tsne_tybalt_features.ipynb

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    "# 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": {
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   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn import manifold"
   ]
  },
  {
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   "execution_count": 2,
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     "execution_count": 2,
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   "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)"
   ]
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       "                   RPS4Y1      XIST      KRT5  AGR2  CEACAM5     KRT6A  \\\n",
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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": 4,
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       "                         1         2\n",
       "tcga_id                             \n",
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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": 5,
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      "text/plain": [
       "                         1         2\n",
       "tcga_id                             \n",
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   ],
   "source": [
    "# Perform t-SNE on zero-one RNAseq 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(rnaseq_df)\n",
    "tsne_out = pd.DataFrame(tsne_out, columns=['1', '2'])\n",
    "tsne_out.index = rnaseq_df.index\n",
    "tsne_out.index.name = 'tcga_id'\n",
    "tsne_out_file = os.path.join('results', 'rnaseq_tsne_features.tsv')\n",
    "tsne_out.to_csv(tsne_out_file, sep='\\t')\n",
    "tsne_out.head(2)"
   ]
  }
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