tsne_tybalt_features.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Encoded representation layer recapitulates signal identified in raw data\n",
    "\n",
    "## Visualized with t-SNE\n",
    "\n",
    "Perform a t-sne on tybalt features to visualize if the latent layer recapitulates relationships observed through raw data t-sne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn import manifold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load VAE feature activations per sample\n",
    "encoded_file = os.path.join('data', 'encoded_rnaseq_onehidden_warmup_batchnorm.tsv')\n",
    "encoded_df = pd.read_table(encoded_file, index_col=0)\n",
    "encoded_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Load ADAGE feature activations per sample\n",
    "adage_file = os.path.join('data', 'encoded_adage_features.tsv')\n",
    "adage_df = pd.read_table(adage_file, index_col=0)\n",
    "adage_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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       "                   RPS4Y1      XIST      KRT5  AGR2  CEACAM5     KRT6A  \\\n",
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       "\n",
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       "\n",
       "                  C8orf48    CDK5R1    FAM81A  C13orf18     GDPD3     SMAGP  \\\n",
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       "\n",
       "                  C2orf85   POU5F1B     CHST2  \n",
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# Load Zero-One transformed (min-max scaled) RNAseq data\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",
    "print(rnaseq_df.shape)\n",
    "rnaseq_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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      "text/plain": [
       "                         1         2\n",
       "tcga_id                             \n",
       "TCGA-02-0047-01  15.205528 -7.820806\n",
       "TCGA-02-0055-01   9.571207 -6.080736"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Perform t-SNE on VAE encoded_features\n",
    "tsne = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                     learning_rate=300, n_iter=400)\n",
    "tsne_out = tsne.fit_transform(encoded_df)\n",
    "tsne_out = pd.DataFrame(tsne_out, columns=['1', '2'])\n",
    "tsne_out.index = encoded_df.index\n",
    "tsne_out.index.name = 'tcga_id'\n",
    "tsne_out_file = os.path.join('results', 'tybalt_tsne_features.tsv')\n",
    "tsne_out.to_csv(tsne_out_file, sep='\\t')\n",
    "tsne_out.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "                         1         2\n",
       "tcga_id                             \n",
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     "metadata": {},
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    }
   ],
   "source": [
    "# Perform t-SNE on ADAGE encoded_features\n",
    "tsne_adage = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                           learning_rate=300, n_iter=400)\n",
    "tsne_adage_out = tsne_adage.fit_transform(adage_df)\n",
    "tsne_adage_out = pd.DataFrame(tsne_adage_out, columns=['1', '2'])\n",
    "tsne_adage_out.index = adage_df.index\n",
    "tsne_adage_out.index.name = 'tcga_id'\n",
    "tsne_adage_out_file = os.path.join('results', 'adage_tsne_features.tsv')\n",
    "tsne_adage_out.to_csv(tsne_adage_out_file, sep='\\t')\n",
    "tsne_adage_out.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcga_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-02-0047-01</th>\n",
       "      <td>12.082565</td>\n",
       "      <td>-4.386090</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-02-0055-01</th>\n",
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      "text/plain": [
       "                         1         2\n",
       "tcga_id                             \n",
       "TCGA-02-0047-01  12.082565 -4.386090\n",
       "TCGA-02-0055-01   8.941544 -3.303283"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform t-SNE on zero-one RNAseq features\n",
    "tsne_rna = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                         learning_rate=300, n_iter=400)\n",
    "tsne_rna_out = tsne_rna.fit_transform(rnaseq_df)\n",
    "tsne_rna_out = pd.DataFrame(tsne_rna_out, columns=['1', '2'])\n",
    "tsne_rna_out.index = rnaseq_df.index\n",
    "tsne_rna_out.index.name = 'tcga_id'\n",
    "tsne_rna_out_file = os.path.join('results', 'rnaseq_tsne_features.tsv')\n",
    "tsne_rna_out.to_csv(tsne_rna_out_file, sep='\\t')\n",
    "tsne_rna_out.head(2)"
   ]
  }
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