extract_tybalt_weights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Tybalt Weights\n",
    "\n",
    "The weights learned by the Tybalt model indicate patterns of gene expression variably activated across tumors. As Tybalt is an unsupervised model, these weights learned can point to known biology, unknown biology, or unrelated noise. One of the benefits of a variational autoencoder (VAE), is that the weights learned that represent each encoding are  nonlinear. Therefore, they can extract out signal representative of noise while still retaining relevant known and unknown biology.\n",
    "\n",
    "Here, we extract the weights learned by the VAE and save them to file. We also explore specific signals that should be present in the data:\n",
    "\n",
    "1. Signals representing the sex of the patient\n",
    "2. Melanoma activation patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from keras.models import load_model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style='white', color_codes=True)\n",
    "sns.set_context('paper', rc={'font.size':8, 'axes.titlesize':10, 'axes.labelsize':15})   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('seaborn-notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the complex architecture involved in encoding the data, we will use the `decoded` weights to describe feature encoding specific activation patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gway/anaconda3/envs/vae_pancancer/lib/python2.7/site-packages/keras/models.py:258: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "# Load the decoder model\n",
    "decoder_model_file = os.path.join('models', 'decoder_onehidden_vae.hdf5')\n",
    "decoder = load_model(decoder_model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>DDX3Y</th>\n",
       "      <th>KDM5D</th>\n",
       "      <th>...</th>\n",
       "      <th>FAM129A</th>\n",
       "      <th>C8orf48</th>\n",
       "      <th>CDK5R1</th>\n",
       "      <th>FAM81A</th>\n",
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       "      <th>GDPD3</th>\n",
       "      <th>SMAGP</th>\n",
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       "      <th>POU5F1B</th>\n",
       "      <th>CHST2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>TCGA-02-0047-01</th>\n",
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       "      <td>0.687833</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.092586</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.140642</td>\n",
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       "      <td>0.592269</td>\n",
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       "      <td>0.610192</td>\n",
       "      <td>0.374569</td>\n",
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       "<p>2 rows × 5000 columns</p>\n",
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      "text/plain": [
       "                   RPS4Y1      XIST      KRT5  AGR2  CEACAM5     KRT6A  \\\n",
       "TCGA-02-0047-01  0.678296  0.289910  0.034230   0.0      0.0  0.084731   \n",
       "TCGA-02-0055-01  0.200633  0.654917  0.181993   0.0      0.0  0.100606   \n",
       "\n",
       "                    KRT14   CEACAM6     DDX3Y     KDM5D    ...      FAM129A  \\\n",
       "TCGA-02-0047-01  0.031863  0.037709  0.746797  0.687833    ...     0.440610   \n",
       "TCGA-02-0055-01  0.050011  0.092586  0.103725  0.140642    ...     0.620658   \n",
       "\n",
       "                  C8orf48    CDK5R1    FAM81A  C13orf18     GDPD3     SMAGP  \\\n",
       "TCGA-02-0047-01  0.428782  0.732819  0.634340  0.580662  0.294313  0.458134   \n",
       "TCGA-02-0055-01  0.363207  0.592269  0.602755  0.610192  0.374569  0.722420   \n",
       "\n",
       "                  C2orf85   POU5F1B     CHST2  \n",
       "TCGA-02-0047-01  0.478219  0.168263  0.638497  \n",
       "TCGA-02-0055-01  0.271356  0.160465  0.602560  \n",
       "\n",
       "[2 rows x 5000 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load RNAseq file\n",
    "rnaseq_file = os.path.join('data', 'pancan_scaled_zeroone_rnaseq.tsv.gz')\n",
    "rnaseq_df = pd.read_table(rnaseq_file, index_col=0)\n",
    "rnaseq_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a future pathway analysis, the background genes are important\n",
    "# Also needed to set column names on weights\n",
    "background_file = os.path.join('data', 'background_genes.txt')\n",
    "background_genes = pd.DataFrame(rnaseq_df.columns)\n",
    "background_genes.to_csv(background_file, index=False, header=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Tybalt weight matrix and write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPS4Y1</th>\n",
       "      <th>XIST</th>\n",
       "      <th>KRT5</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>CEACAM5</th>\n",
       "      <th>KRT6A</th>\n",
       "      <th>KRT14</th>\n",
       "      <th>CEACAM6</th>\n",
       "      <th>DDX3Y</th>\n",
       "      <th>KDM5D</th>\n",
       "      <th>...</th>\n",
       "      <th>FAM129A</th>\n",
       "      <th>C8orf48</th>\n",
       "      <th>CDK5R1</th>\n",
       "      <th>FAM81A</th>\n",
       "      <th>C13orf18</th>\n",
       "      <th>GDPD3</th>\n",
       "      <th>SMAGP</th>\n",
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       "      <th>POU5F1B</th>\n",
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       "      <td>0.012619</td>\n",
       "      <td>-0.041976</td>\n",
       "      <td>-0.035155</td>\n",
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       "      <td>-0.003017</td>\n",
       "      <td>-0.042659</td>\n",
       "      <td>-0.044308</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007668</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.020055</td>\n",
       "      <td>0.012365</td>\n",
       "      <td>0.010221</td>\n",
       "      <td>0.010597</td>\n",
       "      <td>-0.018201</td>\n",
       "      <td>0.031298</td>\n",
       "      <td>-0.014771</td>\n",
       "      <td>-0.012871</td>\n",
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      "text/plain": [
       "             RPS4Y1      XIST      KRT5      AGR2   CEACAM5     KRT6A  \\\n",
       "encodings                                                               \n",
       "1         -0.038720  0.016661  0.029607  0.039584 -0.001849  0.012594   \n",
       "2         -0.038551  0.025872  0.026606  0.014903  0.011040 -0.018997   \n",
       "\n",
       "              KRT14   CEACAM6     DDX3Y     KDM5D    ...      FAM129A  \\\n",
       "encodings                                            ...                \n",
       "1          0.016473  0.012619 -0.041976 -0.035155    ...     0.015413   \n",
       "2          0.002153 -0.003017 -0.042659 -0.044308    ...    -0.007668   \n",
       "\n",
       "            C8orf48    CDK5R1    FAM81A  C13orf18     GDPD3     SMAGP  \\\n",
       "encodings                                                               \n",
       "1         -0.021422 -0.009609 -0.016911  0.001458 -0.014941 -0.008457   \n",
       "2          0.000821  0.020055  0.012365  0.010221  0.010597 -0.018201   \n",
       "\n",
       "            C2orf85   POU5F1B     CHST2  \n",
       "encodings                                \n",
       "1         -0.028340 -0.004947  0.014693  \n",
       "2          0.031298 -0.014771 -0.012871  \n",
       "\n",
       "[2 rows x 5000 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the weights from the decoder model\n",
    "weights = []\n",
    "for layer in decoder.layers:\n",
    "    weights.append(layer.get_weights())\n",
    "    \n",
    "weight_layer_df = pd.DataFrame(weights[1][0], columns=rnaseq_df.columns, index=range(1, 101))\n",
    "weight_layer_df.index.name = 'encodings'\n",
    "weight_layer_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the genes to file\n",
    "weight_file = os.path.join('results', 'tybalt_gene_weights.tsv')\n",
    "weight_layer_df.to_csv(weight_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting example patterns learned by Tybalt\n",
    "\n",
    "Focusing on two examples: Sex-specific and tissue-specific activation\n",
    "\n",
    "### Sex specific activation by node 82"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3OjI0NJTc3FwCAwM5ffo0y5cvx8PDA71eT3d3N9u2bSMiIuKqoti9oqKi0Ov1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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f027018ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We previously identified node 82 as robustly separating sex in the data set:\n",
    "# Visualize the distribution of gene weights here\n",
    "sex_node_plot = weight_layer_df.loc[[82, 85], :].T\n",
    "sex_node_plot.columns = ['encoding 82', 'encoding 85']\n",
    "\n",
    "sex_node_plot = (\n",
    "    sex_node_plot.reindex(sex_node_plot['encoding 82'].abs()\n",
    "                          .sort_values(ascending=False).index)\n",
    "    )\n",
    "\n",
    "g = sns.jointplot(x='encoding 82', y='encoding 85',\n",
    "                  data=sex_node_plot, color='black',\n",
    "                  edgecolor=\"w\", stat_func=None);\n",
    "\n",
    "# Save Figure\n",
    "sex_node_plot_file = os.path.join('figures', 'sex_node_gene_scatter.pdf')\n",
    "g.savefig(sex_node_plot_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encoding 82</th>\n",
       "      <th>encoding 85</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EIF1AY</th>\n",
       "      <td>-0.543605</td>\n",
       "      <td>0.013644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTY</th>\n",
       "      <td>-0.514367</td>\n",
       "      <td>0.019198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYorf15A</th>\n",
       "      <td>-0.514013</td>\n",
       "      <td>0.016753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KDM5D</th>\n",
       "      <td>-0.499488</td>\n",
       "      <td>0.019061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDX3Y</th>\n",
       "      <td>-0.497709</td>\n",
       "      <td>0.022053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMSB4Y</th>\n",
       "      <td>-0.495302</td>\n",
       "      <td>-0.003714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USP9Y</th>\n",
       "      <td>-0.494874</td>\n",
       "      <td>0.022164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTY15</th>\n",
       "      <td>-0.487078</td>\n",
       "      <td>0.014047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCRNA00185</th>\n",
       "      <td>-0.473645</td>\n",
       "      <td>-0.019878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYorf15B</th>\n",
       "      <td>-0.472546</td>\n",
       "      <td>0.019967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZFY</th>\n",
       "      <td>-0.453348</td>\n",
       "      <td>0.018549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPS4Y1</th>\n",
       "      <td>-0.452888</td>\n",
       "      <td>0.016435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NLGN4Y</th>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.013003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAZ1</th>\n",
       "      <td>-0.369805</td>\n",
       "      <td>-0.008085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XIST</th>\n",
       "      <td>0.316281</td>\n",
       "      <td>-0.023227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSIX</th>\n",
       "      <td>0.298300</td>\n",
       "      <td>-0.030515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRKY</th>\n",
       "      <td>-0.294522</td>\n",
       "      <td>0.017516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            encoding 82  encoding 85\n",
       "EIF1AY        -0.543605     0.013644\n",
       "UTY           -0.514367     0.019198\n",
       "CYorf15A      -0.514013     0.016753\n",
       "KDM5D         -0.499488     0.019061\n",
       "DDX3Y         -0.497709     0.022053\n",
       "TMSB4Y        -0.495302    -0.003714\n",
       "USP9Y         -0.494874     0.022164\n",
       "TTTY15        -0.487078     0.014047\n",
       "NCRNA00185    -0.473645    -0.019878\n",
       "CYorf15B      -0.472546     0.019967\n",
       "ZFY           -0.453348     0.018549\n",
       "RPS4Y1        -0.452888     0.016435\n",
       "NLGN4Y        -0.371427    -0.013003\n",
       "DAZ1          -0.369805    -0.008085\n",
       "XIST           0.316281    -0.023227\n",
       "TSIX           0.298300    -0.030515\n",
       "PRKY          -0.294522     0.017516"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 17 genes with high activation in node 82\n",
    "# All genes are located on sex chromosomes\n",
    "sex_node_plot.head(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By measuring expression of only *17 genes*, we can reliably predict the sex of the cancer patient. These are genes most expressed by sex chromosomes including x inactivating genes _XIST_ and _TSIX_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Node separating melanoma samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aXq//zzpPZZq8thuYUOf914CH2mBpHYLWbv7bXD7//HPi4+PrhcsmTJhg1o9Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f03f12ff6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We previously observed metastasis samples being robustly separated by two features\n",
    "# Visualize the feature scores here\n",
    "met_node_plot = weight_layer_df.loc[[53, 66], :].T\n",
    "met_node_plot.columns = ['encoding 53', 'encoding 66']\n",
    "\n",
    "met_node_plot = (met_node_plot.reindex(met_node_plot['encoding 53'].abs()\n",
    "                                       .sort_values(ascending=False).index)\n",
    "                 )\n",
    "g = sns.jointplot(x='encoding 53', y='encoding 66',\n",
    "                  data=met_node_plot, color='black',\n",
    "                  edgecolor=\"w\", stat_func=None);\n",
    "\n",
    "# Save outputs\n",
    "met_node_plot_file = os.path.join('figures', 'skcm_metastasis_node_gene_scatter.pdf')\n",
    "g.savefig(met_node_plot_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output high weight genes for two specific SKCM encodings\n",
    "\n",
    "Because the genes involved in these nodes are not as cut and dry as the sex specific nodes, output the high weight genes explaining each tail of node 53 and 66. These will be processed though a pathway analysis downstream."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_high_weight_genes(weight_df, encoding, filename, thresh=2.5):\n",
    "    \"\"\"\n",
    "    Function to process and output high weight genes given specific feature encodings\n",
    "    \"\"\"\n",
    "    \n",
    "    # Sort initial encoding by absolute activation\n",
    "    encoding_df = (\n",
    "        weight_df\n",
    "        .reindex(weight_df[encoding].abs()\n",
    "                 .sort_values(ascending=False).index)[encoding]\n",
    "    )\n",
    "    \n",
    "    hw_pos_cutoff = encoding_df.mean() + (encoding_df.std() * thresh)\n",
    "    hw_pos_df = pd.DataFrame(encoding_df[encoding_df > hw_pos_cutoff])\n",
    "    hw_pos_df = hw_pos_df.assign(direction='positive')\n",
    "    \n",
    "    hw_neg_cutoff = encoding_df.mean() - (encoding_df.std() * thresh)\n",
    "    hw_neg_df = pd.DataFrame(encoding_df[encoding_df < hw_neg_cutoff])\n",
    "    hw_neg_df = hw_neg_df.assign(direction='negative')\n",
    "\n",
    "    hw_df = pd.concat([hw_pos_df, hw_neg_df])\n",
    "    hw_df.index.name = 'genes'\n",
    "    hw_df.to_csv(filename, sep='\\t')\n",
    "    return hw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encoding 66</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ROPN1</th>\n",
       "      <td>0.115224</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROPN1B</th>\n",
       "      <td>0.108729</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADIPOQ</th>\n",
       "      <td>0.107172</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOX10</th>\n",
       "      <td>0.105733</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIDEA</th>\n",
       "      <td>0.103107</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        encoding 66 direction\n",
       "genes                        \n",
       "ROPN1      0.115224  positive\n",
       "ROPN1B     0.108729  positive\n",
       "ADIPOQ     0.107172  positive\n",
       "SOX10      0.105733  positive\n",
       "CIDEA      0.103107  positive"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding 66\n",
    "hw_node66_file = os.path.join('results', 'high_weight_genes_node66_skcm.tsv')\n",
    "node66_df = output_high_weight_genes(met_node_plot, 'encoding 66', hw_node66_file)\n",
    "node66_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encoding 53</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HCG22</th>\n",
       "      <td>0.084451</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RXRG</th>\n",
       "      <td>0.081329</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGAT4C</th>\n",
       "      <td>0.076966</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLP1</th>\n",
       "      <td>0.076440</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10orf90</th>\n",
       "      <td>0.076005</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          encoding 53 direction\n",
       "genes                          \n",
       "HCG22        0.084451  positive\n",
       "RXRG         0.081329  positive\n",
       "MGAT4C       0.076966  positive\n",
       "PLP1         0.076440  positive\n",
       "C10orf90     0.076005  positive"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding 53\n",
    "hw_node53_file = os.path.join('results', 'high_weight_genes_node53_skcm.tsv')\n",
    "node53_df = output_high_weight_genes(met_node_plot, 'encoding 53', hw_node53_file)\n",
    "node53_df.head(5)"
   ]
  }
 ],
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