvalue', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e715954e-2d62-4cd9-b4b0-063524bca495",
   "metadata": {},
   "source": [
    "### Differences plotted with created annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20d356d-c397-4440-b70e-9d899aa200fd",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "figsize = (4, 4)\n",
    "size = 5\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "x_col = to_plot.columns[0]\n",
    "y_col = to_plot.columns[1]\n",
    "ax = sns.scatterplot(data=to_plot,\n",
    "                     x=x_col,\n",
    "                     y=y_col,\n",
    "                     s=size,\n",
    "                     hue='Differential Analysis Comparison',\n",
    "                     ax=ax)\n",
    "_ = ax.legend(fontsize=fontsize,\n",
    "              title_fontsize=fontsize,\n",
    "              markerscale=0.4,\n",
    "              title='',\n",
    "              )\n",
    "ax.set_xlabel(f\"qvalue for {x_col}\")\n",
    "ax.set_ylabel(f\"qvalue for {y_col}\")\n",
    "ax.hlines(0.05, 0, 1, color='grey', linestyles='dotted')\n",
    "ax.vlines(0.05, 0, 1, color='grey', linestyles='dotted')\n",
    "sns.move_legend(ax, \"upper right\")\n",
    "files_out[f'diff_analysis_comparision_1_{args.model_key}'] = (\n",
    "    args.out_folder /\n",
    "    f'diff_analysis_comparision_1_{args.model_key}')\n",
    "fname = files_out[f'diff_analysis_comparision_1_{args.model_key}']\n",
    "pimmslearn.savefig(fig, name=fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8849c76-c5f6-4618-87c0-f2635dc9ac66",
   "metadata": {},
   "source": [
    "- also showing how many features were measured (\"observed\") by size of circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cf258f8-9dcb-40bb-af66-b600f8d413f6",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "ax = sns.scatterplot(data=to_plot,\n",
    "                     x=to_plot.columns[0],\n",
    "                     y=to_plot.columns[1],\n",
    "                     size='frequency',\n",
    "                     s=size,\n",
    "                     sizes=(5, 20),\n",
    "                     hue='Differential Analysis Comparison')\n",
    "_ = ax.legend(fontsize=fontsize,\n",
    "              title_fontsize=fontsize,\n",
    "              markerscale=0.6,\n",
    "              title='',\n",
    "              )\n",
    "ax.set_xlabel(f\"qvalue for {x_col}\")\n",
    "ax.set_ylabel(f\"qvalue for {y_col}\")\n",
    "ax.hlines(0.05, 0, 1, color='grey', linestyles='dotted')\n",
    "ax.vlines(0.05, 0, 1, color='grey', linestyles='dotted')\n",
    "sns.move_legend(ax, \"upper right\")\n",
    "files_out[f'diff_analysis_comparision_2_{args.model_key}'] = (\n",
    "    args.out_folder / f'diff_analysis_comparision_2_{args.model_key}')\n",
    "pimmslearn.savefig(\n",
    "    fig, name=files_out[f'diff_analysis_comparision_2_{args.model_key}'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fee3a21-d8b3-40c6-aea2-4774dfe855ca",
   "metadata": {},
   "source": [
    "## Only features contained in model\n",
    "- this block exist due to a specific part in the ALD analysis of the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e23a01-fd37-4496-a518-445a9ef38db1",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "scores_model_only = scores.reset_index(level=-1, drop=True)\n",
    "_diff = scores_model_only.index.difference(scores_common.index)\n",
    "if not _diff.empty:\n",
    "    scores_model_only = (scores_model_only\n",
    "                         .loc[\n",
    "                             _diff,\n",
    "                             args.model_key]\n",
    "                         .sort_values(by='qvalue', ascending=True)\n",
    "                         .join(freq_feat.squeeze().rename(freq_feat.columns.droplevel()[0])\n",
    "                               )\n",
    "                         )\n",
    "    display(scores_model_only)\n",
    "else:\n",
    "    scores_model_only = None\n",
    "    logger.info(\"No features only in new comparision model.\")\n",
    "\n",
    "if not _diff.empty:\n",
    "    scores_model_only.to_excel(writer, 'only_model', **writer_args)\n",
    "    display(scores_model_only.rejected.value_counts())\n",
    "    scores_model_only_rejected = scores_model_only.loc[scores_model_only.rejected]\n",
    "    scores_model_only_rejected.to_excel(\n",
    "        writer, 'only_model_rejected', **writer_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b2c336",
   "metadata": {},
   "source": [
    "## DISEASES DB lookup\n",
    "\n",
    "Query diseases database for gene associations with specified disease ontology id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d93a9242-0ef4-4fc7-bd98-226a93639f58",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "data = pimmslearn.databases.diseases.get_disease_association(\n",
    "    doid=args.disease_ontology, limit=10000)\n",
    "data = pd.DataFrame.from_dict(data, orient='index').rename_axis('ENSP', axis=0)\n",
    "data = data.rename(columns={'name': args.annotaitons_gene_col}).reset_index(\n",
    ").set_index(args.annotaitons_gene_col)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe22443-2613-46ad-83c2-41048e13dd57",
   "metadata": {},
   "source": [
    "## Shared features\n",
    "ToDo: new script -> DISEASES DB lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c26415e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b68b43df",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "feat_name = scores.index.names[0]  # first index level is feature name\n",
    "if args.annotaitons_gene_col in scores.index.names:\n",
    "    logger.info(f\"Found gene annotation in scores index:  {scores.index.names}\")\n",
    "else:\n",
    "    logger.info(f\"No gene annotation in scores index:  {scores.index.names}\"\n",
    "                \" Exiting.\")\n",
    "    import sys\n",
    "    sys.exit(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8d4a74d-5a9b-4d9b-9345-4288bb23e19f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "gene_to_PG = (scores.droplevel(\n",
    "    list(set(scores.index.names) - {feat_name, args.annotaitons_gene_col})\n",
    ")\n",
    "    .index\n",
    "    .to_frame()\n",
    "    .reset_index(drop=True)\n",
    "    .set_index(args.annotaitons_gene_col)\n",
    ")\n",
    "gene_to_PG.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9e76def-b48a-458d-a90b-765e6e70f7a4",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "disease_associations_all = data.join(\n",
    "    gene_to_PG).dropna().reset_index().set_index(feat_name).join(annotations)\n",
    "disease_associations_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217972a3-ba4a-4f28-ab8f-25dc0f5e9ede",
   "metadata": {},
   "source": [
    "## only by model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e9d6944-87ba-4c41-af14-fb5ed93262f0",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "idx = disease_associations_all.index.intersection(scores_model_only.index)\n",
    "disease_assocications_new = disease_associations_all.loc[idx].sort_values(\n",
    "    'score', ascending=False)\n",
    "disease_assocications_new.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceefc483-b889-4bab-b207-c8d5fd97fa4a",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "mask = disease_assocications_new.loc[idx, 'score'] >= 2.0\n",
    "disease_assocications_new.loc[idx].loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85eede2-7b61-4285-ba8c-745f77f6ab22",
   "metadata": {},
   "source": [
    "## Only by model which were significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4644759b-8cc2-4f99-a16c-16419cfb915c",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "idx = disease_associations_all.index.intersection(\n",
    "    scores_model_only_rejected.index)\n",
    "disease_assocications_new_rejected = disease_associations_all.loc[idx].sort_values(\n",
    "    'score', ascending=False)\n",
    "disease_assocications_new_rejected.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365d8641-97a8-464f-b69b-270af9ae6e2d",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "mask = disease_assocications_new_rejected.loc[idx, 'score'] >= 2.0\n",
    "disease_assocications_new_rejected.loc[idx].loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f22614b-e058-4e35-b8a9-141c291a93bc",
   "metadata": {},
   "source": [
    "## Shared which are only significant for by model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82eb2ba8-4fe7-431d-8095-5be43f7791b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = (scores_common[(str(args.model_key), 'rejected')] & mask_different)\n",
    "mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6416d494-5f3e-4cf4-b766-b1f95e40ae1c",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "idx = disease_associations_all.index.intersection(mask.index[mask])\n",
    "disease_assocications_shared_rejected_by_model = (disease_associations_all.loc[idx].sort_values(\n",
    "    'score', ascending=False))\n",
    "disease_assocications_shared_rejected_by_model.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10899e28-4aee-4d44-a542-e45be6699a1b",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "mask = disease_assocications_shared_rejected_by_model.loc[idx, 'score'] >= 2.0\n",
    "disease_assocications_shared_rejected_by_model.loc[idx].loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c082ea-7d92-4296-8ec9-d279509142a3",
   "metadata": {},
   "source": [
    "## Only significant by RSN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59aae50-6877-47cc-99f6-646862d29121",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = (scores_common[(str(args.baseline), 'rejected')] & mask_different)\n",
    "mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7780de55-c63b-4028-a6d0-58bce7be81da",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "idx = disease_associations_all.index.intersection(mask.index[mask])\n",
    "disease_assocications_shared_rejected_by_RSN = (\n",
    "    disease_associations_all\n",
    "    .loc[idx]\n",
    "    .sort_values('score', ascending=False))\n",
    "disease_assocications_shared_rejected_by_RSN.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03115c8f-1f20-4b51-a78c-4d7c0317dc33",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "mask = disease_assocications_shared_rejected_by_RSN.loc[idx, 'score'] >= 2.0\n",
    "disease_assocications_shared_rejected_by_RSN.loc[idx].loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d72708d-ad27-435b-b672-7dfe35283601",
   "metadata": {},
   "source": [
    "## Write to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91e7fbb7-69fd-4b4c-9bc2-40e8dd1907b3",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "disease_associations_all.to_excel(\n",
    "    writer, sheet_name='disease_assoc_all', **writer_args)\n",
    "disease_assocications_new.to_excel(\n",
    "    writer, sheet_name='disease_assoc_new', **writer_args)\n",
    "disease_assocications_new_rejected.to_excel(\n",
    "    writer, sheet_name='disease_assoc_new_rejected', **writer_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a42028-7e2d-47d5-be02-52f7ff1f3665",
   "metadata": {},
   "source": [
    "## Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f3a7433-3bf1-4168-8f16-eb6d415ef17f",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "writer.close()\n",
    "files_out"
   ]
  }
 ],
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