latent_distances.ipynb

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   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import pandas as pd\n",
    "from scipy.stats import spearmanr\n",
    "import os\n",
    "import numpy as np"
   ]
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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "df = pd.read_csv(\"datasets/gtex10/mainTable.csv\", index_col=0)\n",
    "df_files = pd.read_csv(\"datasets/gtex10/files.dat\").set_index(\"SAMPID\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
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   "source": [
    "def get_corr(df):\n",
    "    return df.join(df_files[\"SMTS\"]).groupby(\"SMTS\").mean().transpose().corr()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics = [\n",
    "    pd.read_csv(\"datasets/gtex10/topsbm/topsbm_level_2_topic-dist.csv\").drop([\"i_doc\"], axis=1).set_index(\"doc\"),\n",
    "    pd.read_csv(\"datasets/gtex10/lda/lda_level_2_topic-dist.csv\").drop([\"i_doc\"], axis=1).set_index(\"doc\"),\n",
    "    pd.read_csv(\"datasets/gtex10/tm/tm_level_0_topic-dist.csv\").drop([\"i_doc\"], axis=1).set_index(\"doc\"),\n",
    "    pd.read_csv(\"datasets/gtex10/wgcna/wgcna_level_0_topic-dist.csv\").drop([\"i_doc\"], axis=1).set_index(\"doc\"),\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
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   "source": [
    "corr_data = get_corr(df.transpose())\n",
    "corr_top = list(map(get_corr, df_topics))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 64,
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   "source": [
    "tissues = corr_data.columns\n",
    "spearmans = []\n",
    "for tissue in tissues:\n",
    "    s = [spearmanr(corr_data[tissue].reindex(index=tissues), ctop[tissue].reindex(index=tissues))[0] for ctop in corr_top]\n",
    "    spearmans.append(s)"
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    "fig = make_subplots(2,2)\n",
    "ls={\"width\":5, \"dash\":\"dash\", \"color\":\"gray\"}\n",
    "fig.add_trace(go.Bar(y=[s[0] for s in spearmans], name=\"hSBM\"), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=[0, len(tissues)-1], y=np.repeat(np.mean([s[0] for s in spearmans]),2), mode=\"lines\", line=ls, name=\"mean\"), row=1, col=1)\n",
    "fig.add_trace(go.Bar(y=[s[1] for s in spearmans], name=\"LDA\"), row=1, col=2)\n",
    "fig.add_trace(go.Scatter(x=[0, len(tissues)-1], y=np.repeat(np.mean([s[1] for s in spearmans]),2), mode=\"lines\", line=ls, name=\"mean\"), row=1, col=2)\n",
    "fig.add_trace(go.Bar(y=[s[2] for s in spearmans], name=\"TM\"), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(x=[0, len(tissues)-1], y=np.repeat(np.mean([s[2] for s in spearmans]),2), mode=\"lines\", line=ls, name=\"mean\"), row=2, col=1)\n",
    "fig.add_trace(go.Bar(y=[s[3] for s in spearmans], name=\"WGCNA\"), row=2, col=2)\n",
    "fig.add_trace(go.Scatter(x=[0, len(tissues)-1], y=np.repeat(np.mean([s[3] for s in spearmans]),2), mode=\"lines\", line=ls, name=\"mean\"), row=2, col=2)\n",
    "\n",
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    "fig.update_xaxes({\"tickmode\":\"array\", \"tickvals\":list(range(len(tissues))), \"ticktext\": tissues, \"tickangle\":45,\"tickfont\":{\"size\":18}})\n",
    "\n",
    "fig.update_layout(width=1000, height=900)\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"spearman.pdf\")"
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