3626 lines (3625 with data), 578.2 kB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a temporary notebook for shap value analysis and plots."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/scai/PhenPred\n"
]
}
],
"source": [
"%cd .."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"proj_dir = \"/home/scai/PhenPred\"\n",
"if not os.path.exists(proj_dir):\n",
" proj_dir = \"/Users/emanuel/Projects/PhenPred\"\n",
"sys.path.extend([proj_dir])\n",
"\n",
"import json\n",
"import PhenPred\n",
"import argparse\n",
"import pandas as pd\n",
"from PhenPred.vae import plot_folder\n",
"from PhenPred.vae.Hypers import Hypers\n",
"from PhenPred.vae.Train import CLinesTrain\n",
"from PhenPred.vae.DatasetDepMap23Q2 import CLinesDatasetDepMap23Q2\n",
"pd.set_option(\"display.max_rows\", 100)\n",
"pd.set_option(\"display.max_columns\", 100)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"plt.rcParams[\"font.family\"] = \"Arial\"\n",
"plt.rcParams[\"font.size\"] = 4\n",
"plt.rcParams[\"axes.linewidth\"] = 0.25\n",
"plt.rcParams[\"figure.figsize\"] = (2.5, 2.5)\n",
"plt.rcParams[\"pdf.fonttype\"] = 42\n",
"plt.rcParams[\"ps.fonttype\"] = 42\n",
"plt.rcParams[\"figure.dpi\"] = 200\n",
"plt.rcParams[\"axes.linewidth\"] = 0.25\n",
"plt.rcParams[\"legend.fontsize\"] = 4\n",
"\n",
"sns.set(style=\"ticks\", context=\"paper\", font_scale=1, font=\"Arial\")\n",
"sns.set_context(\n",
" \"paper\",\n",
" rc={\n",
" \"axes.linewidth\": 0.25,\n",
" \"xtick.major.size\": 2,\n",
" \"xtick.major.width\": 0.25,\n",
" \"ytick.major.size\": 2,\n",
" \"ytick.major.width\": 0.25,\n",
" \"xtick.labelsize\": 6,\n",
" \"ytick.labelsize\": 6,\n",
" \"axes.labelsize\": 7,\n",
" \"legend.fontsize\": 6,\n",
" \"legend.title_fontsize\": 6,\n",
" },\n",
")\n",
"\n",
"import matplotlib.patches as mpatches\n",
"import umap\n",
"\n",
"pd.set_option(\"display.max_rows\", 100)\n",
"pd.set_option(\"display.max_columns\", 100)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import shap\n",
"import pickle\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"TIMESTAMP = \"20241210_000556\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# ---- Hyperparameters\n",
"{\n",
" \"activation_function\": \"prelu\",\n",
" \"batch_norm\": false,\n",
" \"batch_size\": 256,\n",
" \"contrastive_neg_margin\": 0.15,\n",
" \"contrastive_pos_margin\": 0.85,\n",
" \"dataname\": \"depmap23Q2\",\n",
" \"datasets\": {\n",
" \"copynumber\": \"/home/scai/PhenPred/data/clines//cnv_summary_20230303_matrix.csv\",\n",
" \"crisprcas9\": \"/home/scai/PhenPred/data/clines//depmap23Q2/CRISPRGeneEffect.csv\",\n",
" \"drugresponse\": \"/home/scai/PhenPred/data/clines//drugresponse.csv\",\n",
" \"metabolomics\": \"/home/scai/PhenPred/data/clines//metabolomics.csv\",\n",
" \"methylation\": \"/home/scai/PhenPred/data/clines//methylation.csv\",\n",
" \"proteomics\": \"/home/scai/PhenPred/data/clines//proteomics.csv\",\n",
" \"transcriptomics\": \"/home/scai/PhenPred/data/clines//depmap23Q2/OmicsExpressionGenesExpectedCountProfileVoom.csv\"\n",
" },\n",
" \"dip_vae_type\": \"i\",\n",
" \"feature_dropout\": 0,\n",
" \"feature_miss_rate_thres\": 0.85,\n",
" \"filter_features\": [\n",
" \"transcriptomics\",\n",
" \"crisprcas9\",\n",
" \"methylation\"\n",
" ],\n",
" \"filtered_encoder_only\": true,\n",
" \"gmvae_decay_temp\": true,\n",
" \"gmvae_decay_temp_rate\": 0.013862944,\n",
" \"gmvae_hard_gumbel\": 0.7936881144482251,\n",
" \"gmvae_hidden_size\": 935,\n",
" \"gmvae_init_temp\": 1.0,\n",
" \"gmvae_k\": 51,\n",
" \"gmvae_min_temp\": 0.5,\n",
" \"gmvae_views_logits\": 726,\n",
" \"hidden_dims\": [\n",
" 0.7\n",
" ],\n",
" \"labels\": [\n",
" \"tissue\",\n",
" \"mutations\",\n",
" \"fussions\",\n",
" \"msi\",\n",
" \"growth\"\n",
" ],\n",
" \"lambda_d\": 0.1,\n",
" \"lambda_od\": 0.01,\n",
" \"latent_dim\": 200,\n",
" \"learning_rate\": 0.0002,\n",
" \"load_run\": \"20241210_000556\",\n",
" \"model\": \"MOSA\",\n",
" \"n_folds\": 3,\n",
" \"num_epochs\": 500,\n",
" \"optimizer_type\": \"adam\",\n",
" \"probability\": 0.4,\n",
" \"reconstruction_loss\": \"mse\",\n",
" \"save_model\": true,\n",
" \"scheduler\": \"plateau\",\n",
" \"scheduler_factor\": 0.6,\n",
" \"scheduler_min_lr\": 1e-07,\n",
" \"scheduler_patience\": 7,\n",
" \"scheduler_threshold\": 0.0001,\n",
" \"skip_benchmarks\": false,\n",
" \"skip_cv\": true,\n",
" \"standardize\": true,\n",
" \"use_conditionals\": true,\n",
" \"verbose\": 0,\n",
" \"view_dropout\": 0.3,\n",
" \"view_latent_dim\": 0.25,\n",
" \"view_loss_recon_type\": [\n",
" \"mean\",\n",
" \"mean\",\n",
" \"mean\",\n",
" \"mean\",\n",
" \"mean\",\n",
" \"mean\",\n",
" \"macro\"\n",
" ],\n",
" \"view_loss_weights\": [\n",
" 1.0,\n",
" 1.0,\n",
" 1.0,\n",
" 1.0,\n",
" 1.0,\n",
" 1.0,\n",
" 1.0\n",
" ],\n",
" \"w_cat\": 0.01,\n",
" \"w_contrastive\": 0.005,\n",
" \"w_decay\": 0.0005,\n",
" \"w_gauss\": 0.0001,\n",
" \"w_kl\": 0.0001,\n",
" \"w_rec\": 1\n",
"}\n",
"DepMap23Q2 | Samples = 1,523 | Proteomics = 4,922 (0 masked) | Metabolomics = 225 (0 masked) | Drug response = 810 (0 masked) | CRISPR-Cas9 = 17,931 (12,718 masked) | Methylation = 14,608 (7,018 masked) | Transcriptomics = 15,278 (7,200 masked) | Copy number = 777 (0 masked) | Labels = 237\n"
]
}
],
"source": [
"hyperparameters = Hypers.read_hyperparameters(timestamp=TIMESTAMP)\n",
"clines_db = CLinesDatasetDepMap23Q2(\n",
" labels_names=hyperparameters[\"labels\"],\n",
" datasets=hyperparameters[\"datasets\"],\n",
" feature_miss_rate_thres=hyperparameters[\"feature_miss_rate_thres\"],\n",
" standardize=hyperparameters[\"standardize\"],\n",
" filter_features=hyperparameters[\"filter_features\"],\n",
" filtered_encoder_only=hyperparameters[\"filtered_encoder_only\"],\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"OMIC_PALLETS = {\n",
" \"conditionals\": \"#4c72b0\",\n",
" \"copynumber\": \"#dd8452\",\n",
" \"drugresponse\": \"#55a868\",\n",
" \"metabolomics\": \"#c44e52\",\n",
" \"proteomics\": \"#8172b3\",\n",
" \"crisprcas9\": \"#937860\",\n",
" \"transcriptomics\": \"#da8bc3\",\n",
" \"methylation\": \"#8c8c8c\",\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"OMIC_MARKERS = {\n",
" \"conditionals\": 0,\n",
" \"copynumber\": 1,\n",
" \"drugresponse\": 2,\n",
" \"metabolomics\": 3,\n",
" \"proteomics\": 4,\n",
" \"crisprcas9\": 5,\n",
" \"transcriptomics\": 6,\n",
" \"methylation\": 7,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"shap_values = pickle.load(\n",
" open(f\"./reports/vae/files/{TIMESTAMP}_explanation_latent.pkl\", \"rb\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"shap_values = shap_values.values"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"feature_names_all = []\n",
"for view_name in clines_db.view_names:\n",
" feature_names_all.append(\n",
" clines_db.features_mask[view_name][\n",
" clines_db.features_mask[view_name] == True\n",
" ].index.values\n",
" )\n",
"feature_names_all.append(clines_db.labels_name)\n",
"view_names = clines_db.view_names + [\"conditionals\"]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1523, 4922, 200)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_values[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"all_shap_df = []\n",
"\n",
"# Iterate through each latent dimension (now accessed from the last axis)\n",
"for latent_dim in range(shap_values[0].shape[-1]):\n",
" latent_dfs = []\n",
"\n",
" # Iterate through views (now the first dimension)\n",
" for i in range(len(view_names)):\n",
" view_name = view_names[i]\n",
" feature_names = feature_names_all[i]\n",
"\n",
" # Access the SHAP values for current view and latent dimension\n",
" # Shape is now [views, samples, features, latent_dim]\n",
" # So we index [i, :, :, latent_dim] to get values for current view and latent dimension\n",
" tmp_df = pd.DataFrame(\n",
" shap_values[i][:, :, latent_dim],\n",
" columns=feature_names,\n",
" index=clines_db.samples,\n",
" )\n",
"\n",
" # Add view name prefix to column names\n",
" tmp_df.columns = [f\"{view_names[i]}_{c}\" for c in tmp_df.columns]\n",
" latent_dfs.append(tmp_df)\n",
"\n",
" # Combine all views for this latent dimension\n",
" latent_dfs = pd.concat(latent_dfs, axis=1)\n",
" latent_dfs[\"latent_dim\"] = f\"latent_dim_{latent_dim}\"\n",
"\n",
" all_shap_df.append(latent_dfs)\n",
"\n",
"# Combine all latent dimensions\n",
"all_shap_df = pd.concat(all_shap_df, axis=0)\n",
"\n",
"# Reorder columns to put latent_dim first\n",
"cols = all_shap_df.columns.tolist()\n",
"cols = [cols[-1]] + cols[:-1]\n",
"all_shap_df = all_shap_df[cols]\n",
"all_shap_df.index.name = \"model_id\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# all_shap_df = []\n",
"# for latent_dim in range(len(shap_values)):\n",
"# shap_latent = shap_values[latent_dim]\n",
"# latent_dfs = []\n",
"# for i in range(len(view_names)):\n",
"# view_name = view_names[i]\n",
"# feature_names = feature_names_all[i]\n",
"# tmp_df = pd.DataFrame(shap_latent[i], columns=feature_names, index=clines_db.samples)\n",
"# tmp_df.columns = [f\"{view_names[i]}_{c}\" for c in tmp_df.columns]\n",
"# latent_dfs.append(tmp_df)\n",
"# latent_dfs = pd.concat(latent_dfs, axis=1)\n",
"# latent_dfs['latent_dim'] = f\"latent_dim_{latent_dim}\"\n",
" \n",
"# all_shap_df.append(latent_dfs)\n",
"# all_shap_df = pd.concat(all_shap_df, axis=0)\n",
"# cols = all_shap_df.columns.tolist()\n",
"# cols = [cols[-1]] + cols[:-1]\n",
"# all_shap_df = all_shap_df[cols]\n",
"# all_shap_df.index.name = 'model_id'"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"all_shap_df = all_shap_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(304600, 27854)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_shap_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>model_id</th>\n",
" <th>latent_dim</th>\n",
" <th>proteomics_AAAS</th>\n",
" <th>proteomics_AACS</th>\n",
" <th>proteomics_AAGAB</th>\n",
" <th>proteomics_AAK1</th>\n",
" <th>proteomics_AAMDC</th>\n",
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" <th>proteomics_ABCC3</th>\n",
" <th>proteomics_ABCC4</th>\n",
" <th>proteomics_ABCD1</th>\n",
" <th>proteomics_ABCD3</th>\n",
" <th>proteomics_ABCE1</th>\n",
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" <th>proteomics_ABLIM3</th>\n",
" <th>proteomics_ABRACL</th>\n",
" <th>proteomics_ABRAXAS2</th>\n",
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" <th>proteomics_ACAD8</th>\n",
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" <th>proteomics_ACADM</th>\n",
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" <th>proteomics_ACADVL</th>\n",
" <th>proteomics_ACAP2</th>\n",
" <th>...</th>\n",
" <th>conditionals_mut_PTPRB</th>\n",
" <th>conditionals_mut_PTPRC</th>\n",
" <th>conditionals_mut_PTPRK</th>\n",
" <th>conditionals_mut_PTPRT</th>\n",
" <th>conditionals_mut_QKI</th>\n",
" <th>conditionals_mut_RAC1</th>\n",
" <th>conditionals_mut_RASA1</th>\n",
" <th>conditionals_mut_RASA2</th>\n",
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" <th>conditionals_mut_RGS7</th>\n",
" <th>conditionals_mut_RHOA</th>\n",
" <th>conditionals_mut_RNF43</th>\n",
" <th>conditionals_mut_RNF6</th>\n",
" <th>conditionals_mut_RPL22</th>\n",
" <th>conditionals_mut_SDHA</th>\n",
" <th>conditionals_mut_SETD1B</th>\n",
" <th>conditionals_mut_SETD2</th>\n",
" <th>conditionals_mut_SMAD4</th>\n",
" <th>conditionals_mut_SMARCA4</th>\n",
" <th>conditionals_mut_SMARCB1</th>\n",
" <th>conditionals_mut_SOX9</th>\n",
" <th>conditionals_mut_SPEN</th>\n",
" <th>conditionals_mut_STAG2</th>\n",
" <th>conditionals_mut_STAT5B</th>\n",
" <th>conditionals_mut_STK11</th>\n",
" <th>conditionals_mut_TAF15</th>\n",
" <th>conditionals_mut_TET1</th>\n",
" <th>conditionals_mut_TET2</th>\n",
" <th>conditionals_mut_TGFBR2</th>\n",
" <th>conditionals_mut_TNC</th>\n",
" <th>conditionals_mut_TP53</th>\n",
" <th>conditionals_mut_TRIM24</th>\n",
" <th>conditionals_mut_TRRAP</th>\n",
" <th>conditionals_mut_TSC2</th>\n",
" <th>conditionals_mut_UBR5</th>\n",
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" <td>-0.00019</td>\n",
" <td>0.000000</td>\n",
" <td>0.000033</td>\n",
" <td>0.000214</td>\n",
" <td>0.000000</td>\n",
" <td>0.000081</td>\n",
" <td>0.000101</td>\n",
" <td>-0.000122</td>\n",
" <td>0.00000</td>\n",
" <td>0.000174</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000108</td>\n",
" <td>0.000000</td>\n",
" <td>0.008310</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000105</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000381</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000188</td>\n",
" <td>0.000676</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000098</td>\n",
" <td>0.000036</td>\n",
" <td>0.000041</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SIDM00003</td>\n",
" <td>latent_dim_0</td>\n",
" <td>-0.000099</td>\n",
" <td>0.000012</td>\n",
" <td>-0.000063</td>\n",
" <td>0.000198</td>\n",
" <td>0.000093</td>\n",
" <td>-0.000081</td>\n",
" <td>0.000063</td>\n",
" <td>0.000105</td>\n",
" <td>0.000053</td>\n",
" <td>-0.000011</td>\n",
" <td>-0.000022</td>\n",
" <td>0.000049</td>\n",
" <td>-0.000095</td>\n",
" <td>0.000089</td>\n",
" <td>-0.000024</td>\n",
" <td>0.000007</td>\n",
" <td>0.000047</td>\n",
" <td>-0.000011</td>\n",
" <td>-0.000111</td>\n",
" <td>-0.000235</td>\n",
" <td>0.000165</td>\n",
" <td>0.000004</td>\n",
" <td>0.000152</td>\n",
" <td>0.000153</td>\n",
" <td>-0.000223</td>\n",
" <td>-0.000241</td>\n",
" <td>0.000010</td>\n",
" <td>0.000256</td>\n",
" <td>-0.000120</td>\n",
" <td>0.000020</td>\n",
" <td>-0.000408</td>\n",
" <td>0.000007</td>\n",
" <td>-0.000112</td>\n",
" <td>-0.000154</td>\n",
" <td>-0.000334</td>\n",
" <td>0.000199</td>\n",
" <td>0.000018</td>\n",
" <td>-0.000113</td>\n",
" <td>-0.000279</td>\n",
" <td>0.000515</td>\n",
" <td>0.000282</td>\n",
" <td>-0.000122</td>\n",
" <td>0.000013</td>\n",
" <td>-0.000620</td>\n",
" <td>-0.000198</td>\n",
" <td>0.000077</td>\n",
" <td>-0.000343</td>\n",
" <td>0.000090</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000187</td>\n",
" <td>0.000000</td>\n",
" <td>0.000479</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000090</td>\n",
" <td>0.000645</td>\n",
" <td>0.000000</td>\n",
" <td>0.000093</td>\n",
" <td>0.000300</td>\n",
" <td>-0.000601</td>\n",
" <td>0.00000</td>\n",
" <td>-0.000684</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000216</td>\n",
" <td>0.000067</td>\n",
" <td>0.000068</td>\n",
" <td>-0.000095</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000190</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000426</td>\n",
" <td>0.000000</td>\n",
" <td>0.005435</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000089</td>\n",
" <td>-7.063922e-06</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000438</td>\n",
" <td>0.000000</td>\n",
" <td>0.000064</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.012666</td>\n",
" <td>-0.000008</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000184</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SIDM00005</td>\n",
" <td>latent_dim_0</td>\n",
" <td>-0.000034</td>\n",
" <td>-0.000008</td>\n",
" <td>0.000104</td>\n",
" <td>0.000204</td>\n",
" <td>0.000032</td>\n",
" <td>-0.000009</td>\n",
" <td>-0.000014</td>\n",
" <td>0.000099</td>\n",
" <td>0.000013</td>\n",
" <td>-0.000033</td>\n",
" <td>0.000029</td>\n",
" <td>-0.000014</td>\n",
" <td>-0.000071</td>\n",
" <td>-0.000036</td>\n",
" <td>-0.000435</td>\n",
" <td>0.000028</td>\n",
" <td>0.000309</td>\n",
" <td>0.000032</td>\n",
" <td>-0.000026</td>\n",
" <td>0.000037</td>\n",
" <td>0.000319</td>\n",
" <td>0.000144</td>\n",
" <td>0.000391</td>\n",
" <td>0.000423</td>\n",
" <td>0.000233</td>\n",
" <td>-0.000016</td>\n",
" <td>-0.000046</td>\n",
" <td>-0.000207</td>\n",
" <td>-0.000593</td>\n",
" <td>-0.000044</td>\n",
" <td>0.000401</td>\n",
" <td>0.000206</td>\n",
" <td>-0.000621</td>\n",
" <td>0.000024</td>\n",
" <td>-0.000101</td>\n",
" <td>-0.000127</td>\n",
" <td>0.000085</td>\n",
" <td>0.000018</td>\n",
" <td>-0.000103</td>\n",
" <td>-0.000038</td>\n",
" <td>0.000109</td>\n",
" <td>-0.000144</td>\n",
" <td>0.000049</td>\n",
" <td>-0.000229</td>\n",
" <td>0.000002</td>\n",
" <td>-0.000044</td>\n",
" <td>-0.000089</td>\n",
" <td>0.000045</td>\n",
" <td>...</td>\n",
" <td>0.000038</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000021</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000060</td>\n",
" <td>0.000000</td>\n",
" <td>0.000399</td>\n",
" <td>-0.000015</td>\n",
" <td>-0.000158</td>\n",
" <td>0.000434</td>\n",
" <td>0.000103</td>\n",
" <td>0.000000</td>\n",
" <td>0.000108</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000201</td>\n",
" <td>0.000159</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000211</td>\n",
" <td>0.00000</td>\n",
" <td>0.000078</td>\n",
" <td>0.000182</td>\n",
" <td>0.000000</td>\n",
" <td>0.000037</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.004226</td>\n",
" <td>0.000072</td>\n",
" <td>-0.000143</td>\n",
" <td>7.357006e-07</td>\n",
" <td>0.000122</td>\n",
" <td>-0.000171</td>\n",
" <td>-0.000002</td>\n",
" <td>0.000000</td>\n",
" <td>0.000014</td>\n",
" <td>0.000044</td>\n",
" <td>0.000161</td>\n",
" <td>0.000000</td>\n",
" <td>0.000138</td>\n",
" <td>-0.000032</td>\n",
" <td>-0.000099</td>\n",
" <td>0.000042</td>\n",
" <td>0.000024</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SIDM00006</td>\n",
" <td>latent_dim_0</td>\n",
" <td>0.000061</td>\n",
" <td>0.000027</td>\n",
" <td>0.000019</td>\n",
" <td>-0.000177</td>\n",
" <td>-0.000002</td>\n",
" <td>0.000061</td>\n",
" <td>-0.000239</td>\n",
" <td>0.000336</td>\n",
" <td>0.000002</td>\n",
" <td>0.000013</td>\n",
" <td>0.000048</td>\n",
" <td>0.000110</td>\n",
" <td>-0.000192</td>\n",
" <td>0.000148</td>\n",
" <td>-0.000064</td>\n",
" <td>-0.000047</td>\n",
" <td>-0.000874</td>\n",
" <td>-0.000095</td>\n",
" <td>-0.000118</td>\n",
" <td>-0.000036</td>\n",
" <td>-0.000277</td>\n",
" <td>0.000050</td>\n",
" <td>-0.000553</td>\n",
" <td>0.000167</td>\n",
" <td>0.000216</td>\n",
" <td>-0.000290</td>\n",
" <td>-0.000049</td>\n",
" <td>0.000309</td>\n",
" <td>0.000908</td>\n",
" <td>0.000037</td>\n",
" <td>-0.000371</td>\n",
" <td>-0.000070</td>\n",
" <td>-0.000282</td>\n",
" <td>0.000120</td>\n",
" <td>0.000020</td>\n",
" <td>0.000163</td>\n",
" <td>0.000163</td>\n",
" <td>-0.000123</td>\n",
" <td>-0.000075</td>\n",
" <td>0.000393</td>\n",
" <td>-0.000244</td>\n",
" <td>0.000164</td>\n",
" <td>-0.000077</td>\n",
" <td>0.000074</td>\n",
" <td>-0.000295</td>\n",
" <td>-0.000043</td>\n",
" <td>-0.000299</td>\n",
" <td>0.000052</td>\n",
" <td>...</td>\n",
" <td>0.000142</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.004726</td>\n",
" <td>0.000000</td>\n",
" <td>0.003685</td>\n",
" <td>0.000000</td>\n",
" <td>0.000128</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000559</td>\n",
" <td>0.00000</td>\n",
" <td>-0.000225</td>\n",
" <td>0.000000</td>\n",
" <td>0.000321</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000207</td>\n",
" <td>0.00000</td>\n",
" <td>0.000186</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000003</td>\n",
" <td>0.000155</td>\n",
" <td>0.000000</td>\n",
" <td>0.006846</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000222</td>\n",
" <td>3.255997e-05</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000450</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000063</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>SIDM00007</td>\n",
" <td>latent_dim_0</td>\n",
" <td>0.000060</td>\n",
" <td>-0.000043</td>\n",
" <td>-0.000061</td>\n",
" <td>0.000245</td>\n",
" <td>0.000106</td>\n",
" <td>-0.000063</td>\n",
" <td>0.000239</td>\n",
" <td>-0.000314</td>\n",
" <td>0.000070</td>\n",
" <td>-0.000087</td>\n",
" <td>-0.000107</td>\n",
" <td>0.000127</td>\n",
" <td>-0.000040</td>\n",
" <td>0.000274</td>\n",
" <td>0.000005</td>\n",
" <td>0.000389</td>\n",
" <td>0.000263</td>\n",
" <td>0.000002</td>\n",
" <td>-0.000080</td>\n",
" <td>0.000031</td>\n",
" <td>-0.000016</td>\n",
" <td>0.000292</td>\n",
" <td>0.000654</td>\n",
" <td>0.000467</td>\n",
" <td>0.000236</td>\n",
" <td>0.000128</td>\n",
" <td>-0.000048</td>\n",
" <td>-0.000401</td>\n",
" <td>0.000113</td>\n",
" <td>0.000023</td>\n",
" <td>0.000685</td>\n",
" <td>0.000439</td>\n",
" <td>-0.000991</td>\n",
" <td>0.000015</td>\n",
" <td>0.000025</td>\n",
" <td>0.000060</td>\n",
" <td>-0.000053</td>\n",
" <td>-0.000054</td>\n",
" <td>-0.000109</td>\n",
" <td>0.000294</td>\n",
" <td>-0.000439</td>\n",
" <td>-0.000129</td>\n",
" <td>0.000133</td>\n",
" <td>-0.000300</td>\n",
" <td>0.000234</td>\n",
" <td>-0.000028</td>\n",
" <td>0.000610</td>\n",
" <td>-0.000254</td>\n",
" <td>...</td>\n",
" <td>0.000029</td>\n",
" <td>0.000291</td>\n",
" <td>0.000487</td>\n",
" <td>0.000130</td>\n",
" <td>5.952346e-06</td>\n",
" <td>0.000000</td>\n",
" <td>0.000061</td>\n",
" <td>-0.000068</td>\n",
" <td>-0.005149</td>\n",
" <td>-0.000009</td>\n",
" <td>-0.000148</td>\n",
" <td>0.000000</td>\n",
" <td>0.000549</td>\n",
" <td>0.000233</td>\n",
" <td>0.001051</td>\n",
" <td>-0.001857</td>\n",
" <td>0.00000</td>\n",
" <td>-0.000611</td>\n",
" <td>0.000066</td>\n",
" <td>0.000237</td>\n",
" <td>0.000000</td>\n",
" <td>0.000063</td>\n",
" <td>0.000063</td>\n",
" <td>-0.000133</td>\n",
" <td>0.00034</td>\n",
" <td>0.000259</td>\n",
" <td>0.000305</td>\n",
" <td>0.000046</td>\n",
" <td>-0.000010</td>\n",
" <td>0.000080</td>\n",
" <td>0.000129</td>\n",
" <td>0.004512</td>\n",
" <td>0.000082</td>\n",
" <td>-0.000121</td>\n",
" <td>3.720920e-05</td>\n",
" <td>0.000309</td>\n",
" <td>0.000000</td>\n",
" <td>0.000044</td>\n",
" <td>-0.000209</td>\n",
" <td>0.000141</td>\n",
" <td>0.000088</td>\n",
" <td>0.000257</td>\n",
" <td>0.000141</td>\n",
" <td>-0.011337</td>\n",
" <td>-0.000047</td>\n",
" <td>0.000000</td>\n",
" <td>0.000039</td>\n",
" <td>0.000062</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27854 columns</p>\n",
"</div>"
],
"text/plain": [
" model_id latent_dim proteomics_AAAS proteomics_AACS \\\n",
"0 SIDM00001 latent_dim_0 -0.000048 0.000026 \n",
"1 SIDM00003 latent_dim_0 -0.000099 0.000012 \n",
"2 SIDM00005 latent_dim_0 -0.000034 -0.000008 \n",
"3 SIDM00006 latent_dim_0 0.000061 0.000027 \n",
"4 SIDM00007 latent_dim_0 0.000060 -0.000043 \n",
"\n",
" proteomics_AAGAB proteomics_AAK1 proteomics_AAMDC proteomics_AAMP \\\n",
"0 -0.000059 0.000152 -0.000017 -0.000043 \n",
"1 -0.000063 0.000198 0.000093 -0.000081 \n",
"2 0.000104 0.000204 0.000032 -0.000009 \n",
"3 0.000019 -0.000177 -0.000002 0.000061 \n",
"4 -0.000061 0.000245 0.000106 -0.000063 \n",
"\n",
" proteomics_AARS1 proteomics_AARS2 proteomics_AARSD1 proteomics_AASDHPPT \\\n",
"0 0.000092 -0.000026 -0.000007 -0.000102 \n",
"1 0.000063 0.000105 0.000053 -0.000011 \n",
"2 -0.000014 0.000099 0.000013 -0.000033 \n",
"3 -0.000239 0.000336 0.000002 0.000013 \n",
"4 0.000239 -0.000314 0.000070 -0.000087 \n",
"\n",
" proteomics_AASS proteomics_AATF proteomics_ABAT proteomics_ABCB10 \\\n",
"0 0.000070 0.000008 0.000069 -0.000150 \n",
"1 -0.000022 0.000049 -0.000095 0.000089 \n",
"2 0.000029 -0.000014 -0.000071 -0.000036 \n",
"3 0.000048 0.000110 -0.000192 0.000148 \n",
"4 -0.000107 0.000127 -0.000040 0.000274 \n",
"\n",
" proteomics_ABCB6 proteomics_ABCB7 proteomics_ABCC1 proteomics_ABCC3 \\\n",
"0 0.000191 0.000394 0.000420 0.000031 \n",
"1 -0.000024 0.000007 0.000047 -0.000011 \n",
"2 -0.000435 0.000028 0.000309 0.000032 \n",
"3 -0.000064 -0.000047 -0.000874 -0.000095 \n",
"4 0.000005 0.000389 0.000263 0.000002 \n",
"\n",
" proteomics_ABCC4 proteomics_ABCD1 proteomics_ABCD3 proteomics_ABCE1 \\\n",
"0 -0.000008 -0.000047 0.000112 0.000136 \n",
"1 -0.000111 -0.000235 0.000165 0.000004 \n",
"2 -0.000026 0.000037 0.000319 0.000144 \n",
"3 -0.000118 -0.000036 -0.000277 0.000050 \n",
"4 -0.000080 0.000031 -0.000016 0.000292 \n",
"\n",
" proteomics_ABCF1 proteomics_ABCF2 proteomics_ABCF3 proteomics_ABHD10 \\\n",
"0 0.000363 0.000081 -0.000079 0.000171 \n",
"1 0.000152 0.000153 -0.000223 -0.000241 \n",
"2 0.000391 0.000423 0.000233 -0.000016 \n",
"3 -0.000553 0.000167 0.000216 -0.000290 \n",
"4 0.000654 0.000467 0.000236 0.000128 \n",
"\n",
" proteomics_ABHD11 proteomics_ABHD12 proteomics_ABHD14B \\\n",
"0 -0.000034 -0.000095 0.000240 \n",
"1 0.000010 0.000256 -0.000120 \n",
"2 -0.000046 -0.000207 -0.000593 \n",
"3 -0.000049 0.000309 0.000908 \n",
"4 -0.000048 -0.000401 0.000113 \n",
"\n",
" proteomics_ABHD16A proteomics_ABI1 proteomics_ABI2 proteomics_ABLIM1 \\\n",
"0 0.000041 -0.000318 -0.000110 0.000227 \n",
"1 0.000020 -0.000408 0.000007 -0.000112 \n",
"2 -0.000044 0.000401 0.000206 -0.000621 \n",
"3 0.000037 -0.000371 -0.000070 -0.000282 \n",
"4 0.000023 0.000685 0.000439 -0.000991 \n",
"\n",
" proteomics_ABLIM3 proteomics_ABRACL proteomics_ABRAXAS2 proteomics_ABT1 \\\n",
"0 -0.000002 -0.000001 0.000132 0.000132 \n",
"1 -0.000154 -0.000334 0.000199 0.000018 \n",
"2 0.000024 -0.000101 -0.000127 0.000085 \n",
"3 0.000120 0.000020 0.000163 0.000163 \n",
"4 0.000015 0.000025 0.000060 -0.000053 \n",
"\n",
" proteomics_ACAA1 proteomics_ACAA2 proteomics_ACACA proteomics_ACAD10 \\\n",
"0 0.000050 0.000014 0.000164 0.000247 \n",
"1 -0.000113 -0.000279 0.000515 0.000282 \n",
"2 0.000018 -0.000103 -0.000038 0.000109 \n",
"3 -0.000123 -0.000075 0.000393 -0.000244 \n",
"4 -0.000054 -0.000109 0.000294 -0.000439 \n",
"\n",
" proteomics_ACAD8 proteomics_ACAD9 proteomics_ACADM proteomics_ACADS \\\n",
"0 -0.000138 0.000012 0.000321 0.000420 \n",
"1 -0.000122 0.000013 -0.000620 -0.000198 \n",
"2 -0.000144 0.000049 -0.000229 0.000002 \n",
"3 0.000164 -0.000077 0.000074 -0.000295 \n",
"4 -0.000129 0.000133 -0.000300 0.000234 \n",
"\n",
" proteomics_ACADSB proteomics_ACADVL proteomics_ACAP2 ... \\\n",
"0 -0.000012 -0.000015 0.000417 ... \n",
"1 0.000077 -0.000343 0.000090 ... \n",
"2 -0.000044 -0.000089 0.000045 ... \n",
"3 -0.000043 -0.000299 0.000052 ... \n",
"4 -0.000028 0.000610 -0.000254 ... \n",
"\n",
" conditionals_mut_PTPRB conditionals_mut_PTPRC conditionals_mut_PTPRK \\\n",
"0 0.000007 0.000082 0.000000 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000038 0.000000 0.000000 \n",
"3 0.000142 0.000000 0.000000 \n",
"4 0.000029 0.000291 0.000487 \n",
"\n",
" conditionals_mut_PTPRT conditionals_mut_QKI conditionals_mut_RAC1 \\\n",
"0 0.000092 -9.507989e-07 -0.000172 \n",
"1 0.000000 0.000000e+00 0.000000 \n",
"2 -0.000021 0.000000e+00 0.000000 \n",
"3 0.000000 0.000000e+00 0.000000 \n",
"4 0.000130 5.952346e-06 0.000000 \n",
"\n",
" conditionals_mut_RASA1 conditionals_mut_RASA2 conditionals_mut_RB1 \\\n",
"0 0.000061 0.000000 0.000000 \n",
"1 0.000187 0.000000 0.000479 \n",
"2 0.000060 0.000000 0.000399 \n",
"3 0.000000 0.000000 -0.004726 \n",
"4 0.000061 -0.000068 -0.005149 \n",
"\n",
" conditionals_mut_RGPD3 conditionals_mut_RGS7 conditionals_mut_RHOA \\\n",
"0 0.000000 0.000000 0.000242 \n",
"1 0.000000 -0.000090 0.000645 \n",
"2 -0.000015 -0.000158 0.000434 \n",
"3 0.000000 0.003685 0.000000 \n",
"4 -0.000009 -0.000148 0.000000 \n",
"\n",
" conditionals_mut_RNF43 conditionals_mut_RNF6 conditionals_mut_RPL22 \\\n",
"0 0.000146 0.000000 0.000223 \n",
"1 0.000000 0.000093 0.000300 \n",
"2 0.000103 0.000000 0.000108 \n",
"3 0.000128 0.000000 0.000000 \n",
"4 0.000549 0.000233 0.001051 \n",
"\n",
" conditionals_mut_SDHA conditionals_mut_SETD1B conditionals_mut_SETD2 \\\n",
"0 -0.000920 -0.00019 0.000000 \n",
"1 -0.000601 0.00000 -0.000684 \n",
"2 0.000000 0.00000 0.000000 \n",
"3 -0.000559 0.00000 -0.000225 \n",
"4 -0.001857 0.00000 -0.000611 \n",
"\n",
" conditionals_mut_SMAD4 conditionals_mut_SMARCA4 conditionals_mut_SMARCB1 \\\n",
"0 0.000033 0.000214 0.000000 \n",
"1 0.000000 0.000000 0.000216 \n",
"2 0.000000 0.000201 0.000159 \n",
"3 0.000000 0.000321 0.000000 \n",
"4 0.000066 0.000237 0.000000 \n",
"\n",
" conditionals_mut_SOX9 conditionals_mut_SPEN conditionals_mut_STAG2 \\\n",
"0 0.000081 0.000101 -0.000122 \n",
"1 0.000067 0.000068 -0.000095 \n",
"2 0.000000 0.000000 -0.000211 \n",
"3 0.000000 0.000000 -0.000207 \n",
"4 0.000063 0.000063 -0.000133 \n",
"\n",
" conditionals_mut_STAT5B conditionals_mut_STK11 conditionals_mut_TAF15 \\\n",
"0 0.00000 0.000174 0.000000 \n",
"1 0.00000 0.000000 0.000190 \n",
"2 0.00000 0.000078 0.000182 \n",
"3 0.00000 0.000186 0.000000 \n",
"4 0.00034 0.000259 0.000305 \n",
"\n",
" conditionals_mut_TET1 conditionals_mut_TET2 conditionals_mut_TGFBR2 \\\n",
"0 0.000000 0.000000 0.000108 \n",
"1 0.000000 0.000000 0.000426 \n",
"2 0.000000 0.000037 0.000000 \n",
"3 0.000000 -0.000003 0.000155 \n",
"4 0.000046 -0.000010 0.000080 \n",
"\n",
" conditionals_mut_TNC conditionals_mut_TP53 conditionals_mut_TRIM24 \\\n",
"0 0.000000 0.008310 0.000000 \n",
"1 0.000000 0.005435 0.000000 \n",
"2 0.000000 -0.004226 0.000072 \n",
"3 0.000000 0.006846 0.000000 \n",
"4 0.000129 0.004512 0.000082 \n",
"\n",
" conditionals_mut_TRRAP conditionals_mut_TSC2 conditionals_mut_UBR5 \\\n",
"0 0.000000 0.000000e+00 0.000000 \n",
"1 -0.000089 -7.063922e-06 0.000000 \n",
"2 -0.000143 7.357006e-07 0.000122 \n",
"3 -0.000222 3.255997e-05 0.000000 \n",
"4 -0.000121 3.720920e-05 0.000309 \n",
"\n",
" conditionals_mut_USP8 conditionals_mut_USP9X conditionals_mut_VHL \\\n",
"0 -0.000105 0.000000 -0.000381 \n",
"1 0.000000 0.000000 -0.000438 \n",
"2 -0.000171 -0.000002 0.000000 \n",
"3 0.000000 0.000000 -0.000450 \n",
"4 0.000000 0.000044 -0.000209 \n",
"\n",
" conditionals_mut_WNK2 conditionals_mut_WNK4 conditionals_mut_WRN \\\n",
"0 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000064 0.000000 \n",
"2 0.000014 0.000044 0.000161 \n",
"3 0.000000 0.000000 0.000000 \n",
"4 0.000141 0.000088 0.000257 \n",
"\n",
" conditionals_mut_ZEB1 conditionals_mut_ZFHX3 conditionals_mut_ZNF208 \\\n",
"0 0.000188 0.000676 0.000000 \n",
"1 0.000000 -0.012666 -0.000008 \n",
"2 0.000000 0.000138 -0.000032 \n",
"3 0.000000 0.000000 -0.000063 \n",
"4 0.000141 -0.011337 -0.000047 \n",
"\n",
" conditionals_mut_ZNF429 conditionals_mut_ZNF626 conditionals_mut_ZNF93 \\\n",
"0 -0.000098 0.000036 0.000041 \n",
"1 0.000000 0.000000 0.000184 \n",
"2 -0.000099 0.000042 0.000024 \n",
"3 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.000039 0.000062 \n",
"\n",
" conditionals_mut_ZNRF3 conditionals_msi_status \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
"[5 rows x 27854 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_shap_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"all_shap_df.iloc[:, 2:] = all_shap_df.iloc[:, 2:].abs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# aggregate latent"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"shap_latent_sum_df = all_shap_df.drop(columns=['latent_dim']).groupby('model_id').sum()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"shap_latent_sum_df.to_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_latent_shap_values_df_sum.csv.gz\", compression=\"gzip\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"shap_latent_sum_df = pd.read_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_latent_shap_values_df_sum.csv.gz\", compression=\"gzip\", index_col=0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_df = (\n",
" shap_latent_sum_df.mean()\n",
" .sort_values(ascending=False)\n",
" .reset_index(name=\"importance\")\n",
")\n",
"global_feature_importance_df.rename(columns={\"index\": \"feature\"}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_df.to_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_latent_shap_values_df_sum_global.csv\", index=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_df = pd.read_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_latent_shap_values_df_sum_global.csv\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>copynumber_RSPH10B2</td>\n",
" <td>2.927180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>copynumber_CDKN2A</td>\n",
" <td>1.216689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>copynumber_PMS2</td>\n",
" <td>1.207333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>conditionals_day4_day1_ratio</td>\n",
" <td>1.181447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>copynumber_ZMYM2</td>\n",
" <td>1.164136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>conditionals_mut_TP53</td>\n",
" <td>1.140086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>conditionals_tissue_Haematopoietic and Lymphoid</td>\n",
" <td>1.126392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>copynumber_NBEA</td>\n",
" <td>1.092891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>copynumber_UGT2B17</td>\n",
" <td>0.953780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>copynumber_NFIB</td>\n",
" <td>0.916468</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance\n",
"0 copynumber_RSPH10B2 2.927180\n",
"1 copynumber_CDKN2A 1.216689\n",
"2 copynumber_PMS2 1.207333\n",
"3 conditionals_day4_day1_ratio 1.181447\n",
"4 copynumber_ZMYM2 1.164136\n",
"5 conditionals_mut_TP53 1.140086\n",
"6 conditionals_tissue_Haematopoietic and Lymphoid 1.126392\n",
"7 copynumber_NBEA 1.092891\n",
"8 copynumber_UGT2B17 0.953780\n",
"9 copynumber_NFIB 0.916468"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>metabolomics_1-methylnicotinamide</td>\n",
" <td>1.269368</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>metabolomics_C52:2 TAG</td>\n",
" <td>1.252069</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>metabolomics_C52:3 TAG</td>\n",
" <td>1.250422</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>metabolomics_C50:2 TAG</td>\n",
" <td>1.195308</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>metabolomics_C54:3 TAG</td>\n",
" <td>1.188903</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>metabolomics_C16:1 SM</td>\n",
" <td>1.144335</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>metabolomics_C48:2 TAG</td>\n",
" <td>1.110043</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>metabolomics_C50:1 TAG</td>\n",
" <td>1.063773</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>metabolomics_C14:0 SM</td>\n",
" <td>1.056895</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>metabolomics_C54:4 TAG</td>\n",
" <td>1.056064</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"23 metabolomics_1-methylnicotinamide 1.269368 metabolomics\n",
"25 metabolomics_C52:2 TAG 1.252069 metabolomics\n",
"26 metabolomics_C52:3 TAG 1.250422 metabolomics\n",
"30 metabolomics_C50:2 TAG 1.195308 metabolomics\n",
"31 metabolomics_C54:3 TAG 1.188903 metabolomics\n",
"38 metabolomics_C16:1 SM 1.144335 metabolomics\n",
"41 metabolomics_C48:2 TAG 1.110043 metabolomics\n",
"51 metabolomics_C50:1 TAG 1.063773 metabolomics\n",
"52 metabolomics_C14:0 SM 1.056895 metabolomics\n",
"53 metabolomics_C54:4 TAG 1.056064 metabolomics"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"metabolom\")\n",
"].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>conditionals_day4_day1_ratio</td>\n",
" <td>1.181447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>conditionals_mut_TP53</td>\n",
" <td>1.140086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>conditionals_tissue_Haematopoietic and Lymphoid</td>\n",
" <td>1.126392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>conditionals_tissue_Lung</td>\n",
" <td>0.821399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>conditionals_doubling_time_hours</td>\n",
" <td>0.327485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>838</th>\n",
" <td>conditionals_tissue_Skin</td>\n",
" <td>0.320393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>conditionals_mut_KMT2C</td>\n",
" <td>0.251876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2180</th>\n",
" <td>conditionals_tissue_Central Nervous System</td>\n",
" <td>0.247180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3698</th>\n",
" <td>conditionals_mut_KRAS</td>\n",
" <td>0.223838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5268</th>\n",
" <td>conditionals_mut_SDHA</td>\n",
" <td>0.206595</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance\n",
"3 conditionals_day4_day1_ratio 1.181447\n",
"5 conditionals_mut_TP53 1.140086\n",
"6 conditionals_tissue_Haematopoietic and Lymphoid 1.126392\n",
"14 conditionals_tissue_Lung 0.821399\n",
"800 conditionals_doubling_time_hours 0.327485\n",
"838 conditionals_tissue_Skin 0.320393\n",
"1975 conditionals_mut_KMT2C 0.251876\n",
"2180 conditionals_tissue_Central Nervous System 0.247180\n",
"3698 conditionals_mut_KRAS 0.223838\n",
"5268 conditionals_mut_SDHA 0.206595"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"conditionals\")\n",
"].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>787</th>\n",
" <td>transcriptomics_CDKN2A</td>\n",
" <td>0.330626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1019</th>\n",
" <td>transcriptomics_USP9Y</td>\n",
" <td>0.297834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1148</th>\n",
" <td>transcriptomics_CDKN2B</td>\n",
" <td>0.285237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1154</th>\n",
" <td>transcriptomics_C5</td>\n",
" <td>0.284975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1180</th>\n",
" <td>transcriptomics_M1AP</td>\n",
" <td>0.283337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1297</th>\n",
" <td>transcriptomics_PWP2</td>\n",
" <td>0.276248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>transcriptomics_LPCAT1</td>\n",
" <td>0.276126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1308</th>\n",
" <td>transcriptomics_GATD3A</td>\n",
" <td>0.275667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1386</th>\n",
" <td>transcriptomics_SCARB1</td>\n",
" <td>0.271782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1419</th>\n",
" <td>transcriptomics_RPS27L</td>\n",
" <td>0.270263</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance\n",
"787 transcriptomics_CDKN2A 0.330626\n",
"1019 transcriptomics_USP9Y 0.297834\n",
"1148 transcriptomics_CDKN2B 0.285237\n",
"1154 transcriptomics_C5 0.284975\n",
"1180 transcriptomics_M1AP 0.283337\n",
"1297 transcriptomics_PWP2 0.276248\n",
"1299 transcriptomics_LPCAT1 0.276126\n",
"1308 transcriptomics_GATD3A 0.275667\n",
"1386 transcriptomics_SCARB1 0.271782\n",
"1419 transcriptomics_RPS27L 0.270263"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"transcriptomics\")\n",
"].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4431</th>\n",
" <td>transcriptomics_NNMT</td>\n",
" <td>0.346385</td>\n",
" <td>transcriptomics</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"4431 transcriptomics_NNMT 0.346385 transcriptomics"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rna_importance_df = global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"transcriptomics\")\n",
"].reset_index(drop=True)\n",
"rna_importance_df[rna_importance_df[\"feature\"].str.contains(\"NNMT\")]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8078, 3)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rna_importance_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [feature, importance, omic_layer]\n",
"Index: []"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"copynumbercas9\")\n",
"].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>738</th>\n",
" <td>proteomics_HBA1</td>\n",
" <td>0.340855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>908</th>\n",
" <td>proteomics_PRKDC</td>\n",
" <td>0.310210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>921</th>\n",
" <td>proteomics_RPL37A</td>\n",
" <td>0.308294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>955</th>\n",
" <td>proteomics_GCDH</td>\n",
" <td>0.304455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>969</th>\n",
" <td>proteomics_BANF1</td>\n",
" <td>0.303244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1025</th>\n",
" <td>proteomics_NUP62</td>\n",
" <td>0.297157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1066</th>\n",
" <td>proteomics_GCLM</td>\n",
" <td>0.293002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1089</th>\n",
" <td>proteomics_DCXR</td>\n",
" <td>0.290456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1170</th>\n",
" <td>proteomics_RBM3</td>\n",
" <td>0.283758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1176</th>\n",
" <td>proteomics_PRPF4B</td>\n",
" <td>0.283572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1201</th>\n",
" <td>proteomics_MTHFD1L</td>\n",
" <td>0.281854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1206</th>\n",
" <td>proteomics_LYRM7</td>\n",
" <td>0.281622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1211</th>\n",
" <td>proteomics_RAD50</td>\n",
" <td>0.281154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1227</th>\n",
" <td>proteomics_G6PD</td>\n",
" <td>0.280031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1250</th>\n",
" <td>proteomics_LONP1</td>\n",
" <td>0.278799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1264</th>\n",
" <td>proteomics_NUP43</td>\n",
" <td>0.278070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1287</th>\n",
" <td>proteomics_MANF</td>\n",
" <td>0.276713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>proteomics_WDR33</td>\n",
" <td>0.275966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1305</th>\n",
" <td>proteomics_STX7</td>\n",
" <td>0.275827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1330</th>\n",
" <td>proteomics_MRE11</td>\n",
" <td>0.274413</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance\n",
"738 proteomics_HBA1 0.340855\n",
"908 proteomics_PRKDC 0.310210\n",
"921 proteomics_RPL37A 0.308294\n",
"955 proteomics_GCDH 0.304455\n",
"969 proteomics_BANF1 0.303244\n",
"1025 proteomics_NUP62 0.297157\n",
"1066 proteomics_GCLM 0.293002\n",
"1089 proteomics_DCXR 0.290456\n",
"1170 proteomics_RBM3 0.283758\n",
"1176 proteomics_PRPF4B 0.283572\n",
"1201 proteomics_MTHFD1L 0.281854\n",
"1206 proteomics_LYRM7 0.281622\n",
"1211 proteomics_RAD50 0.281154\n",
"1227 proteomics_G6PD 0.280031\n",
"1250 proteomics_LONP1 0.278799\n",
"1264 proteomics_NUP43 0.278070\n",
"1287 proteomics_MANF 0.276713\n",
"1301 proteomics_WDR33 0.275966\n",
"1305 proteomics_STX7 0.275827\n",
"1330 proteomics_MRE11 0.274413"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"proteomics\")\n",
"].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13587</th>\n",
" <td>methylation_FOXD4L3</td>\n",
" <td>0.333646</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13969</th>\n",
" <td>methylation_UCK1</td>\n",
" <td>0.327589</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14849</th>\n",
" <td>methylation_SNORA81</td>\n",
" <td>0.313605</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14850</th>\n",
" <td>methylation_MIR1248</td>\n",
" <td>0.313605</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15076</th>\n",
" <td>methylation_ADHFE1</td>\n",
" <td>0.309721</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15081</th>\n",
" <td>methylation_SNORA63</td>\n",
" <td>0.309627</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15320</th>\n",
" <td>methylation_CCHCR1</td>\n",
" <td>0.305029</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15478</th>\n",
" <td>methylation_NFKBID</td>\n",
" <td>0.301979</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15693</th>\n",
" <td>methylation_INTS4L2</td>\n",
" <td>0.297477</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15767</th>\n",
" <td>methylation_BOLA2B</td>\n",
" <td>0.295996</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15768</th>\n",
" <td>methylation_BOLA2</td>\n",
" <td>0.295995</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15805</th>\n",
" <td>methylation_SNORA65</td>\n",
" <td>0.295021</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15815</th>\n",
" <td>methylation_SHBG</td>\n",
" <td>0.294755</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15828</th>\n",
" <td>methylation_SNHG7</td>\n",
" <td>0.294544</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15936</th>\n",
" <td>methylation_SULT1A3</td>\n",
" <td>0.292160</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15937</th>\n",
" <td>methylation_SULT1A4</td>\n",
" <td>0.292160</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16015</th>\n",
" <td>methylation_KAZALD1</td>\n",
" <td>0.290273</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16085</th>\n",
" <td>methylation_LANCL2</td>\n",
" <td>0.288802</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16131</th>\n",
" <td>methylation_SNORD22</td>\n",
" <td>0.287737</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16216</th>\n",
" <td>methylation_SNORD36A</td>\n",
" <td>0.285684</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"13587 methylation_FOXD4L3 0.333646 methylation\n",
"13969 methylation_UCK1 0.327589 methylation\n",
"14849 methylation_SNORA81 0.313605 methylation\n",
"14850 methylation_MIR1248 0.313605 methylation\n",
"15076 methylation_ADHFE1 0.309721 methylation\n",
"15081 methylation_SNORA63 0.309627 methylation\n",
"15320 methylation_CCHCR1 0.305029 methylation\n",
"15478 methylation_NFKBID 0.301979 methylation\n",
"15693 methylation_INTS4L2 0.297477 methylation\n",
"15767 methylation_BOLA2B 0.295996 methylation\n",
"15768 methylation_BOLA2 0.295995 methylation\n",
"15805 methylation_SNORA65 0.295021 methylation\n",
"15815 methylation_SHBG 0.294755 methylation\n",
"15828 methylation_SNHG7 0.294544 methylation\n",
"15936 methylation_SULT1A3 0.292160 methylation\n",
"15937 methylation_SULT1A4 0.292160 methylation\n",
"16015 methylation_KAZALD1 0.290273 methylation\n",
"16085 methylation_LANCL2 0.288802 methylation\n",
"16131 methylation_SNORD22 0.287737 methylation\n",
"16216 methylation_SNORD36A 0.285684 methylation"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"methylation\")\n",
"].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr>\n",
" <th>13587</th>\n",
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" <th>13969</th>\n",
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" <tr>\n",
" <th>14849</th>\n",
" <td>methylation_SNORA81</td>\n",
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" <tr>\n",
" <th>14850</th>\n",
" <td>methylation_MIR1248</td>\n",
" <td>0.313605</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15076</th>\n",
" <td>methylation_ADHFE1</td>\n",
" <td>0.309721</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>15081</th>\n",
" <td>methylation_SNORA63</td>\n",
" <td>0.309627</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15320</th>\n",
" <td>methylation_CCHCR1</td>\n",
" <td>0.305029</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15478</th>\n",
" <td>methylation_NFKBID</td>\n",
" <td>0.301979</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15693</th>\n",
" <td>methylation_INTS4L2</td>\n",
" <td>0.297477</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15767</th>\n",
" <td>methylation_BOLA2B</td>\n",
" <td>0.295996</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15768</th>\n",
" <td>methylation_BOLA2</td>\n",
" <td>0.295995</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15805</th>\n",
" <td>methylation_SNORA65</td>\n",
" <td>0.295021</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15815</th>\n",
" <td>methylation_SHBG</td>\n",
" <td>0.294755</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15828</th>\n",
" <td>methylation_SNHG7</td>\n",
" <td>0.294544</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15936</th>\n",
" <td>methylation_SULT1A3</td>\n",
" <td>0.292160</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15937</th>\n",
" <td>methylation_SULT1A4</td>\n",
" <td>0.292160</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16015</th>\n",
" <td>methylation_KAZALD1</td>\n",
" <td>0.290273</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16085</th>\n",
" <td>methylation_LANCL2</td>\n",
" <td>0.288802</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16131</th>\n",
" <td>methylation_SNORD22</td>\n",
" <td>0.287737</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16216</th>\n",
" <td>methylation_SNORD36A</td>\n",
" <td>0.285684</td>\n",
" <td>methylation</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"13587 methylation_FOXD4L3 0.333646 methylation\n",
"13969 methylation_UCK1 0.327589 methylation\n",
"14849 methylation_SNORA81 0.313605 methylation\n",
"14850 methylation_MIR1248 0.313605 methylation\n",
"15076 methylation_ADHFE1 0.309721 methylation\n",
"15081 methylation_SNORA63 0.309627 methylation\n",
"15320 methylation_CCHCR1 0.305029 methylation\n",
"15478 methylation_NFKBID 0.301979 methylation\n",
"15693 methylation_INTS4L2 0.297477 methylation\n",
"15767 methylation_BOLA2B 0.295996 methylation\n",
"15768 methylation_BOLA2 0.295995 methylation\n",
"15805 methylation_SNORA65 0.295021 methylation\n",
"15815 methylation_SHBG 0.294755 methylation\n",
"15828 methylation_SNHG7 0.294544 methylation\n",
"15936 methylation_SULT1A3 0.292160 methylation\n",
"15937 methylation_SULT1A4 0.292160 methylation\n",
"16015 methylation_KAZALD1 0.290273 methylation\n",
"16085 methylation_LANCL2 0.288802 methylation\n",
"16131 methylation_SNORD22 0.287737 methylation\n",
"16216 methylation_SNORD36A 0.285684 methylation"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.startswith(\"methylation\")\n",
"].head(20)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_df[\"omic_layer\"] = global_feature_importance_df[\n",
" \"feature\"\n",
"].map(lambda x: x.split(\"_\")[0])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"df_sorted = global_feature_importance_df.sort_values(\n",
" [\"omic_layer\", \"importance\"], ascending=[True, False]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>day4_day1_ratio</td>\n",
" <td>1.181447</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>mut_TP53</td>\n",
" <td>1.140086</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>tissue_Haematopoietic and Lymphoid</td>\n",
" <td>1.126392</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>tissue_Lung</td>\n",
" <td>0.821399</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>doubling_time_hours</td>\n",
" <td>0.327485</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"3 day4_day1_ratio 1.181447 conditionals\n",
"5 mut_TP53 1.140086 conditionals\n",
"6 tissue_Haematopoietic and Lymphoid 1.126392 conditionals\n",
"14 tissue_Lung 0.821399 conditionals\n",
"800 doubling_time_hours 0.327485 conditionals"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sorted['feature'] = df_sorted['feature'].map(lambda x: \"_\".join(x.split(\"_\")[1:]))\n",
"df_sorted.head()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"df_sorted_no_tag = df_sorted[~df_sorted[\"feature\"].str.contains(\" TAG\")]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"df_top10_no_tag = df_sorted_no_tag.groupby(\"omic_layer\").head(10)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"df_top5 = df_sorted.groupby('omic_layer').head(5).reset_index(drop=True)\n",
"df_top2 = df_sorted.groupby('omic_layer').head(2).reset_index(drop=True)\n",
"df_top1 = df_sorted.groupby('omic_layer').head(1).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"# Calculate the mean importance for each omic_layer\n",
"mean_importance = df_top2.groupby(\"omic_layer\")[\"importance\"].mean().reset_index()\n",
"\n",
"# Sort omic_layer by mean importance\n",
"mean_importance_sorted = mean_importance.sort_values(\"importance\", ascending=False)\n",
"\n",
"# Reorder DataFrame based on the sorted omic_layers\n",
"df_top2[\"omic_layer\"] = pd.Categorical(\n",
" df_top2[\"omic_layer\"], categories=mean_importance_sorted[\"omic_layer\"], ordered=True\n",
")\n",
"df_top2 = df_top2.sort_values(\n",
" [\"omic_layer\", \"importance\"], ascending=[True, False]\n",
").reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(2,2))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"sns.scatterplot(\n",
" data=df_top1,\n",
" y=\"feature\",\n",
" x=\"importance\",\n",
" hue=\"omic_layer\",\n",
" style=\"omic_layer\",\n",
" s=30,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top1.iterrows():\n",
" plt.plot([0, row[\"importance\"]], [row[\"feature\"], row[\"feature\"]], \"grey\", lw=0.5)\n",
"\n",
"plt.ylabel(\"\")\n",
"plt.xlabel(\"\")\n",
"plt.legend(title=\"Omic Layer\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(2,2))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"sns.scatterplot(\n",
" data=df_top2,\n",
" y=\"feature\",\n",
" x=\"importance\",\n",
" hue=\"omic_layer\",\n",
" style=\"omic_layer\",\n",
" s=30,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top2.iterrows():\n",
" plt.plot([0, row[\"importance\"]], [row[\"feature\"], row[\"feature\"]], \"grey\", lw=0.5)\n",
"\n",
"plt.ylabel(\"\")\n",
"plt.xlabel(\"\")\n",
"plt.yticks([],fontsize=8)\n",
"plt.legend(title=\"Omic Layer\")\n",
"# plt.show()\n",
"plt.savefig(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top2.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"# Calculate the mean importance for each omic_layer\n",
"mean_importance = df_top5.groupby(\"omic_layer\")[\"importance\"].mean().reset_index()\n",
"\n",
"# Sort omic_layer by mean importance\n",
"mean_importance_sorted = mean_importance.sort_values(\"importance\", ascending=False)\n",
"\n",
"# Reorder DataFrame based on the sorted omic_layers\n",
"df_top5[\"omic_layer\"] = pd.Categorical(\n",
" df_top5[\"omic_layer\"], categories=mean_importance_sorted[\"omic_layer\"], ordered=True\n",
")\n",
"df_top5 = df_top5.sort_values(\n",
" [\"omic_layer\", \"importance\"], ascending=[True, False]\n",
").reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['copynumber',\n",
" 'conditionals',\n",
" 'drugresponse',\n",
" 'metabolomics',\n",
" 'crisprcas9',\n",
" 'proteomics',\n",
" 'transcriptomics',\n",
" 'methylation']"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(mean_importance_sorted[\"omic_layer\"])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"df_top10_no_tag.to_csv(\n",
" f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top10_no_tag.csv\",\n",
" index=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"df_top5.to_csv(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_top5 = pd.read_csv(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"df_top5_part1 = df_top5[\n",
" df_top5[\"omic_layer\"]\n",
" .isin([\"conditionals\", \"drugresponse\", \"copynumber\", \"metabolomics\"])\n",
" .reset_index(drop=True)\n",
"]\n",
"df_top5_part2 = df_top5[\n",
" ~df_top5[\"omic_layer\"]\n",
" .isin([\"conditionals\", \"drugresponse\", \"copynumber\", \"metabolomics\"])\n",
" .reset_index(drop=True)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(4.5, 1.5))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"g = sns.scatterplot(\n",
" data=df_top5_part1,\n",
" x=\"feature\",\n",
" y=\"importance\",\n",
" hue=\"omic_layer\",\n",
" palette=OMIC_PALLETS,\n",
" style=\"omic_layer\",\n",
" s=20,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top5_part1.iterrows():\n",
" plt.plot([row[\"feature\"], row[\"feature\"]], [0, row[\"importance\"]], \"grey\", lw=0.5)\n",
"\n",
"plt.xticks(rotation=45, ha=\"right\")\n",
"\n",
"plt.xlabel(\"Feature\")\n",
"plt.ylabel(\"Importance\")\n",
"plt.legend(title=\"Omic Layer\").remove()\n",
"# plt.show()\n",
"# plt.tight_layout()\n",
"plt.savefig(\n",
" f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5_part1_h.pdf\",\n",
" bbox_inches=\"tight\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 300x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(1.5, 3))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"g = sns.scatterplot(\n",
" data=df_top5_part1,\n",
" y=\"feature\",\n",
" x=\"importance\",\n",
" hue=\"omic_layer\",\n",
" palette=OMIC_PALLETS,\n",
" style=\"omic_layer\",\n",
" s=20,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top5_part1.iterrows():\n",
" plt.plot([0, row[\"importance\"]], [row[\"feature\"], row[\"feature\"]], \"grey\", lw=0.5)\n",
"\n",
"# plt.title(\"Top 5 Features from Each Omic Layer by Importance\")\n",
"plt.ylabel(\"Feature\")\n",
"plt.xlabel(\"Importance\")\n",
"plt.legend(title=\"Omic Layer\")\n",
"# plt.show()\n",
"# plt.tight_layout()\n",
"plt.savefig(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5_part1.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(4.5, 1.5))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"g = sns.scatterplot(\n",
" data=df_top5_part2,\n",
" x=\"feature\",\n",
" y=\"importance\",\n",
" hue=\"omic_layer\",\n",
" palette=OMIC_PALLETS,\n",
" style=\"omic_layer\",\n",
" s=20,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top5_part2.iterrows():\n",
" plt.plot([row[\"feature\"], row[\"feature\"]], [0, row[\"importance\"]], \"grey\", lw=0.5)\n",
"\n",
"plt.xticks(rotation=45, ha=\"right\")\n",
"\n",
"plt.xlabel(\"Feature\")\n",
"plt.ylabel(\"Importance\")\n",
"plt.legend(title=\"Omic Layer\").remove()\n",
"# plt.show()\n",
"# plt.tight_layout()\n",
"plt.savefig(\n",
" f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5_part2_h.pdf\",\n",
" bbox_inches=\"tight\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 300x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(1.5, 3))\n",
"\n",
"# Create a categorical scatter plot and set marker style and size\n",
"sns.scatterplot(\n",
" data=df_top5_part2,\n",
" y=\"feature\",\n",
" x=\"importance\",\n",
" hue=\"omic_layer\",\n",
" palette=OMIC_PALLETS,\n",
" style=\"omic_layer\",\n",
" s=20,\n",
")\n",
"\n",
"# Draw lines connecting each point to the y-axis\n",
"for _, row in df_top5_part2.iterrows():\n",
" plt.plot([0, row[\"importance\"]], [row[\"feature\"], row[\"feature\"]], \"grey\", lw=0.5)\n",
"\n",
"# plt.title(\"Top 5 Features from Each Omic Layer by Importance\")\n",
"plt.ylabel(\"Feature\")\n",
"plt.xlabel(\"Importance\")\n",
"plt.legend(title=\"Omic Layer\").remove()\n",
"plt.savefig(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_top5_part2.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>importance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>omic_layer</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>metabolomics</th>\n",
" <td>0.638349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>copynumber</th>\n",
" <td>0.483749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>drugresponse</th>\n",
" <td>0.477286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>crisprcas9</th>\n",
" <td>0.413631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>transcriptomics</th>\n",
" <td>0.356984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>proteomics</th>\n",
" <td>0.316226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>conditionals</th>\n",
" <td>0.175110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>methylation</th>\n",
" <td>0.156285</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance\n",
"omic_layer \n",
"metabolomics 0.638349\n",
"copynumber 0.483749\n",
"drugresponse 0.477286\n",
"crisprcas9 0.413631\n",
"transcriptomics 0.356984\n",
"proteomics 0.316226\n",
"conditionals 0.175110\n",
"methylation 0.156285"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df.groupby(\"omic_layer\")[[\"importance\"]].mean().sort_values(\n",
" \"importance\", ascending=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_omic_summary_df = pd.merge(\n",
" global_feature_importance_df.groupby(\"omic_layer\")[[\"importance\"]]\n",
" .mean()\n",
" .sort_values(\"importance\", ascending=False)\n",
" .reset_index(),\n",
" global_feature_importance_df.groupby(\"omic_layer\")\n",
" .size()\n",
" .reset_index(name=\"feature_count\"),\n",
" on=\"omic_layer\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>omic_layer</th>\n",
" <th>importance</th>\n",
" <th>feature_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>metabolomics</td>\n",
" <td>0.394204</td>\n",
" <td>225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>copynumber</td>\n",
" <td>0.343448</td>\n",
" <td>777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>drugresponse</td>\n",
" <td>0.285795</td>\n",
" <td>810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>crisprcas9</td>\n",
" <td>0.212119</td>\n",
" <td>5213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>transcriptomics</td>\n",
" <td>0.143757</td>\n",
" <td>8078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>proteomics</td>\n",
" <td>0.126493</td>\n",
" <td>4922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>methylation</td>\n",
" <td>0.086297</td>\n",
" <td>7590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>conditionals</td>\n",
" <td>0.059727</td>\n",
" <td>237</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" omic_layer importance feature_count\n",
"0 metabolomics 0.394204 225\n",
"1 copynumber 0.343448 777\n",
"2 drugresponse 0.285795 810\n",
"3 crisprcas9 0.212119 5213\n",
"4 transcriptomics 0.143757 8078\n",
"5 proteomics 0.126493 4922\n",
"6 methylation 0.086297 7590\n",
"7 conditionals 0.059727 237"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_omic_summary_df"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"global_feature_importance_df.to_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_latent_shap_values_df_sum_global.csv\", index=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>metabolomics_1-methylnicotinamide</td>\n",
" <td>0.573762</td>\n",
" <td>metabolomics</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"101 metabolomics_1-methylnicotinamide 0.573762 metabolomics"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.contains(\"metabolomics_1-\")\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>importance</th>\n",
" <th>omic_layer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>copynumber_SMAD4</td>\n",
" <td>0.425694</td>\n",
" <td>copynumber</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4742</th>\n",
" <td>crisprcas9_SMAD4</td>\n",
" <td>0.211849</td>\n",
" <td>crisprcas9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18367</th>\n",
" <td>transcriptomics_SMAD4</td>\n",
" <td>0.106632</td>\n",
" <td>transcriptomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18893</th>\n",
" <td>proteomics_SMAD4</td>\n",
" <td>0.103112</td>\n",
" <td>proteomics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24844</th>\n",
" <td>conditionals_mut_SMAD4</td>\n",
" <td>0.066022</td>\n",
" <td>conditionals</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature importance omic_layer\n",
"389 copynumber_SMAD4 0.425694 copynumber\n",
"4742 crisprcas9_SMAD4 0.211849 crisprcas9\n",
"18367 transcriptomics_SMAD4 0.106632 transcriptomics\n",
"18893 proteomics_SMAD4 0.103112 proteomics\n",
"24844 conditionals_mut_SMAD4 0.066022 conditionals"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df[\n",
" global_feature_importance_df[\"feature\"].str.contains(\"SMAD4\")\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['metabolomics',\n",
" 'copynumber',\n",
" 'drugresponse',\n",
" 'crisprcas9',\n",
" 'transcriptomics',\n",
" 'proteomics',\n",
" 'methylation',\n",
" 'conditionals']"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_omic_summary_df[\"omic_layer\"].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>importance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>omic_layer</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>conditionals</th>\n",
" <td>41.501134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>copynumber</th>\n",
" <td>375.873147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>crisprcas9</th>\n",
" <td>2156.257968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>drugresponse</th>\n",
" <td>386.601644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>metabolomics</th>\n",
" <td>143.628547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>methylation</th>\n",
" <td>1186.206485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>proteomics</th>\n",
" <td>1556.466352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>transcriptomics</th>\n",
" <td>2883.715463</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance\n",
"omic_layer \n",
"conditionals 41.501134\n",
"copynumber 375.873147\n",
"crisprcas9 2156.257968\n",
"drugresponse 386.601644\n",
"metabolomics 143.628547\n",
"methylation 1186.206485\n",
"proteomics 1556.466352\n",
"transcriptomics 2883.715463"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_feature_importance_df.groupby(\"omic_layer\")[[\"importance\"]].sum()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='importance', ylabel='omic_layer'>"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dnR0A9+/fJygoiDNnzlClSpU0nVuDBg3o168fAOXLl2fjxo0UKFDAGGTq1avH5s2bOXbsGM2bNzfuV6dOHcaNG2c8xr1791i8eDFdu3bF2tqa2bNn0759e4YNGwZA/fr1cXBwYNiwYXz88cdUqFDBeC6jRo2idOnSaapbRORVlZohsMTERM6ePZum4547d+55S5JnUEB6ghMnThAXF0eTJk1Mlg8ZMoQ//viDFi1aEBgYaLKuVKlSuLm5cejQIZPl7733nvG1lZUVTZo0ISQkhNjYWOzt7WnatCnr1683BqS1a9fy7rvvkjt3buN+5cuXN4YjgCJFigA818Q/Nzc34+tChQoBUK1aNZMa8+XLR0xMjMl+LVq0MHnftGlTFi1axIkTJ7CysiI2NhYvLy+TITMvLy/g8XBkUkDKmTMnpUqVSnPdGzZsYMOGDWneT0QksyTdwZbaILN3797naic6Ovq59pMnU0B6gjt37gDg6Oj4xHUFCxZMtq5gwYKcPn3aZFlSmElSoEABDAYDMTEx5MqVC19fX9avX8+RI0ewtbXl/PnzjBo1ymSfXLlymby3tn48OpqYmJim8wJMgtaTjp8S8/NN6pt/32Lao0ePFPeNiIgwvi5QoABWVlapqvXfvL298fb2Trbc398/zccSEckINjY2AFSoUCHF773/lidPHjp27Jim4587d46pU6eSN2/e565RUqaA9ARJ/9giIyMpW7ascfn169f5/fffAbh161ay/f7++2/y589vsiwqKsokJN26dYts2bLh4OAAgLu7O6VKlWLLli3Y2NhQunRpatasmaZ6kwJHQkIC2bJlAx4Pw6Un899Qks6/QIECxt+SJk+eTJkyZZLtm1KYFBF5XdjZ2WFvb//Ubezt7XFycsqgiuRZNEn7CVxcXLCxsTFOpk6yaNEipk+fTqFChfj+++9N1l25coUTJ05QvXp1k+U7duwwvjYYDPzwww/UqFEDW1tb4HG48fHx4ccff+THH3+kTZs2aa436TeTf99Fd+zYsTQf52n27Nlj8n7jxo3kypWLatWqUa1aNWxsbLh58yZVq1Y1/rGxsWHKlClcvXo1XWsRERGxJF1BegJHR0c6d+7MokWLsLW1pXbt2pw6dYolS5YQGBiIg4MDQ4cOpX///rz//vtERUURGhpKvnz5+Pjjj02ONWnSJB49esSbb77JypUr+eOPP1i0aJHJNj4+PoSEhGAwGHj//ffTXK+npyfjxo3jyy+/pHv37ty4cYPQ0FDy5MnzIt1g4ocffqBIkSLUrVuXvXv3smLFCvr27WsMZ926dSM4OJh79+7h4eHBzZs3CQ4OxsrKiooVK6ZbHSIiIpamgPQUgwYNomDBgixfvpwFCxZQokQJPv/8cz788EPg8XjxnDlz6NWrF3Z2djRo0IDAwEDjxOckI0eOZM6cOVy5coVKlSqxYMGCZENoRYoUoWLFiuTPn59ixYqludY333yTCRMmMHv2bHr06EG5cuUYPXo0o0ePfv4OMDNkyBC2bNnC119/TaFChRg6dChdunQxru/Xrx+FChVi2bJlzJs3j3z58lGnTh0CAwOfeWlZRCQrSnqMiaUeZ2JnZ4e1tfUz5zdJ2lkZzJ8GKOlm9erVDB06lO3bt1OiRImnbnvz5k28vLyYOnUqzZo1y6AKswZ/f3+cnJyS3XUnIvIyiIuLM07Wfhp7e/tkd0enxt27d8mXL9/zlJal+Pv7ExYWlm7H0xWkTHbmzBm2b9/O1q1bKVGiBO+8805mlyQiIukoNeHoRSgcWYYmaWeyhw8fsnDhQhISEpg+fbrxDjQRERHJPLqCZEE+Pj74+Pg8dRtXV1eOHj2aQRWJiIhIaugKkoiIiIgZBSQRERERMwpIIiIiImYUkERERETMKCCJiIiImFFAEhERETGjgCQiIiJiRs9BkixBn/UmIq86fR97uSggSZbQvXv3zC5BRESyEA2xiYiIiJhRQBIRERExo4AkIiIiYkYBSURERMSMApKIiIiIGQUkERERETMKSCIiIiJmFJBEREREzCggiYiIiJhRQBIRERExo4AkIiIiYkafxSZZQnh4ODExMZldhohkIHt7e30Oo1iMApJkCTExMQpIIiKSbjTEJiIiImJGAUlERETEjAKSiIiIiBkFJBEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjALS/7d69WqcnZ25evVqZpciIiL/X1xcXLoc5+7du+lyHHl9KCCJiMhLKTY2lgMHDhAbG/tCx7l27Rq+vr5cu3YtnSqT14ECkoiIvJTi4+NN/n5e9+7dIzExkXv37qVHWfKaeC0DUmJiIrNmzaJRo0ZUq1aNgIAAk8uvISEhNGnShNDQUDw8PHjnnXeIiorCy8uLIUOGmBwrpaG5nTt34uPjg4uLC82aNWPDhg00adKEkJAQAA4dOoSzszP//e9/ady4MXXr1mXv3r0AHDlyhE6dOlGtWjXc3d0JCgoiMjLSpPbg4GC8vLyoUqUKXl5eTJ061XgZ+urVqzg7O7Nx40b8/f2pVq0anp6ehISEkJiYaDxOQkICS5cupVWrVri4uNCoUSMmT57Mw4cPjdsMGTKEjz76iFWrVtGsWTOqVKlC69at2bVrV6rrAXj48CETJ07E09OTKlWq0KpVKzZt2vRCX0MRERFLyp7ZBWSGSZMmsXjxYvz9/XF1dWXLli1MmTLFZJtr166xbds2pk6dSlRUFPnz50/VsQ8ePEhAQACNGzemb9++XL58mREjRpgEjyTTpk1j1KhRPHz4EFdXV37++Wc+/vhjateuzfTp07l79y7BwcF07tyZ7777jpw5cxIeHs7SpUsJCgqiZMmS/PLLL0ybNg0bGxs+++wz47FHjhxpDEZHjx5l1qxZ3L9/3xjwhg8fztq1a+nWrRvu7u6cPn2amTNncubMGebNm4eVlRUAv/76KxEREfTp0wc7OzuCg4Pp06cPu3fvJl++fM+sx2Aw0KtXL44dO0afPn0oV64c27Zto3///jx69Ij333//Ob+KIiIilvPaBaTo6Gi++eYbOnfubAwUDRo04ObNm+zZs8e4XXx8PEFBQdStWzdNxw8JCaF8+fKEhoYaQ0aBAgUIDAxMtm2HDh149913je+nTJnCm2++yZw5c8iWLRsA1apVo2XLlqxatYqOHTty+PBhKleuTNu2bQFwd3cnV65c2NnZmRy7UqVKTJ48GYCGDRvy4MEDlixZQkBAABEREXz33Xf069ePTz/9FIB69epRuHBhBg8ezO7du/H09AQgJiaG1atXU6pUKQBy585Np06dOHjwIM2aNXtmPfv372fPnj1MmzaNFi1aGPs7NjaWyZMn4+3tTfbsr90/QxFJgycNjSUmJnL27Nln7n/u3Ln0LkleA6/dT6YTJ04QFxfH22+/bbK8efPmJgEJwMnJKU3HfvToEcePH6dXr17GcATQrFmzFEOAs7Oz8XVsbCy//PILn3zyCQaDwTjmXrJkScqVK8e+ffvo2LEjHh4eTJkyhQ8//JAmTZrQsGFDOnXqlOzYrVu3NnnfrFkzFi9ezIkTJ4zDga1atTLZpmXLlgwdOpRDhw4ZA5Kjo6MxHAEULVrUWC/wzHoOHDiAlZUVnp6eJvMIvLy8WL9+PefOneOtt956WrcabdiwgQ0bNqRqWxF59SUN1T8t4CRNT0iN6OjoF65JXh+vXUBKmmvk6OhosrxQoULJti1YsGCajn3nzh0SEhIoUKCAyfLs2bOnOET37+2io6NJTEwkPDyc8PDwZNvmyJEDgG7dupEnTx5WrVrFhAkTGD9+PE5OTnz++efUqVPHuH3hwoVN9k863+joaGMfmJ9zUp0xMTHGZbly5TLZJin4Jc1nelY9d+7cwWAwUL169ZS6jIiIiFQHJG9vb7y9vZMt9/f3T9X+IvJqsbGxAaBChQrJrpID5MmTh44dOz7zOOfOnWPq1KnkzZs33WuUrOu1C0hJQeX27duULVvWuPzOnTup2j8hIcHk/YMHD4yvCxQogI2NDbdv3zbZJjExkaioqKceN0+ePFhZWfHRRx/RsmXLZOuTgoq1tTUdO3akY8eO3L59m127dhEWFsZnn33G/v37n3g+STUVKFDA+FvU33//TYkSJYzbxMXFpWm+VWrqsbe3J3fu3CxevDjF/UuXLp3qtkTk9WRnZ4e9vX2y5fb29mm+0i+SWq/dXWxubm7kzJmTLVu2mCz/6aefnrmvnZ0dN27cMFl27Ngx4+ts2bJRvXp1fvzxR5NtduzY8czbVO3s7KhUqRIXLlygatWqxj8VKlQgNDSUQ4cOAY/nLX311VfA47Dj4+NDx44diYmJMRmn37Fjh8nxt27dSq5cuYx3xwF8//33Jtts3LiRhIQEatSo8cy+SPKsetzd3Xnw4AEGg8HkvM6dO8fMmTNf+PZdERERS3jtriDlyZOHgIAApk+fTq5cuahduza7du1KVUBq3Lgxc+bMISwsDFdXV3bu3MmBAwdMtunTpw9+fn706dPH+GCy4OBgAJN5SSkJDAykR48eDBgwgNatW5OQkMCCBQv45ZdfjJOpa9WqxYIFCyhYsCBubm7cvHmThQsX4u7ujqOjo/GK1pYtWyhYsCCenp4cPnyYpUuX0r9/f3Lnzk358uVp06YNoaGh/PPPP3h4eHDmzBnjYw0aNGiQ6v58Vj2enp7UqlWLgIAAAgICKFeuHCdPniQkJIT69esnG+oUERF5Gbx2AQmgZ8+e5M6dm0WLFrFo0SLc3NwICgpi5MiRz9wvMjKSBQsWEBcXR6NGjRgzZowxvADUrFmTkJAQgoODCQgIoHjx4nz55Zf079+fPHnyPPX49evXZ/78+YSGhtKnTx9sbGyoXLkyCxcuxNXVFYC+fftia2vLqlWrmDlzJvb29nh5eTFgwACTY/Xt25fDhw+zYsUKihUrxvDhw/nggw+M68eMGUPp0qVZtWoV8+fPp3Dhwvj5+dGrVy+srVN/YfFZ9VhbWzN37lyCg4OZM2cOt2/fpkiRInz00Uf06tUr1e2IyOsn6eaWF73T1c7ODmtr6xTnMYk8iZXBYDBkdhFZyfbt2ylatCiVK1c2Ljt37hze3t7MmjUr2d1z6e3q1au8/fbbjBs3Dh8fH4u29bLw9/fHycnJZHK5iGQNcXFxxsna5uzt7VN8hEpK7t69S758+dKzNHnJ+Pv7ExYWlm7Hey2vIFnS3r172bRpEwMHDuTNN9/kxo0bzJ49m7Jly1K/fv3MLk9E5JXypHCUVgpHklYKSOksKCiInDlzMnv2bCIiInBwcKBBgwYMGDDAeKu+iIiIvNwUkNJZzpw5CQoKIigoKFPaL1GiBL///numtC0iIpJVvHa3+YuIiIg8iwKSiIiIiBkFJBEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaPnIEmWYG9vn9kliEgG0/97sSQFJMkSunfvntkliIhIFqIhNhEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjAKSiIiIiBmLB6Rp06Zx9OhRSzcjIiIikm4sHpAWL16sgCQiIiKvFIsHpNy5c5MtWzZLNyMiIiKSbiz+WWyBgYFMnDiRPHny0LhxYwoVKoS1taY+SfoKDw8nJiYms8sQsTh7e3t99qBIBrB4QPr666959OgRo0aNYtSoUSluY2VlxenTpy1dimRhMTExCkgiIpJuLB6QHBwccHBwsHQzIiIiIunG4gHpm2++sXQTIiIiIukqQycDxcbGcvHiRWJjY0lMTMzIpkVERERSLUMC0pUrV+jZsye1atWiRYsWnDhxgsOHD9OqVSs9AkBEREReOhYPSNevX+c///kPBw4coHr16sbliYmJXLx4kW7duvHbb79ZugwRERGRVLN4QJoxYwYPHz5kzZo1TJ8+HYPBAEDdunX57rvvsLW1Zfbs2ZYuQ0RERCTVLB6Q9uzZwwcffEC5cuWwsrIyWVexYkU6dOjAiRMnLF2GiIiISKpZPCDduXOH0qVLP3H9G2+8QVRUlKXLEBEREUk1iwekokWLcv78+SeuP3HiBIULF7Z0GSIiIiKpZvGA1KRJE1auXMnJkyeNy5KG2jZt2sT69etp3LixpcsQERERSTWLB6SAgACKFi3Khx9+yEcffYSVlRWhoaG0atWKAQMGULhwYT799FNLl/HKcHZ2JiQkJLPLSFd//PEH/v7+uLm54e7uTu/evblw4UJmlyXySrt79y53797N7DJEsiyLByR7e3tWrFhBu3btuHHjBgaDgSNHjvDXX3/RqlUrVqxYgaOjo6XLeGUk9VVWceXKFT744APOnTvH8OHDmTx5MtmyZaNDhw5cuXIls8sTeSVdu3YNHx8ffHx8uHbtWmaXI5IlWfyjRgDy5s3LiBEjGDFiBJGRkSQmJuLo6Ii1dYY+yPuV4OrqmtklpKtFixYRGxvLqlWrKFmyJAANGjSgffv2TJ8+nSlTpmRyhSKvnnv37qX4WkTSj8UTyieffMKmTZt49OgRAI6OjhQsWPC1CEcGg4GlS5fSsmVLXFxcaNKkCeHh4RgMBoYMGUKXLl0YMWIENWvWpE2bNsTHxycbYvvmm2949913qVq1Kg0aNGDkyJEm3xCdnZ1ZsmQJQUFBuLm5UbduXb766iv++ecf4zZ+fn4MHDiQPn36UL16dXr06AHA/fv3GTduHA0bNsTV1RUfHx927Nhh3O+ff/5hypQpNG3alCpVqlC9enU+/vhjzpw5Y9wmMjKSgQMHUq9ePapWrcp7773H2rVrjesvXLhAhQoVjOEIHs9Bq1GjBrt27UrX/hYREUkvFr+CdPLkSfbv34+dnR0tW7bEx8cHFxcXSzf7Upg6dSrz58/no48+ol69evzv//4v06ZNM4bFI0eOYGVlRUhICPfv3yd7dtMvx8aNG5kwYQJBQUE4Oztz4cIFJkyYwD///MP48eON2wUHB1OtWjWmT5/OH3/8wfTp07l586ZJ0Nq8eTPvvvsuM2fOJCEhgcTERLp168Yff/xBnz59KFeuHOvWraN3794sXLgQDw8PBg8ezM8//8yAAQMoVaoUly5dIjg4mP79+7N582asrKwYNGgQt2/fZtSoUeTJk4f169cTFBREsWLF8PDwIH/+/Jw9e5a4uDhsbGyM9Vy5coWYmBju3LmDg4ODZb8QIiIiaWTxgLRv3z5+/PFH1q5dy8qVK1mxYgVvvvkmbdu2pXXr1hQqVMjSJWSK6OhoFi5ciJ+fH4MHDwagXr16REZGcvToUQoXLkx8fDyjRo164nOiDh06RPHixenYsSPW1ta4u7uTO3fuZM+NcnR0JCwsjOzZs+Pp6Ym1tTXjxo3j3LlzVKhQAQBra2tGjx5N7ty5Adi5cyfHjh1j1qxZvP322wDUrl2by5cvc/DgQdzc3Lh//z5ffvklLVq0AMDd3Z379+8zfvx4/v77bwoXLszhw4cJCAjgnXfeAcDDwwMHBweyZcsGgI+PDxs2bCAoKIj+/ftjZ2fHunXr2LNnD/D4A4wVkERE5GVj8YBka2tLixYtaNGiBbdu3WL9+vWsWbOGSZMmMW3aNOrVq0fbtm1p3LixyRWGV92JEyeIi4ujSZMmJsuHDBli/DtnzpyUKlXqiceoXbs2K1aswMfHh6ZNm9KoUSNatWqV7InkLVu2NLn61KxZM8aNG8eRI0eMAalEiRLGcASPr17Z2NiYPGLBysqK5cuXG9/Pnz8fgIiICC5fvsyFCxf46aefAIiLiwMeB6KQkBB+++03PD09adiwIUFBQcZj1KtXj0mTJjF27Fg2btwIQJ06dejZsyfBwcHkypXrWV1ptGHDBjZs2JDq7UWyqujo6BRfi0j6yZBJ2kkKFixI165d6dq1K5cuXSIsLIy1a9eye/du8ufPT9u2bencuXOWuKp0584dgKfeoVegQIFkYeffWrRoQWJiIsuWLSM0NJTg4GCKFy/OgAEDaNmypXE78wdtFihQADD9xlmwYMFk9Tk4ODx1LtiePXsYO3YsFy5cIE+ePDg7O5MnTx4A42fqTZs2jbCwMDZv3syWLVuwtrambt26jBw50jjvqHXr1nh7e/Pnn3+SK1cuihQpwowZM7C2tsbe3v6J7Zvz9vbG29s72XJ/f/9UH0MkK8ibN2+Kr0Uk/WT4TOmLFy8yffp0Pv30U9auXUv27Nlp0qQJNWvW5Ouvv6ZFixb8/PPPGV1Wukv6phUZGWmy/Pr16xw8eNB4BeZZvL29WbZsGYcOHWL69Ok4ODgwaNAgbt68adwmKYwluXXrFvD0cGZvb8+dO3dITEw0WX7mzBlOnTrFn3/+Sa9evahYsSLbtm3j2LFjLF++PNlDPe3t7Rk0aBA7duxg8+bNBAYGcuzYMUaNGgU8fgbS2rVrsba2pkyZMhQpUgSA//3f/6VixYrGoTgREZGXSYYEpMjISL755ht8fX1p0aKFcb7MkCFD2L17NzNmzGDGjBls2rQJW1tbRo4cmRFlWZSLiws2NjZs377dZPmiRYvo27fvU68cJenXrx+9e/cGHgeR5s2bExAQQEJCAhEREcbt/n3nGcDWrVuxsrKidu3aTzx2zZo1iYuLM7mTzGAw8MUXXzB79mx+/fVXHj58SM+ePU2GAZPmDhkMBv766y88PT3ZsmULAGXLlqV79+7UrVuXGzduAHDu3DmCgoL4448/jMc4f/48e/fuNc5bEhERedlYfIjN39+fvXv3Eh8fj729Pf/5z39o27ZtineylSxZEnd3d3bv3m3psizO0dGRzp07s2jRImxtbalduzanTp1iyZIlBAYGcvbs2Wceo3bt2owYMYIJEybQsGFDoqOjCQ0NpUyZMlSsWNG43cmTJxk4cCDvvfcev//+OzNmzOA///mPya315ho1aoSbmxtDhw6lb9++lC5dmu+//56zZ8/y5Zdf4ujoSPbs2Zk0aRJdu3bl0aNHrF69mp07dwLw4MEDnJ2dKVq0KF999RX37t2jVKlS/Prrr+zatYuePXsC4OnpSalSpRg4cCB9+/bl/v37TJw4kRIlStClS5cX62QRERELsXhA2rVrF+7u7rRt25ZmzZqRI0eOp25fu3Zt6tata+myMsSgQYMoWLAgy5cvZ8GCBZQoUYLPP/+cDz/80DhZ+2k6dOhAXFwc//3vf1m2bBk5c+akTp06DBo0yGRCe5cuXbh58ya9e/cmf/78+Pv7GwPKk2TLlo3w8HCmTJlCSEgIDx48oGLFisybNw83NzcApkyZQmhoKJ9++in58uXD1dWVb775Bj8/P44cOYKzszOhoaFMnTqV4OBgoqKiKFasGL179zY+aylXrlzMmzePsWPHMnDgQHLkyEHDhg0JDAzEzs7uBXpX5PX17/87+n8kYhlWhqTZthZy9epVSpQoYckmXmvOzs707t2bzz77LLNLyTT+/v44OTkRExOT2aWIWJy9vT2BgYHGz2HLly9fJlck8nLw9/cnLCws3Y5n8TlITwtHiYmJ3Llzh++//97SZYiIZCn58uVTOBKxIIsPsT148IDx48ezadMmHjx4wJMuWLVq1crSpYiIiIikisUDUkhICN9++y2FCxemWLFinD9/nho1avD3339z+fJlcuXKxeeff27pMrKs33//PbNLEBERyXIsPsT2448/4urqyk8//cTcuXMxGAyMGjWKrVu3EhwczMOHD/UsHBEREXmpWDwg3bhxg+bNm5MtWzaKFSuGg4MDv/zyC/D4IzGaN2/Ot99+a+kyRERERFLN4gEpe/bsxo+nAChVqhTnzp0zvnd3d+fKlSuWLkNEREQk1SwekEqWLGkyT6ZkyZImD0mMi4vj/v37li5DREREJNUsHpDefvttVqxYwaJFi4iLi6N69eocPnyY/fv3c+vWLVauXKnnJImIiMhLxeIBqVu3bjg5OTF+/HhiY2Np06YNhQsX5pNPPqFBgwacPXuWjh07WroMERERkVSz+G3+efLkYcWKFezatcv4CffLli1jxowZ3Llzh7fffhsfHx9LlyEiIiKSahYPSPD4c7+8vLyM74sUKcKYMWMyoml5Tdjb22d2CSIZQv/WRTJGugeka9euPdd+b7zxRjpXIq+T7t27Z3YJIiKShaR7QPLy8sLKyipN+1hZWXH69On0LkVERETkuaR7QHr//ffTHJBEREREXibpHpDGjx//Qvs/fPiQzZs3U79+fQoWLJhOVYmIiIiknsVv80+rmJgYhg4davK0bREREZGM9NIFJACDwZDZJYiIiMhr7KUMSCIiIiKZSQFJRERExIwCkoiIiIgZBSQRERERMwpIIiIiImYUkERERETMZMiH1YpYWnh4ODExMZldhmQwe3t7fQ6fiFjESxmQ9FElklYxMTEKSCIikm5eyiE2PShSREREMlOGBKTffvuNfv36cfv2beOycePG0bt3by5fvmyybcGCBfntt9+oU6dORpQmIiIikozFA9Lp06f54IMP2LZtGxEREcblOXLkYM+ePfznP/9JFpJEREREMpPFA1JISAj29vasX7+et956y7g8MDCQ77//nhw5cjBjxgxLlyEiIiKSahYPSCdPnsTPz49y5colW1eqVCk+/PBDfv75Z0uXISIiIpJqFg9IDx48IEeOHE9cb29vz927dy1dhoiIiEiqWTwglS1blm3btj1x/Y4dOyhTpoylyxARERFJNYsHJF9fX37++WcGDx7Mb7/9RlxcHPHx8Zw7d45hw4axf/9+fH19LV2GiIiISKpZ/EGRH3zwAcePH2f9+vV8//33JusMBgMtW7bEz8/P0mWIiIiIpFqGPEl74sSJ+Pj48OOPP3L16lXi4+MpUaIE77zzDvXr18+IEkRERERSLcM+aqR27drUrl3b4u0YDIbX9qNKXudzl9fX3bt3yZcvn/FvEZH0kO5zkK5du8Y///xj8j41f9LD9u3bCQoKSpdjZSZnZ2dCQkLStM/KlSuZMGGChSoydfXqVZydnVm9enWGtCfyJA8ePMDX15ejR4/i6+ubbt9LRETS/QrS22+/zcSJE2nVqhUAXl5ez7yqYWVlxenTp1+47a+//vqFj/EyWLFiBUWLFk3TPrNnz8bd3d1CFZkqXLgwK1asoFSpUhnSnsiTxMXFkZiYyI0bN0hMTOTevXuZXZKIZBHpHpDef/99kx+c77//voZ90sjV1TWzS3gqW1vbl75GERGRF5HuAWncuHEm78ePH5/eTaTIz8+Pw4cPA4+HqBYvXkznzp0ZNWoUc+bM4eHDh0ycOJH69euzcuVKli9fzoULF0hMTOTNN9+kZ8+etGjRAoDVq1czbNgwli9fztixYzl9+jQFChSgY8eOdO/e3djmpk2bCAsL49KlS+TOnZv69eszePBgChcuDDyeE7Rs2TKWLVvGlStXKFKkCP/5z3/o1q0bVlZWDBkyhOvXr1OmTBk2btxIyZIlWblyJZUrV6Z379589tlnHDp0iM6dOzN//nxmz57NqVOnKFKkCF26dKFTp07A46t0f/31F2vWrGHNmjVs376dEiVKcOnSJaZOncrRo0e5f/8+VatWpV+/ftSoUQN4PFT29ttvM2PGDNatW8f+/fvJnTs3nTp1onPnzowdO5atW7eSI0cO2rRpw8CBA7GysjLuN27cOHx8fAD4888/mTx5MgcPHsRgMODq6srgwYOpUKFCqvpKRETkZWLx5yBllBEjRlCpUiUqVarEihUrjJfap02bRlBQEEFBQbi6urJ06VKGDx/O22+/zZw5c5g0aRI2NjYMGjTIZP5CYmIi/fr1o0WLFsydO5caNWowefJk9uzZA8DRo0cZOHAgTZs2JTw8nKFDh3Lw4EEGDBhgPMbUqVMZM2YMnp6ezJ49m3bt2jFt2jRmzZpl3ObIkSNcvnyZkJAQevXqRfbsKWfW/v37U6lSJWbOnEm9evUYPXo033zzDQChoaEUKlQIT09PVqxYQeHChTl//jw+Pj5cuXKFYcOGMXnyZKysrOjSpYsxSCb54osvcHJyYvbs2dSuXZvg4GB8fX3JmTMnwcHBeHl5MW/ePLZs2ZJibREREbRr144LFy4wYsQIJk+ezN27d/noo4+IjIxMVV+JvIjz589ndgkiksVY/C62hIQEFi5cyM6dO7l58yaJiYnJtrGysuLHH398oXbKly+PnZ0d8HiI6tChQwB06NCBd99917jdlStX6Nq1K7169TIuK1GiBD4+Phw7dow33ngDeHz1JyAggHbt2gFQo0YNtm3bxs6dO2nQoAFHjx4lR44cdO/e3fhRKg4ODpw6dQqDwUBMTAwLFy7Ez8+PwYMHA1CvXj1jYEgSHx/PqFGjKF269FPP75133uGLL74AoEGDBkRERDB79mw6duxIpUqVsLW1xdHR0Tj0FRoaio2NDYsXL8be3h6ARo0a4e3tzaRJk1i5cqXx2A0aNKBfv37Gfty4cSMFChRg+PDhxro3b97MsWPHaN68ebLaFi5cyD///MPChQspVKgQAG+99Rbt27fnxIkTnD9//ql9ldoh2A0bNrBhw4ZUbSuvh0ePHgGwfv16AKKjozOzHBHJQiwekKZPn054eDjZs2enQIECZMuWLdk2BoPBYu07OzubvB8yZAgAMTExXLp0iUuXLnHgwAHg8YTPf3NzczO+TgogDx48AKBWrVpMmzaNVq1a0bx5cxo2bEj9+vXx9PQE4MSJE8TFxdGkSZMU20+SM2fOVE12fu+990zeN23alO3bt3Px4sUUPwj48OHDNG7c2BiOALJnz07Lli2ZOXMm9+/fT/E8kwJOtWrVjMusrKzIly8fMTExKdZ29OhRXF1djfvC44ncP/30EwD58+d/al+llre3N97e3smW+/v7p+k4knXY2toC0Lp1a9avX0/evHkzuSIRySosHpA2btxIrVq1CAsLI0+ePJZuLpkCBQqYvP/zzz8ZPnw4Bw8eJHv27JQtW9YYosyDWs6cOU3eW1tbG7dxc3Nj7ty5fP3118yfP5+wsDAKFSpE9+7d6dKlC3fu3AHA0dHxmfWl5gqK+VydpPN60m/Md+/epWDBgsmWFyxYEIPBYHK3T9KVt3/LlSvXM2tKcufOHUqUKPHE9c/qK5EXVb58+cwuQUSyGIsHpMjISD799NNMCUfmEhMT6dGjBzY2Nnz77bdUqlSJ7Nmzc/78eeMl+rRo0KABDRo0IDY2loMHD7J48WLGjh2Lq6ur8TfZyMhIypYta9zn+vXrXL582ThROrWSAleS27dvA8kDYJJ8+fJx69atZMv//vtv4PFVnYiIiDTV8CT29vZERkYmW37gwAFKlChByZIln9pX/75aJSIi8jKw+CTtt956i8uXL1u6GeDxFZ6niYqK4uLFi/j6+uLi4mKcEL17926AFOdHPcmECRPw9fXFYDCQK1cuGjdubHxI5fXr13FxccHGxobt27eb7Ldo0SL69u2b5kcf7Nixw+T9li1bKF68uHF4zvzca9WqxU8//WQyLJaQkMDGjRupWrWqcWgiPdSsWZMTJ04YQxs8Dobdu3dn+/btz+wrERGRl43FryD169ePgIAAXF1deeeddyzaVt68eTl+/DgHDhxI8YFxBQoUoHjx4ixdupSiRYuSN29e9u7dy6JFiwCIjY1NdVt16tRh4cKFDBkyhNatWxMXF8e8efNwcHCgdu3aODg40LlzZxYtWoStrS21a9fm1KlTLFmyhMDAwCferfYkX3/9NTlz5sTV1ZUffviBn376iSlTppic++nTpzl8+DAuLi707t2b3bt307lzZ3r06IGtrS1LlizhypUrzJs3L01tP8tHH33E2rVr+eSTT/D39ydHjhzMmTOHwoUL8/7773Py5Mmn9pXI87KxscHa2ppixYphbW2d4nCxiMjzsHhAcnNzo0aNGnz22WfY2tqmOOcmPe5iA+jYsSO//vor3bt3T/Y8piSzZs1izJgxDBkyBFtbW8qXL8/s2bMZO3YsR44cwc/PL1VtNWzYkMmTJ7NgwQJ69+6NlZUVNWrUYPHixTg4OAAwaNAgChYsyPLly1mwYAElSpTg888/58MPP0zzuX3++eesWbOGOXPmULZsWWbMmEGzZs2M67t27crYsWP55JNPWLhwITVr1mTZsmVMnTqVzz//HCsrK1xcXFi8eDE1a9ZMc/tPU6xYMZYtW8akSZMYOnQotra2uLu7M2nSJBwcHFLVVyLPI3fu3Hz33Xfky5fP+LeISHqwMljyFjJg7NixLF68mGzZslGgQIEnXjkxH0KSx5IeFLl48WI8PDwyu5yXkr+/P05OTk+8y06yLnt7ewIDAzO7DBF5Cfj7+xMWFpZux8uwu9hCQkJ0tUBEREReCRafpH3//n1atWqlcCQiIiKvDItfQapYsWKG3cWWFXl4ePD7779ndhkiIiKvFYtfQerTpw///e9/2bp1KwkJCZZuTkREROSFWfwK0uLFi8mTJw/9+vXD1tYWBweHZBO10+suNhEREZH0YPGAdPbsWbJnz06xYsWMy8xvnLPwjXQiIiIiaWLxgPTv2/cNBgNRUVHY2trqgW4iIiLy0rJ4QILHH/ExadIkfvjhB+OnyNvZ2dGsWTMGDBhA/vz5M6IMERERkVSxeECKjo6mQ4cOXL58mdKlS+Ph4UFCQgIXLlzgu+++49ChQ6xZs0ZXlEREROSlYfGANHv2bP78809Gjx5Nu3btTNZ99913fPnll8yZM4cBAwZYuhTJwuzt7TO7BMkE+rqLiKVYPCBt27aN9957L1k4AvD19eXo0aP88MMPCkjyQrp3757ZJYiISBZi8ecg3bhxA1dX1yeur1atGtevX7d0GSIiIiKpZvGAlC9fPq5du/bE9VevXtX8IxEREXmpWDwgeXh4sGzZMi5evJhs3R9//MHy5ctxd3e3dBkiIiIiqWbxOUi9evVix44dvP/++7z33nuUK1cOKysrzp07x/r167GysiIgIMDSZYiIiIikmsUDUrly5Zg3bx6ff/453377rcm6kiVL8tVXX+Hk5GTpMkRERERSLUMeFFmzZk22bt3K6dOn+fPPPzEYDJQqVYpKlSphbW3xUT4RERGRNMmQgASPP5C2cuXKVK5cOaOaFBEREXkuunwjIiIiYkYBSURERMSMApKIiIiIGQUkERERETMZNklbxJLCw8OJiYnJ7DIkjezt7fU5eiLyUlJAkiwhJiZGAUlERNKNhthEREREzCggiYiIiJhRQBIRERExo4AkIiIiYkYBSURERMSMApKIiIiIGQUkERERETMKSCIiIiJmFJBEREREzCggiYiIiJhRQHoFGAyGzC5BxGLu3r2b4msRkcykgPSSO3r0KD179szsMkwMGTIELy+vzC5DsoAHDx7g6+vLtWvXuHbtmvG1iEhm04fVvuRWrlzJ+fPnM7sMEwEBAXTu3Dmzy5AsIC4ujsTERO7duwdg8lpEJDMpIEmalSpVKrNLEBERsSgNsaUDLy8vpk2bxrhx43B3d8fd3Z1BgwYRFRUFPB6S6tKlCyNGjKBmzZq0adOG+Ph4Hj58yMyZM3n33XepWrUqTZs2Ze7cuSQmJhr3W7NmDX/99RfOzs6sXr0agIcPHzJx4kQ8PT2pUqUKrVq1YtOmTSY1JSQksHTpUlq1aoWLiwuNGjVi8uTJPHz40LjNkCFD+OSTT/j222955513cHFxoUOHDly8eJGffvqJVq1aUa1aNdq1a8eZM2dM9vv3EJvBYGDp0qW0bNkSFxcXmjRpQnh4uHHuVGRkJAMHDqRevXpUrVqV9957j7Vr11rkayEiIpIedAUpnSxbtozSpUszduxYIiMjmTJlChcuXGDlypUAHDlyBCsrK0JCQrh//z7ZsmWje/funDhxgl69evHWW29x6NAhpk+fzpUrVxg9ejQBAQFERkZy+vRpQkNDKVWqFAaDgV69enHs2DH69OlDuXLl2LZtG/379+fRo0e8//77AAwfPpy1a9fSrVs33N3dOX36NDNnzuTMmTPMmzcPKysrAE6cOEFERARDhgzhn3/+YeTIkfTo0QMrKyv69OmDtbU1Y8eOZeDAgWzcuDHFc586dSrz58/no48+ol69evzv//4v06ZN49GjR/Tq1YtBgwZx+/ZtRo0aRZ48eVi/fj1BQUEUK1YMDw+PDPn6yMvt3LlzmV2CiIgJBaR0YmVlxcKFC7G3twfA0dGRXr16sXv3bgDi4+MZNWoUpUuXBmDXrl3s37+fSZMm0bp1awDq1atHzpw5CQ4OpkuXLpQvXx5HR0dsbW1xdXUFYN++fezZs4dp06bRokULABo0aEBsbCyTJ0/G29ubS5cu8d1339GvXz8+/fRT47ELFy7M4MGD2b17N56engDcu3eP6dOnU65cOQAOHz7MihUr+Prrr6lTpw4AN27cYMKECURHR5M3b16T846OjmbhwoX4+fkxePBgY1uRkZEcPXrUeMyAgADeeecdADw8PHBwcCBbtmxp6uMNGzawYcOGNO0jL7dHjx4Bj0N2kujo6MwqR0TESAEpnTRu3NgYjuDxsJuNjQ1HjhwBIGfOnCZzdw4fPky2bNmMISdJ69atCQ4O5tChQ5QvXz5ZOwcOHMDKygpPT0/i4+NN2lu/fj3nzp3j+PHjALRq1cpk35YtWzJ06FAOHTpkDEj58uUzhiOAQoUKARgDGYCDgwNAigHpxIkTxMXF0aRJE5PlQ4YMMb728PAgJCSE3377DU9PTxo2bEhQUFCyc3sWb29vvL29ky339/dP87Hk5WBrawtAYGAg8Dgomf8bExHJDApI6aRw4cIm762trXFwcDD+NlygQAHjsBY8ft5L/vz5yZ7d9EuQFFBiYmJSbOfOnTsYDAaqV6+e4vqIiAjjs2SSjpUke/bs5M+f3+TYdnZ2KR4nV65cKS5PqR54fMXsSaZNm0ZYWBibN29my5YtWFtbU7duXUaOHEnJkiVT1Y5kbRUqVMjsEkRETCggpZOkoJAkISGBqKgoHB0duXHjRrLt8+XLR1RUFPHx8SYhKSIiAoD8+fOn2I69vT25c+dm8eLFKa4vXbo0f/31FwB///03JUqUMK6Li4sjKirqicd+Hkm/7UdGRlK2bFnj8uvXr3P58mVq1KiBvb09gwYNYtCgQVy4cIHt27cza9YsRo0axbx589KtFhERkfSiu9jSyZ49e4zzKQC2b99OfHy8cR6POXd3dxISEpLdfbZ+/XoAatSoATy+EmW+34MHDzAYDFStWtX459y5c8ycOZP4+Hjc3d0B+P7770323bhxIwkJCcZjpwcXFxdsbGzYvn27yfJFixbRt29fIiIi8PT0ZMuWLQCULVuW7t27U7du3RSDo4iIyMtAV5DSyY0bN/j000/p3Lkz169fZ+rUqdSvXx8PDw/WrFmTbPuGDRvi4eHBiBEjiIiIoFKlShw+fJjw8HDatGljnH+UN29ebt26xa5du3jrrbfw9PSkVq1aBAQEEBAQQLly5Th58iQhISHUr18fR0dHHB0dadOmDaGhofzzzz94eHhw5swZQkND8fDwoEGDBul23o6OjnTu3JlFixZha2tL7dq1OXXqFEuWLCEwMJDixYtTtGhRvvrqK+7du0epUqX49ddf2bVr10v3hHDJeDY2NlhbWxuHev/9WkQkMykgpZOWLVuSN29e+vXrR+7cuWnTpg39+/d/4vZWVlbMmTOHGTNmsHjxYiIjIylRogT9+/fn448/Nm7n4+PDrl276NWrF3369KFHjx7MnTuX4OBg5syZw+3btylSpAgfffQRvXr1Mu43ZswYSpcuzapVq5g/fz6FCxfGz8+PXr16Jbsq9aIGDRpEwYIFWb58OQsWLKBEiRJ8/vnnfPjhhwCEhoYydepUgoODiYqKolixYvTu3ZsePXqkax3y6smdOzffffcd+fLlAzB5LSKSmawM+iTUF+bl5YW7uzvjx4/P7FJeS/7+/jg5OT1xYru8vOzt7Y13sImIvAh/f3/CwsLS7XiagyQiIiJiRgFJRERExIzmIKWDHTt2ZHYJIiIiko50BUlERETEjAKSiIiIiBkFJBEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaPnIEmWYG9vn9klyHPQ101EXlYKSJIldO/ePbNLEBGRLERDbCIiIiJmFJBEREREzCggiYiIiJhRQBIRERExo4AkIiIiYkYBSURERMSMApKIiIiIGQUkERERETMKSCIiIiJmFJBEREREzOijRiRLCA8PJyYmJrPLeOnZ29vrY1lERFJBAUmyhJiYGAUkERFJNxpiExERETGjgCQiIiJiRgFJRERExIwCkoiIiIgZBSQRERERMwpIIiIiImYUkERERETMKCCJiIiImFFAEhERETGjgCQiIiJiRgHpJWMwGDK7BBMvWz0iIiIZQQHpJXL+/Hk++OADk2XOzs6EhIS80HFXr16Ns7MzV69eTfU+0dHRBAUFceTIEeMyPz8//Pz8XqgWeTncvXuXu3fvZnYZIiIvLX1Y7Utk8+bNHD9+PLPLAODMmTOsXbsWHx8f47IRI0ZkYkWSXq5du2YMut988w1vvPFGJlckIvLyUUCSVCtfvnxmlyDp4N69eym+FhGR/6Mhtufg5eVFaGgo48aNw8PDAzc3NwYMGMD9+/eZO3cuDRs2pEaNGnz22WdERUUZ91u5ciUtW7akSpUqNGrUiJCQEOLj4wEICQkhNDQUSD6sdu/ePb744gvc3d1xc3OjT58+3L59G4ClS5fi7OzMxYsXTWrcuHEjFStWfOKw2sqVK/Hx8cHV1RUXFxfee+89Nm3aBMChQ4fo3LkzAJ07dzZebTAfYnv48CEzZ87k3XffpWrVqjRt2pS5c+eSmJho3MbPz48vvviCuXPn0qhRI6pWrUqHDh345Zdfnq/zRUREMoAC0nNauHAh165dY9q0afj7+7Nhwwbatm3Lvn37GD16NJ999hnbt29nxowZAMyZM4cvv/ySOnXqEBYWRseOHQkPD2f48OEAtGvXDl9fXwBWrFhBu3btjG0tXryYuLg4goOD6d+/Pzt27GDUqFEAtGrVihw5crBu3TqT+tasWYO7uzslSpRIVvvSpUsZPnw4b7/9NnPmzGHSpEnY2NgwaNAgrl27RuXKlY11DR8+PMWhNYPBgL+/P/PmzcPX15ewsDDeffddpk+fnmz7rVu3sn37doYNG8bUqVO5desWffr0ISEh4Xm7X0RExKI0xPac8uTJw7Rp08iePTt169ZlzZo1REREsHLlSuzt7fH09OTgwYMcO3aMmJgYZs+eTfv27Rk2bBgA9evXx8HBgWHDhvHxxx9ToUIFihYtCoCrq6tJW1WrVmXixIkA1KlTh5MnT7J7924A8ubNS5MmTVi/fj19+/bFysqKiIgI9u/fz9ixY1Os/cqVK3Tt2pVevXoZl5UoUQIfHx+OHTuGt7e3cTitfPnyKQ6t7d69m/379zNp0iRat24NQL169ciZMyfBwcF06dLFuF98fDzz58/Hzs4OgPv37xMUFMSZM2eoUqVKqvt8w4YNbNiwIdXbS8qio6NTfC0iIv9HAek5ubi4kD37/3VfoUKFsLOzw97e3rjMwcGBs2fPcvz4cWJjY/Hy8jIOqcHjoTqAffv2UaFChSe2VaNGDZP3JUuWNPnB5uvry4YNGzhy5Ai1atVi3bp15MyZk2bNmqV4vCFDhgAQExPDpUuXuHTpEgcOHAAgLi4uVed/+PBhsmXLRosWLUyWt27dmuDgYA4dOmQSspLCEUCRIkUAiI2NTVVbSby9vfH29k623N/fP03Hed3lzZs3xdciIvJ/FJCe079/4CfJlStXitveuXMHgB49eqS4PiIi4qlt5c6d2+S9tbW1yfOJateuTYkSJVi7di21atVi7dq1NG/e/In1/PnnnwwfPpyDBw+SPXt2ypYti7OzM5D65x7dvXuX/Pnzm4REeBwU4XH4SmJeh7X145Hdf89VEhEReZkoIGWApN/SJ0+eTJkyZZKtL1iw4Asd38rKijZt2rB48WI6duzI+fPn+Z//+Z8Ut01MTKRHjx7Y2Njw7bffUqlSJbJnz8758+dZv359qtvMly8fUVFRxMfHm4SkpLCXP3/+FzonERGRzKRJ2hmgWrVq2NjYcPPmTapWrWr8Y2Njw5QpU4x3miVdWXkebdu2JSYmhnHjxlGmTJlkw3JJoqKiuHjxIr6+vibDhElzmpKu6mTLlu2p7bm7u5OQkGC88y1JUsh6UvsiIiKvAl1BygD58+enW7duBAcHc+/ePTw8PLh58ybBwcFYWVlRsWJF4P+uNG3YsIFq1apRsmTJVLdRrFgx6taty969e+nfv/8TtytQoADFixdn6dKlFC1alLx587J3714WLVoE/N+8oKS5VDt37iRfvnzGGpM0bNgQDw8PRowYQUREBJUqVeLw4cOEh4fTpk0bPTPpJfbv4eGUhopFRERXkDJMv379GDJkCNu2baN79+5MmjSJGjVqsGTJEmMYadq0KVWrVmXIkCHMnz8/zW00btwYa2tr3n///aduN2vWLIoUKcKQIUPo168fJ06cYPbs2ZQtW9b40SIVKlTA29ubpUuXMnDgwGTHsLKyYs6cOXTo0IHFixfTo0cPtmzZQv/+/RkzZkyaa5eM88Ybb7B69WpWr16tp2iLiDyBlUGfRppldO/enWzZshEWFpbZpWQof39/nJycTCaGS8rs7e0JDAzM7DJERNKdv79/uv780xBbFjBz5kwuXrzI7t27WbJkSWaXIyIi8spTQMoCduzYweXLlxk0aBC1atXK7HJEREReeQpIWcCqVasyuwQREZEsRZO0RURERMwoIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjAKSiIiIiBkFJBEREREzeg6SZAlJn2cnT6d+EhFJHQUkyRK6d++e2SWIiEgWoiE2ERERETMKSCIiIiJmFJBEREREzCggiYiIiJhRQBIRERExo4AkIiIiYkYBSURERMSMApKIiIiIGQUkERERETMKSCIiIiJm9FEjkiWEh4cTExOT2WW8MHt7e31siojIS0ABSbKEmJiYLBGQRETk5aAhNhEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjAKSiIiIiBkFJBEREREzCkgiIiIiZhSQshCDwZDZJYiIiGQJCkiviCFDhuDl5WV87+fnh5+fn/H9ypUrmTBhgvH96tWrcXZ25urVqxlaZxIvLy+GDBmSKW2/LOLi4jK1/bt372Zq+yIirzIFpFfUiBEjGDFihPH97NmzuXPnjvF9o0aNWLFiBYULF86E6iQ2NpYDBw4QGxubKe1fu3YNX19frl27linti4i86rJndgHyfMqXL//U9Y6Ojjg6OmZQNWIuPj7e5O+Mdu/ePRITE7l3716mtC8i8qrTFaQ0MBgMLF26lJYtW+Li4kKTJk0IDw83zv3Zt28fH374ITVq1MDDw4MBAwZw/fp14/6rV6+mUqVK/PLLL7Rv356qVavSqFEjwsPDTdq5e/cuQ4cOxcPDg1q1ajFp0iQSExNNtvn3EJuXlxd//fUXa9asMQ6rpTTEll71Xb16lcGDB1O/fn0qV65MnTp1GDx4MFFRUU/su02bNtG6dWtcXFyoXbs2AwcOJCIiIo1fARERkYyhgJQGU6dOZcyYMXh6ejJ79mzatWvHtGnTmDVrFuvWraNr164UKVKEqVOnMnToUI4fP0779u25ffu28RiJiYn069ePFi1aMHfuXGrUqMHkyZPZs2ePcX23bt3YuXMnAwcOZMKECRw/fpxNmzY9sa7Q0FAKFSqEp6fnE4fV0qu+2NhYOnfuzB9//MGIESOYP38+nTp1YsOGDUydOjXF+o4ePcrAgQNp2rQp4eHhDB06lIMHDzJgwIDn+jqIiIhYmobYUik6OpqFCxfi5+fH4MGDAahXrx6RkZEcPXqU5cuXU7duXaZNm2bcp3r16rRo0YIFCxYwaNAg4PFVqICAANq1awdAjRo12LZtGzt37qRBgwbs3r2bkydPMmfOHBo1agRA7dq1TSZom6tUqRK2trY4Ojri6uqabH1iYiKTJk1Kl/ouXbpE0aJFGT9+PKVKlTLWd+rUKQ4fPpxifUePHiVHjhx0796dHDlyAODg4MCpU6cwGAxYWVk9s/9fVWkd4kpMTOTs2bMv3O65c+de+BgiIq8zBaRUOnHiBHFxcTRp0sRk+ZAhQ/jjjz9o0aIFgYGBJutKlSqFm5sbhw4dMlnu5uZmfJ0UbB48eADAkSNHsLGxoWHDhsZtcufOjaenJz///PNz1X7x4kX+/vvvdKnvrbfeYtmyZSQmJnLlyhUuXbrEuXPnuHDhwhPn29SqVYtp06bRqlUrmjdvTsOGDalfvz6enp5pOo8NGzawYcOGNO2TWZLuYHueoLJ37950qyM6OjrdjiUi8jpRQEqlpDvEUpr4nLSuYMGCydYVLFiQ06dPmyzLmTOnyXtra2vjPKa7d+/i4OCAtbXp6GehQoWet/R0rQ9g4cKFzJkzh6ioKAoWLEjlypXJlSsXMTExKbbv5ubG3Llz+frrr5k/fz5hYWEUKlSI7t2706VLl1Sfh7e3N97e3smW+/v7p/oYGcXGxgaAChUqYGdnl+r98uTJQ8eOHV+4/XPnzjF16lTy5s37wscSEXkdKSClUtIPmsjISMqWLWtcfv36dX7//XcAbt26lWy/v//+m/z586e6nfz58xMVFUVCQgLZsmUzLv/3Lfxp5eDgkG71ff/994wfP54BAwbg6+trDIx9+/bl1KlTT9yvQYMGNGjQgNjYWA4ePMjixYsZO3Ysrq6uVKtWLW0n9Aqxs7PD3t4+1dvb29vj5ORkwYpERCQ1NEk7lVxcXLCxsWH79u0myxctWsT06dMpVKgQ33//vcm6K1eucOLECapXr57qdurUqUN8fDw//vijcdmjR4/Yt2/fU/czv+L0b2+++Wa61Xf06FHs7e3p0aOHMRzdv3+fo0ePJrvTLsmECRPw9fXFYDCQK1cuGjduTFBQEIDJXXQiIiIvC11BSiVHR0c6d+7MokWLsLW1NU5MXrJkCYGBgTg4ODB06FD69+/P+++/T1RUFKGhoeTLl4+PP/441e3UqVOH+vXrM2zYMG7fvk3x4sVZvHgxkZGRFChQ4In75c2bl9OnT3P48GFcXFxM1llbWxMYGJgu9bm4uLB8+XLGjx9P48aNiYiIYP78+dy6dYt8+fI98ZwWLlzIkCFDaN26NXFxccybNw8HBwdq166d6rZfJdmzZzf5O6PZ2dlhbW2dpuE9ERH5PwpIaTBo0CAKFizI8uXLWbBgASVKlODzzz/nww8/BB7PH5kzZw69evXCzs6OBg0aEBgYmOb5Q6GhoUyePJkZM2bw8OFDWrRowX/+859kV6/+rWvXrowdO5ZPPvmEhQsXJlvv4+OTLvW1adOGq1evsmrVKpYtW0aRIkXw9PTkww8/5Msvv+T8+fPJHmLZsGFDJk+ezIIFC+jduzdWVlbUqFGDxYsXG4f/sppcuXJRp04d41ykjPbGG2/w3XffPTG0iojI01kZ9Amn8orz9/fHycnpiZPEXyX29vbJ7jYUEZFn8/f3JywsLN2OpzlIIiIiImYUkERERETMKCCJiIiImFFAEhERETGjgCQiIiJiRgFJRERExIwCkoiIiIgZBSQRERERMwpIIiIiImYUkERERETM6LPYJEuwt7fP7BLSRVY5DxGRV50CkmQJ3bt3z+wSREQkC9EQm4iIiIgZBSQRERERMwpIIiIiImYUkERERETMKCCJiIiImNFdbPLKO336NP7+/pldRpZ38+ZNihQpktllZHnq54yhfs4YGdnPV65cSdfjKSDJK69SpUqEhYVldhlZnr+/v/o5A6ifM4b6OWO8yv2sITYRERERMwpIIiIiImYUkERERETMKCCJiIiImFFAkleet7d3ZpfwWlA/Zwz1c8ZQP2eMV7mfrQwGgyGzixARERF5megKkoiIiIgZBSQRERERMwpIIiIiImYUkERERETMKCCJiIiImFFAEhERETGjgCQiIiJiRgFJXmq7d+/Gx8eHatWq0bhxY+bMmcOzHt21bt06WrZsiYuLC82aNWPlypUZVO2r63n6Ocmvv/5K5cqVuXr1qoWrfPWltZ8fPXrEnDlzePfdd3F1daVZs2aEhoby6NGjDKz61ZPWfo6NjWXixIk0btyYatWq0b59e3bv3p2BFb+aXuT7Rnx8PG3btsXPz8/CVT4/BSR5aR07doyAgADKlStHSEgIrVu3Ztq0aYSFhT1xn82bNxMUFES9evWYOXMmtWvXZtiwYaxfvz4DK3+1PE8/J/ntt9/o2bMn8fHxGVDpq+15+nns2LHMnj0bHx8fZs+eTbt27Zg3bx4jR47MuMJfMc/Tz0OHDmXFihV0796d2bNnU6pUKfz9/Tly5EgGVv5qeZHvGwBz587l119/tXCVL8gg8pLq2rWroW3btibLJk6caHB1dTXExsamuE/Tpk0Nffr0MVnWt29fwzvvvGOxOl91z9PPDx8+NMyfP9/g4uJicHd3Nzg5ORmuXLmSEeW+stLaz1FRUQZnZ2dDeHi4yfLw8HCDk5OT4fbt2xat91WV1n6+fPmywcnJybB06VLjsoSEBIOXl5ehf//+Fq/3VfU83zeSnDlzxuDi4mKoV6+eoVOnTpYs84XoCpK8lB49esShQ4do2rSpyfJmzZrx4MGDFH+zu3r1KpcuXUpxnz///JOLFy9atOZX0fP0Mzy+tB4aGoq/vz8DBw7MiFJfac/TzzExMXTo0AEvLy+T5WXKlAHgypUrFqv3VfU8/Vy0aFG+++47WrdubVxmbW1N9uzZNZT5BM/7fQMgLi6OoKAg/Pz8ePPNNy1d6gtRQJKX0pUrV4iLizP+MEhSunRpAC5dupRsnz/++AMgTfu87p6nnwGqVq3Kjh07+PTTT8mWLZuFq3z1PU8/lyxZkpEjR1K2bFmT5du2bcPGxibZseT5+tnW1paqVatiZ2dHYmIi165dY8yYMfz555906NAhA6p+9Tzv9w2A0NBQ4uLi6NOnjwUrTB/ZM7sAkZRER0cDYGdnZ7I8T548ANy7dy/ZPjExMWne53X3PP0MUKRIEcsWlsU8bz+b27p1K+vWraNz587ky5cvfYvMAl60n+fMmcP06dMB8PX1xd3dPf2LzAKet59PnjzJggULWLp0Kba2tpYtMh0oIMlLKTExEQArK6sU11tbJ7/4+aR9DP//roqU9nndPU8/S9qlRz9v2bKFgQMHUqtWLQ1rPsGL9rOXlxc1a9bk119/JSQkhBs3bjB//vx0r/NV9zz9/PDhQ4YMGUKXLl1wcXGxaH3pRQFJXkp58+YFkv8mcv/+fSD5by5P2+fBgwdP3Od19zz9LGn3ov28cOFCJk6ciLu7O7NmzXolfvvODC/az87OzgDUqlULe3t7vvjiC44ePUqNGjUsUO2r63n6efr06SQmJhIQEGC86zXpl9f4+HiyZcv2xMCVWfTrobyUSpUqRbZs2bh8+bLJ8qT35cuXT7ZP0oS/tOzzunuefpa0e95+NhgMjB49mvHjx9OsWTPCw8ONwxiS3PP085UrV1i5ciUPHz40WV61alUAbty4YaFqX13P089bt27l4sWLuLm5UblyZSpXrszPP//Mzz//TOXKlVmzZk2G1J4WCkjyUsqRIwc1a9Zk27ZtJg8e27p1K3nz5k3xEm3p0qUpWbIkW7duNVm+detWypQpQ/HixS1e96vmefpZ0u55+3nq1KksWbKEjz76iGnTpunK0TM8Tz9fvXqVYcOG8cMPP5gs37NnDwAVK1a0bNGvoOfp59mzZ/Pdd9+Z/EkKSt999x2NGzfOyFNIFQ2xyUvr008/5eOPP6Zv3760bduW48ePM3/+fAYOHEjOnDm5d+8e58+fp1SpUjg6OgIQEBDA0KFDcXBwwMvLix07drB582amTZuWyWfz8nqefpa0S2s/nzlzhvDwcKpUqULz5s355ZdfTI5Xvnx5DYGmIK397O7ujoeHB6NHjyY6OpqyZcty8OBB5s+fT/v27SlXrlxmn9JLKa39nDR8+W9JV0OTrta9dDLvEUwiz/bDDz8YvL29DZUrVzZ4eXkZ5s+fb1x38OBBg5OTk2HVqlUm+yxfvtzQpEkTQ5UqVQzNmzc3rFmzJoOrfvU8Tz8nWbVqlR4UmUpp6efp06cbnJycnvjn4MGDmXUaL720/nuOiYkxjB8/3tC4cWND5cqVDc2aNTMsXLjQkJCQkBnlvzJe5PuGwWAwdOrU6aV+UKSVwZDKD04REREReU1oDpKIiIiIGQUkERERETMKSCIiIiJmFJBEREREzCggiYiIiJhRQBIRERExo4AkIiIiYkYBSURERMSMApKIiIiIGQUkERERETMKSCIiIiJmFJBEREREzCggiYiIiJhRQBIRAfz8/HB2ds7sMl5IREQEDx48yOwyRLIEBSQREcDf35+JEydmdhnPbe3atTRr1ozIyMjMLkUkS8ie2QWIiLwM6tWrl9klvJCDBw/q6pFIOtIVJBEREREzCkgiIpjOQVq9ejXOzs4cPHiQMWPGUL9+fVxcXGjXrh0///wzsbGxjB8/nvr16+Pm5saHH37IyZMnjce6evUqzs7OzJo1i6+//hovLy9cXFxo1aoV3377bbK2Hz58yMyZM3n33XepUqUK7u7u+Pv7c+LECZPtQkJCcHZ2ZteuXTRv3pwqVarQqVMnvLy8WLNmDQBvv/02fn5+xn3OnDlDYGAgDRs2pEqVKlSvXp0OHTqwadOmZOfv7e3Nb7/9Rvfu3alRowZubm507drV5NySnDp1is8++4w6derg5ubGe++9x4oVKzAYDCbb7dmzh86dO1O9enWqVauGj48Pq1evTtsXRyQTWBnM/zWLiLyG/Pz8OHz4ML///jurV69m6NChFCtWjEKFCvH+++9z+/Zt5s2bh62tLRUrViQ+Pp6WLVty584d5s2bR548efjhhx+ws7Pj6tWrvP322xQvXpzbt2/TqVMnChcuzIYNGzh58iT+/v70798fgNjYWLp06cIvv/zCO++8Q926dbl16xYrVqzg7t27TJ48mebNmwOPA1JoaCi5c+fG19eXMmXKYGtrS/78+Vm4cCFHjhxh6NChVKhQgXr16vHLL7/QqVMnihUrhq+vL/nz5+fKlSusWLGCO3fusHz5cqpXr248/7Nnz5KQkICnpye1atXi6tWrfP311+TIkYOdO3dib28PPA49n376KXZ2dnzwwQcULlyYbdu2sW/fPpNzW7p0KaNHj6Zq1ao0b94ca2trtm/fzuHDh+ncuTNffPFFJnylRVLJICIihk6dOhmcnJwMBoPBsGrVKoOTk5OhVatWhkePHhm3GTNmjMHJycnw3nvvGeLj443LJ02aZHBycjLs37/fYDAYDFeuXDE4OTkZnJ2dDT///LNxu4cPHxp8fX0Nb731luHy5csGg8FgCA0NNTg5ORmCg4NN6omIiDB4eHgYatasaYiOjjYYDAbDjBkzDE5OToY+ffokqz8oKMjg5ORkuHLlinFZr169DFWqVDHcvHnTZNudO3canJycDKNHj052/nPnzjXZdubMmQYnJyfDihUrDAaDwZCYmGho3Lixwd3d3XDjxg3jdgkJCYYPPvjAUKVKFUNUVJTh+vXrhsqVKxt69uxpSExMNG6XmJhoGDRokMHJycnwyy+/JP9CiLwkNMQmIvIEzZo1w8bGxvi+fPnyALz77rtky5bNuLx06dIA3Lx502T/evXqUbNmTeN7W1tbPv74YxISEti+fTsAW7ZsIXfu3PTs2dNk30KFCtG5c2eio6PZs2ePyTovL69U1T9jxgx27txJ4cKFjcvi4+NJTEwE4P79+8n2ad26tcn7qlWrAvD3338DcPr0af766y9at25NkSJFjNtZW1szadIk1q1bh52dHT/88ANxcXE0b96cqKgoIiMjiYyMJCoqipYtWwLwww8/pOo8RDKD7mITEXmCQoUKmbzPnj17isuTwlJS8Eji5OSU7Jhly5YF4M8//zT+Xbp0aXLkyJFs26T9r169+tS6nsTa2po7d+6wYMECzp8/z9WrV/nzzz+Ji4sDSDZfKKVj29rampxbUi3lypVLtm/x4sWNry9evAjA4MGDn1jfX3/9larzEMkMCkgiIk+QFIjMWVlZpWr/pHDxbwkJCcD/hSqDwfDE4yVta36cf1+9epr169cTFBREgQIFqFWrFi1atMDZ2ZkiRYrg6+ub4j7W1k8fWIiPjwee3QdJtY8ePZoSJUqkuI2jo+OzTkEk0yggiYhYyOXLl5Mtu3DhAgBvvvkmAKVKleLPP//k4cOHya4inTt3DoA33ngjzW0/fPiQESNGUKpUKVatWoWdnZ1x3dGjR9N8vCRJYSfpCtG/7du3j7Vr19KtWzfjdnnz5qVu3bom20VERHDy5ElKliz53HWIWJrmIImIWMiPP/7IpUuXjO8fPXrE/PnzsbW15Z133gEez3N68OABc+bMMdn39u3bLFmyhDx58lC/fv1ntpV05Sdp2Oyff/7hwYMHlChRwiQcxcfHs2DBAuPrtKpcuTJFixbl+++/N3lqt8FgYP78+WzcuJFChQrRtGlTrK2tCQsL459//jE5xvjx4+nVqxe//vprmtsXySi6giQiYiHW1tZ06NCBTp06YWdnx9q1azlz5gyff/65cYLzJ598wk8//cTMmTM5e/YsderUITIykhUrVhAdHc3EiRPJnTv3M9sqWLAgAPPmzaNBgwa888471KpVi7179zJ06FCqV6/OnTt3+P7777lw4QLW1tbExMSk+ZyyZ8/OqFGj6NWrF++99x4dOnQgf/78/Pjjj+zbt4+BAwfi6OiIo6Mjn332GcHBwbz33nu0adOGvHnzsn37dvbu3Uvjxo1p2rRpmtsXySgKSCIiFtKiRQsqVKjAokWLiI6OpmLFisycOdN49Qggd+7cLFmyhLlz57J582bj84Zq1KhBt27dcHV1TVVbH3zwAYcPH2bVqlUcPHiQd955h+nTpzNlyhT27t3Lhg0bKFSoEFWqVGHixImMHDmSI0eOEBsbS65cudJ0Xo0aNWLp0qXMmjWLhQsXkpCQQLly5Zg6darxDjWAgIAAypcvz+LFi5k7dy6JiYmULFmSwYMH4+fnl+q5VCKZQQ+KFBFJZ0kPimzTpg3jx4/P7HJE5DloDpKIiIiIGQUkERERETMKSCIiIiJmNAdJRERExIyuIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjAKSiIiIiBkFJBEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaOAJCIiImJGAUlERETEjAKSiIiIiBkFJBEREREzCkgiIiIiZhSQRERERMwoIImIiIiYUUASERERMaOAJCIiImLm/wGsya+Tk6AHwwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(\n",
" x=\"importance\",\n",
" y=\"omic_layer\",\n",
" data=global_feature_importance_df,\n",
" errwidth=0.5,\n",
" ci=95,\n",
" capsize=0.2,\n",
" order=global_feature_importance_omic_summary_df[\"omic_layer\"].tolist(),\n",
" # palette=OMIC_PALLETS\n",
" color='grey'\n",
")\n",
"# plt.savefig(f\"./reports/vae/latent/{TIMESTAMP}_latent_shap_omic_ranking.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"for omic in global_feature_importance_omic_summary_df[\"omic_layer\"].tolist():\n",
" feature_df = global_feature_importance_df[\n",
" global_feature_importance_df[\"omic_layer\"] == omic\n",
" ]\n",
" feature_df[\"feature\"] = feature_df[\"feature\"].map(lambda x: x.split(\"_\")[1])\n",
" feature_df[[\"feature\", \"importance\"]].to_csv(\n",
" f\"./reports/vae/files/{TIMESTAMP}_shap_values_{omic}_for_gsea.csv\",\n",
" index=False,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mosa",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Datasets
Models
