AdiposeHeatmap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: grid\n",
      "========================================\n",
      "ComplexHeatmap version 1.18.1\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "========================================\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.4\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: http://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization \n",
      "  in R. Bioinformatics 2014.\n",
      "========================================\n",
      "\n",
      "\n",
      "Attaching package: 'gplots'\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    lowess\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# R code to plot heatmap (top ten radiomics features)\n",
    "library(\"openxlsx\")\n",
    "library(\"stringr\")\n",
    "library(ComplexHeatmap)\n",
    "library(circlize)\n",
    "library(colorspace)\n",
    "library(GetoptLong)\n",
    "library(ggplot2)\n",
    "library(gplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(\"RColorBrewer\")\n",
    "# install.packages('OpasnetUtils')\n",
    "library(reticulate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"./Data/\"\n",
    "output_path = \"./Results/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load features, metabolic outcomes and corresponding clinical factors\n",
    "xlfeatures = read.xlsx(paste(data_path , \"all_major_block_features.xlsx\",sep = \"\"), sheet = 1, colNames = TRUE)\n",
    "\n",
    "xlfeatures = data.frame(xlfeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'ID'</li>\n",
       "\t<li>'GLCM_Imc2'</li>\n",
       "\t<li>'GLRLM_RunLengthNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelVariance'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glrlm_RunLengthNonUniformity'</li>\n",
       "\t<li>'WAVELET_HHH_glrlm_RunEntropy'</li>\n",
       "\t<li>'Age'</li>\n",
       "\t<li>'Gender'</li>\n",
       "\t<li>'VFat'</li>\n",
       "\t<li>'SFat'</li>\n",
       "\t<li>'IsMS'</li>\n",
       "\t<li>'IsCO'</li>\n",
       "\t<li>'IsVO'</li>\n",
       "\t<li>'IsCTVO'</li>\n",
       "\t<li>'IsInsulinResis'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'ID'\n",
       "\\item 'GLCM\\_Imc2'\n",
       "\\item 'GLRLM\\_RunLengthNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelVariance'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glrlm\\_RunLengthNonUniformity'\n",
       "\\item 'WAVELET\\_HHH\\_glrlm\\_RunEntropy'\n",
       "\\item 'Age'\n",
       "\\item 'Gender'\n",
       "\\item 'VFat'\n",
       "\\item 'SFat'\n",
       "\\item 'IsMS'\n",
       "\\item 'IsCO'\n",
       "\\item 'IsVO'\n",
       "\\item 'IsCTVO'\n",
       "\\item 'IsInsulinResis'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ID'\n",
       "2. 'GLCM_Imc2'\n",
       "3. 'GLRLM_RunLengthNonUniformity'\n",
       "4. 'GLSZM_GrayLevelNonUniformity'\n",
       "5. 'GLSZM_GrayLevelVariance'\n",
       "6. 'WAVELET_LLL_glcm_Idm'\n",
       "7. 'WAVELET_LLL_glcm_Id'\n",
       "8. 'WAVELET_LLH_glcm_Idm'\n",
       "9. 'WAVELET_LLH_glcm_Id'\n",
       "10. 'WAVELET_LLH_glrlm_RunLengthNonUniformity'\n",
       "11. 'WAVELET_HHH_glrlm_RunEntropy'\n",
       "12. 'Age'\n",
       "13. 'Gender'\n",
       "14. 'VFat'\n",
       "15. 'SFat'\n",
       "16. 'IsMS'\n",
       "17. 'IsCO'\n",
       "18. 'IsVO'\n",
       "19. 'IsCTVO'\n",
       "20. 'IsInsulinResis'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ID\"                                      \n",
       " [2] \"GLCM_Imc2\"                               \n",
       " [3] \"GLRLM_RunLengthNonUniformity\"            \n",
       " [4] \"GLSZM_GrayLevelNonUniformity\"            \n",
       " [5] \"GLSZM_GrayLevelVariance\"                 \n",
       " [6] \"WAVELET_LLL_glcm_Idm\"                    \n",
       " [7] \"WAVELET_LLL_glcm_Id\"                     \n",
       " [8] \"WAVELET_LLH_glcm_Idm\"                    \n",
       " [9] \"WAVELET_LLH_glcm_Id\"                     \n",
       "[10] \"WAVELET_LLH_glrlm_RunLengthNonUniformity\"\n",
       "[11] \"WAVELET_HHH_glrlm_RunEntropy\"            \n",
       "[12] \"Age\"                                     \n",
       "[13] \"Gender\"                                  \n",
       "[14] \"VFat\"                                    \n",
       "[15] \"SFat\"                                    \n",
       "[16] \"IsMS\"                                    \n",
       "[17] \"IsCO\"                                    \n",
       "[18] \"IsVO\"                                    \n",
       "[19] \"IsCTVO\"                                  \n",
       "[20] \"IsInsulinResis\"                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(xlfeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'X1'</li>\n",
       "\t<li>'GLCM_Imc2'</li>\n",
       "\t<li>'GLRLM_RunLengthNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelVariance'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glrlm_RunLengthNonUniformity'</li>\n",
       "\t<li>'WAVELET_HHH_glrlm_RunEntropy'</li>\n",
       "\t<li>'Age'</li>\n",
       "\t<li>'Gender'</li>\n",
       "\t<li>'VFat'</li>\n",
       "\t<li>'SFat'</li>\n",
       "\t<li>'IsMS'</li>\n",
       "\t<li>'IsCO'</li>\n",
       "\t<li>'IsVO'</li>\n",
       "\t<li>'IsIR'</li>\n",
       "\t<li>'IsCTVO'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'X1'\n",
       "\\item 'GLCM\\_Imc2'\n",
       "\\item 'GLRLM\\_RunLengthNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelVariance'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glrlm\\_RunLengthNonUniformity'\n",
       "\\item 'WAVELET\\_HHH\\_glrlm\\_RunEntropy'\n",
       "\\item 'Age'\n",
       "\\item 'Gender'\n",
       "\\item 'VFat'\n",
       "\\item 'SFat'\n",
       "\\item 'IsMS'\n",
       "\\item 'IsCO'\n",
       "\\item 'IsVO'\n",
       "\\item 'IsIR'\n",
       "\\item 'IsCTVO'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'X1'\n",
       "2. 'GLCM_Imc2'\n",
       "3. 'GLRLM_RunLengthNonUniformity'\n",
       "4. 'GLSZM_GrayLevelNonUniformity'\n",
       "5. 'GLSZM_GrayLevelVariance'\n",
       "6. 'WAVELET_LLL_glcm_Idm'\n",
       "7. 'WAVELET_LLL_glcm_Id'\n",
       "8. 'WAVELET_LLH_glcm_Idm'\n",
       "9. 'WAVELET_LLH_glcm_Id'\n",
       "10. 'WAVELET_LLH_glrlm_RunLengthNonUniformity'\n",
       "11. 'WAVELET_HHH_glrlm_RunEntropy'\n",
       "12. 'Age'\n",
       "13. 'Gender'\n",
       "14. 'VFat'\n",
       "15. 'SFat'\n",
       "16. 'IsMS'\n",
       "17. 'IsCO'\n",
       "18. 'IsVO'\n",
       "19. 'IsIR'\n",
       "20. 'IsCTVO'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"X1\"                                      \n",
       " [2] \"GLCM_Imc2\"                               \n",
       " [3] \"GLRLM_RunLengthNonUniformity\"            \n",
       " [4] \"GLSZM_GrayLevelNonUniformity\"            \n",
       " [5] \"GLSZM_GrayLevelVariance\"                 \n",
       " [6] \"WAVELET_LLL_glcm_Idm\"                    \n",
       " [7] \"WAVELET_LLL_glcm_Id\"                     \n",
       " [8] \"WAVELET_LLH_glcm_Idm\"                    \n",
       " [9] \"WAVELET_LLH_glcm_Id\"                     \n",
       "[10] \"WAVELET_LLH_glrlm_RunLengthNonUniformity\"\n",
       "[11] \"WAVELET_HHH_glrlm_RunEntropy\"            \n",
       "[12] \"Age\"                                     \n",
       "[13] \"Gender\"                                  \n",
       "[14] \"VFat\"                                    \n",
       "[15] \"SFat\"                                    \n",
       "[16] \"IsMS\"                                    \n",
       "[17] \"IsCO\"                                    \n",
       "[18] \"IsVO\"                                    \n",
       "[19] \"IsIR\"                                    \n",
       "[20] \"IsCTVO\"                                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(xlfeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = xlfeatures[,2:length(colnames(xlfeatures))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fat volumes (ml), age\n",
    "features$VFat <- features$VFat*1000\n",
    "features$SFat <- features$SFat*1000\n",
    "features$Age <- features$Age *10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "imagefeatures = features[, 1:(length(colnames(features))-9)]\n",
    "imagefeatures = scale(imagefeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "features[, 1:(length(colnames(features))-9)] = imagefeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features[c(colnames(features)[1:14], 'IsCTVO', 'IsIR','IsMS')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'GLCM_Imc2'</li>\n",
       "\t<li>'GLRLM_RunLengthNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelNonUniformity'</li>\n",
       "\t<li>'GLSZM_GrayLevelVariance'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLL_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Idm'</li>\n",
       "\t<li>'WAVELET_LLH_glcm_Id'</li>\n",
       "\t<li>'WAVELET_LLH_glrlm_RunLengthNonUniformity'</li>\n",
       "\t<li>'WAVELET_HHH_glrlm_RunEntropy'</li>\n",
       "\t<li>'Age'</li>\n",
       "\t<li>'Gender'</li>\n",
       "\t<li>'VFat'</li>\n",
       "\t<li>'SFat'</li>\n",
       "\t<li>'IsCTVO'</li>\n",
       "\t<li>'IsIR'</li>\n",
       "\t<li>'IsMS'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'GLCM\\_Imc2'\n",
       "\\item 'GLRLM\\_RunLengthNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelNonUniformity'\n",
       "\\item 'GLSZM\\_GrayLevelVariance'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLL\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Idm'\n",
       "\\item 'WAVELET\\_LLH\\_glcm\\_Id'\n",
       "\\item 'WAVELET\\_LLH\\_glrlm\\_RunLengthNonUniformity'\n",
       "\\item 'WAVELET\\_HHH\\_glrlm\\_RunEntropy'\n",
       "\\item 'Age'\n",
       "\\item 'Gender'\n",
       "\\item 'VFat'\n",
       "\\item 'SFat'\n",
       "\\item 'IsCTVO'\n",
       "\\item 'IsIR'\n",
       "\\item 'IsMS'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'GLCM_Imc2'\n",
       "2. 'GLRLM_RunLengthNonUniformity'\n",
       "3. 'GLSZM_GrayLevelNonUniformity'\n",
       "4. 'GLSZM_GrayLevelVariance'\n",
       "5. 'WAVELET_LLL_glcm_Idm'\n",
       "6. 'WAVELET_LLL_glcm_Id'\n",
       "7. 'WAVELET_LLH_glcm_Idm'\n",
       "8. 'WAVELET_LLH_glcm_Id'\n",
       "9. 'WAVELET_LLH_glrlm_RunLengthNonUniformity'\n",
       "10. 'WAVELET_HHH_glrlm_RunEntropy'\n",
       "11. 'Age'\n",
       "12. 'Gender'\n",
       "13. 'VFat'\n",
       "14. 'SFat'\n",
       "15. 'IsCTVO'\n",
       "16. 'IsIR'\n",
       "17. 'IsMS'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"GLCM_Imc2\"                               \n",
       " [2] \"GLRLM_RunLengthNonUniformity\"            \n",
       " [3] \"GLSZM_GrayLevelNonUniformity\"            \n",
       " [4] \"GLSZM_GrayLevelVariance\"                 \n",
       " [5] \"WAVELET_LLL_glcm_Idm\"                    \n",
       " [6] \"WAVELET_LLL_glcm_Id\"                     \n",
       " [7] \"WAVELET_LLH_glcm_Idm\"                    \n",
       " [8] \"WAVELET_LLH_glcm_Id\"                     \n",
       " [9] \"WAVELET_LLH_glrlm_RunLengthNonUniformity\"\n",
       "[10] \"WAVELET_HHH_glrlm_RunEntropy\"            \n",
       "[11] \"Age\"                                     \n",
       "[12] \"Gender\"                                  \n",
       "[13] \"VFat\"                                    \n",
       "[14] \"SFat\"                                    \n",
       "[15] \"IsCTVO\"                                  \n",
       "[16] \"IsIR\"                                    \n",
       "[17] \"IsMS\"                                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gOloccOdgQ7oErlDDrWlpw30BJb/5m1xXFjLftR2dXm3O6+S/J0vLmUd+ey4M4x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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap\n",
    "ha1 = HeatmapAnnotation(df = features[,11:17],\n",
    "    col = list(IsCTVO = c(\"1\"=\"Orange\",\"0\"=\"blue\"), IsIR = c(\"1\"=\"purple\",\"0\"=\"darkgreen\"), IsMS = c(\"1\"=\"Brown\",\"0\"=\"green\"), Gender = c(\"1\"= \"pink\", \"0\"= \"darkgray\")\n",
    "    ),\n",
    "                       \n",
    "     annotation_legend_param = list(\n",
    "               IsCTVO = list(title = \"Visceral Obesity (CT)\", at = c(\"1\", \"0\"), labels = c(\"Yes\", \"No\")),\n",
    "               IsIR = list(title = \"Insulin Resistance\", at = c(\"1\", \"0\"), labels = c(\"Yes\", \"No\")),\n",
    "         VFat = list(title = \"Visceral Fat\"),\n",
    "         SFat = list(title = \"Subcutaneous Fat\"),\n",
    "      IsMS = list(title = \"Metabolic Syndrome\", at = c(\"1\", \"0\"), labels = c(\"Yes\", \"No\")),\n",
    "       Gender = list(title = \"Gender\", at = c(\"1\", \"0\"), labels = c(\"Female\", \"Male\"))\n",
    "      )\n",
    "   )\n",
    "\n",
    "ht = Heatmap(t(features[,1:10]), name = \"Z-Score\",row_names_gp = gpar(fontsize = 8),\n",
    "        show_row_dend = FALSE,show_column_dend = FALSE, cluster_rows = TRUE,\n",
    "        cluster_columns = TRUE, show_row_names = TRUE, show_column_names = FALSE,clustering_distance_columns = \"pearson\",\n",
    "        show_heatmap_legend = TRUE,\n",
    "       ,top_annotation=ha1)\n",
    "\n",
    "p = draw(ht, heatmap_legend_side = \"right\")\n",
    "\n",
    "decorate_annotation(\"IsIR\", {grid.text(\"Insulin Resistance\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"IsCTVO\", {grid.text(\"Visceral Obesity (CT)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"IsMS\", {grid.text(\"Metabolic Syndrome\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"Age\", {grid.text(\"Age\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"Gender\", {grid.text(\"Gender\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"SFat\", {grid.text(\"Subcutaneous Fat(ml)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"VFat\", {grid.text(\"Viceral Fat(ml)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "title = paste(output_path , \"inner_block_for_heatmap.tif\",sep = \"\")\n",
    "tiff(title, width=900*3, height=500*3, units=\"px\", res=96*3, compression = \"lzw\")\n",
    "p = draw(ht, heatmap_legend_side = \"right\")\n",
    "decorate_annotation(\"IsIR\", {grid.text(\"Insulin Resistance\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"IsCTVO\", {grid.text(\"Visceral Obesity (CT)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"IsMS\", {grid.text(\"Metabolic Syndrome\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"Age\", {grid.text(\"Age\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"Gender\", {grid.text(\"Gender\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"SFat\", {grid.text(\"Subcutaneous Fat(ml)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "decorate_annotation(\"VFat\", {grid.text(\"Viceral Fat(ml)\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n",
    "\n",
    "dev.off()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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