notebooks/25_qusage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Dependenices\" data-toc-modified-id=\"Dependenices-1\">Dependenices</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#Run-qusage\" data-toc-modified-id=\"Run-qusage-5\">Run qusage</a></span></li><li><span><a href=\"#Load-pathways\" data-toc-modified-id=\"Load-pathways-6\">Load pathways</a></span></li><li><span><a href=\"#Validate-with-proteomics\" data-toc-modified-id=\"Validate-with-proteomics-7\">Validate with proteomics</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependenices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: limma\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(gridExtra)\n",
    "library(grid)\n",
    "library(qusage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/color_schemes.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/plotting_functions.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/Heatmap_functions.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "hallmark_IC_stats_ridge <- function(proteinmat, metadata, hallmark){\n",
    "    \n",
    "    protein_mat_pathway <- as.matrix(proteinmat[rownames(proteinmat) %in% Hs.H[[hallmark]], rownames(metadata)])\n",
    "    print(dim(protein_mat_pathway))\n",
    "    pathway_mean <- colMeans(protein_mat_pathway)\n",
    "    \n",
    "    metadata$pathway_mean <- pathway_mean\n",
    "    \n",
    "    print(pairwise.t.test(metadata$pathway_mean, metadata$immune_cluster, p.adjust = \"bonferroni\"))\n",
    "\n",
    "    ridge_plot <- plot_ridge.fx(metadata, \"pathway_mean\")\n",
    "    return(ridge_plot + labs(title = hallmark))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hs.H <- read.gmt(paste0(datapath, \"DEG/h.all.v7.1.symbols.gmt\"))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(paste0(datapath,\"exp_mat/tpm_matrix_ped_final.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                90                279                393                163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC$immune_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpm_matrix_ped_final <- tpm_matrix_ped_final[,rownames(metadata_IC)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TARGET-30-PAIFXV-01A</th><th scope=col>TARGET-30-PAIPGU-01A</th><th scope=col>TARGET-30-PAISNS-01A</th><th scope=col>TARGET-30-PAITCI-01A</th><th scope=col>TARGET-30-PAIVHE-01A</th><th scope=col>TARGET-30-PAIXIF-01A</th><th scope=col>TARGET-30-PAKYZS-01A</th><th scope=col>TARGET-30-PAKZRH-01A</th><th scope=col>TARGET-30-PALBFW-01A</th><th scope=col>TARGET-30-PALCBW-01A</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TSPAN6</th><td>11.57</td><td> 9.14</td><td>31.55</td><td>14.52</td><td>66.42</td><td>19.37</td><td>12.51</td><td>18.51</td><td>16.63</td><td> 21.17</td></tr>\n",
       "\t<tr><th scope=row>TNMD</th><td> 0.10</td><td> 0.08</td><td> 0.00</td><td> 0.00</td><td> 0.00</td><td> 0.00</td><td> 0.00</td><td> 0.17</td><td> 0.17</td><td>  0.19</td></tr>\n",
       "\t<tr><th scope=row>DPM1</th><td>22.48</td><td>31.96</td><td>44.65</td><td>87.72</td><td>36.07</td><td>23.47</td><td>27.27</td><td>43.03</td><td>32.10</td><td> 24.11</td></tr>\n",
       "\t<tr><th scope=row>SCYL3</th><td> 2.37</td><td> 1.78</td><td> 3.77</td><td> 2.49</td><td> 4.91</td><td> 3.00</td><td> 3.22</td><td> 3.84</td><td> 5.51</td><td>  5.68</td></tr>\n",
       "\t<tr><th scope=row>C1orf112</th><td> 1.46</td><td> 3.12</td><td> 8.13</td><td> 3.70</td><td> 6.83</td><td> 5.12</td><td> 2.21</td><td> 3.95</td><td> 3.18</td><td>  2.43</td></tr>\n",
       "\t<tr><th scope=row>FGR</th><td> 2.05</td><td> 4.70</td><td> 2.94</td><td> 0.84</td><td> 6.55</td><td> 2.64</td><td> 2.08</td><td> 1.50</td><td> 3.72</td><td>  6.42</td></tr>\n",
       "\t<tr><th scope=row>CFH</th><td> 9.79</td><td> 8.69</td><td>20.68</td><td>23.68</td><td>36.96</td><td>57.95</td><td>26.22</td><td>10.43</td><td>31.97</td><td>103.99</td></tr>\n",
       "\t<tr><th scope=row>FUCA2</th><td>14.27</td><td>11.80</td><td> 9.61</td><td>13.57</td><td>18.67</td><td>22.23</td><td>23.67</td><td>22.98</td><td>16.85</td><td> 42.18</td></tr>\n",
       "\t<tr><th scope=row>GCLC</th><td> 5.77</td><td> 4.42</td><td> 4.23</td><td> 6.69</td><td>13.15</td><td> 2.52</td><td> 9.76</td><td> 6.68</td><td>14.60</td><td> 10.05</td></tr>\n",
       "\t<tr><th scope=row>NFYA</th><td> 4.77</td><td> 8.20</td><td>13.73</td><td> 6.40</td><td> 9.47</td><td> 4.80</td><td> 4.94</td><td> 6.16</td><td> 8.01</td><td>  9.42</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & TARGET-30-PAIFXV-01A & TARGET-30-PAIPGU-01A & TARGET-30-PAISNS-01A & TARGET-30-PAITCI-01A & TARGET-30-PAIVHE-01A & TARGET-30-PAIXIF-01A & TARGET-30-PAKYZS-01A & TARGET-30-PAKZRH-01A & TARGET-30-PALBFW-01A & TARGET-30-PALCBW-01A\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTSPAN6 & 11.57 &  9.14 & 31.55 & 14.52 & 66.42 & 19.37 & 12.51 & 18.51 & 16.63 &  21.17\\\\\n",
       "\tTNMD &  0.10 &  0.08 &  0.00 &  0.00 &  0.00 &  0.00 &  0.00 &  0.17 &  0.17 &   0.19\\\\\n",
       "\tDPM1 & 22.48 & 31.96 & 44.65 & 87.72 & 36.07 & 23.47 & 27.27 & 43.03 & 32.10 &  24.11\\\\\n",
       "\tSCYL3 &  2.37 &  1.78 &  3.77 &  2.49 &  4.91 &  3.00 &  3.22 &  3.84 &  5.51 &   5.68\\\\\n",
       "\tC1orf112 &  1.46 &  3.12 &  8.13 &  3.70 &  6.83 &  5.12 &  2.21 &  3.95 &  3.18 &   2.43\\\\\n",
       "\tFGR &  2.05 &  4.70 &  2.94 &  0.84 &  6.55 &  2.64 &  2.08 &  1.50 &  3.72 &   6.42\\\\\n",
       "\tCFH &  9.79 &  8.69 & 20.68 & 23.68 & 36.96 & 57.95 & 26.22 & 10.43 & 31.97 & 103.99\\\\\n",
       "\tFUCA2 & 14.27 & 11.80 &  9.61 & 13.57 & 18.67 & 22.23 & 23.67 & 22.98 & 16.85 &  42.18\\\\\n",
       "\tGCLC &  5.77 &  4.42 &  4.23 &  6.69 & 13.15 &  2.52 &  9.76 &  6.68 & 14.60 &  10.05\\\\\n",
       "\tNFYA &  4.77 &  8.20 & 13.73 &  6.40 &  9.47 &  4.80 &  4.94 &  6.16 &  8.01 &   9.42\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | TARGET-30-PAIFXV-01A &lt;dbl&gt; | TARGET-30-PAIPGU-01A &lt;dbl&gt; | TARGET-30-PAISNS-01A &lt;dbl&gt; | TARGET-30-PAITCI-01A &lt;dbl&gt; | TARGET-30-PAIVHE-01A &lt;dbl&gt; | TARGET-30-PAIXIF-01A &lt;dbl&gt; | TARGET-30-PAKYZS-01A &lt;dbl&gt; | TARGET-30-PAKZRH-01A &lt;dbl&gt; | TARGET-30-PALBFW-01A &lt;dbl&gt; | TARGET-30-PALCBW-01A &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TSPAN6 | 11.57 |  9.14 | 31.55 | 14.52 | 66.42 | 19.37 | 12.51 | 18.51 | 16.63 |  21.17 |\n",
       "| TNMD |  0.10 |  0.08 |  0.00 |  0.00 |  0.00 |  0.00 |  0.00 |  0.17 |  0.17 |   0.19 |\n",
       "| DPM1 | 22.48 | 31.96 | 44.65 | 87.72 | 36.07 | 23.47 | 27.27 | 43.03 | 32.10 |  24.11 |\n",
       "| SCYL3 |  2.37 |  1.78 |  3.77 |  2.49 |  4.91 |  3.00 |  3.22 |  3.84 |  5.51 |   5.68 |\n",
       "| C1orf112 |  1.46 |  3.12 |  8.13 |  3.70 |  6.83 |  5.12 |  2.21 |  3.95 |  3.18 |   2.43 |\n",
       "| FGR |  2.05 |  4.70 |  2.94 |  0.84 |  6.55 |  2.64 |  2.08 |  1.50 |  3.72 |   6.42 |\n",
       "| CFH |  9.79 |  8.69 | 20.68 | 23.68 | 36.96 | 57.95 | 26.22 | 10.43 | 31.97 | 103.99 |\n",
       "| FUCA2 | 14.27 | 11.80 |  9.61 | 13.57 | 18.67 | 22.23 | 23.67 | 22.98 | 16.85 |  42.18 |\n",
       "| GCLC |  5.77 |  4.42 |  4.23 |  6.69 | 13.15 |  2.52 |  9.76 |  6.68 | 14.60 |  10.05 |\n",
       "| NFYA |  4.77 |  8.20 | 13.73 |  6.40 |  9.47 |  4.80 |  4.94 |  6.16 |  8.01 |   9.42 |\n",
       "\n"
      ],
      "text/plain": [
       "         TARGET-30-PAIFXV-01A TARGET-30-PAIPGU-01A TARGET-30-PAISNS-01A\n",
       "TSPAN6   11.57                 9.14                31.55               \n",
       "TNMD      0.10                 0.08                 0.00               \n",
       "DPM1     22.48                31.96                44.65               \n",
       "SCYL3     2.37                 1.78                 3.77               \n",
       "C1orf112  1.46                 3.12                 8.13               \n",
       "FGR       2.05                 4.70                 2.94               \n",
       "CFH       9.79                 8.69                20.68               \n",
       "FUCA2    14.27                11.80                 9.61               \n",
       "GCLC      5.77                 4.42                 4.23               \n",
       "NFYA      4.77                 8.20                13.73               \n",
       "         TARGET-30-PAITCI-01A TARGET-30-PAIVHE-01A TARGET-30-PAIXIF-01A\n",
       "TSPAN6   14.52                66.42                19.37               \n",
       "TNMD      0.00                 0.00                 0.00               \n",
       "DPM1     87.72                36.07                23.47               \n",
       "SCYL3     2.49                 4.91                 3.00               \n",
       "C1orf112  3.70                 6.83                 5.12               \n",
       "FGR       0.84                 6.55                 2.64               \n",
       "CFH      23.68                36.96                57.95               \n",
       "FUCA2    13.57                18.67                22.23               \n",
       "GCLC      6.69                13.15                 2.52               \n",
       "NFYA      6.40                 9.47                 4.80               \n",
       "         TARGET-30-PAKYZS-01A TARGET-30-PAKZRH-01A TARGET-30-PALBFW-01A\n",
       "TSPAN6   12.51                18.51                16.63               \n",
       "TNMD      0.00                 0.17                 0.17               \n",
       "DPM1     27.27                43.03                32.10               \n",
       "SCYL3     3.22                 3.84                 5.51               \n",
       "C1orf112  2.21                 3.95                 3.18               \n",
       "FGR       2.08                 1.50                 3.72               \n",
       "CFH      26.22                10.43                31.97               \n",
       "FUCA2    23.67                22.98                16.85               \n",
       "GCLC      9.76                 6.68                14.60               \n",
       "NFYA      4.94                 6.16                 8.01               \n",
       "         TARGET-30-PALCBW-01A\n",
       "TSPAN6    21.17              \n",
       "TNMD       0.19              \n",
       "DPM1      24.11              \n",
       "SCYL3      5.68              \n",
       "C1orf112   2.43              \n",
       "FGR        6.42              \n",
       "CFH      103.99              \n",
       "FUCA2     42.18              \n",
       "GCLC      10.05              \n",
       "NFYA       9.42              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpm_matrix_ped_final[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log2 for qusage\n",
    "tpm_matrix_ped_final_log <- log2(tpm_matrix_ped_final + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run qusage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_pathwaytables <- parallel::mclapply(unique(metadata_IC$immune_cluster), function(x){\n",
    "    \n",
    "    mycluster <- x\n",
    "\n",
    "    message(mycluster)\n",
    "    \n",
    "    tmp <- metadata_IC\n",
    "    tmp$class <- NA\n",
    "    tmp$class[ tmp$immune_cluster == mycluster] <- mycluster\n",
    "    tmp$class[ tmp$immune_cluster != mycluster] <- \"Others\" \n",
    "\n",
    "    tmp$class <- factor(tmp$class)\n",
    "    \n",
    "    mycontrast <- paste0(mycluster, \"-Others\")\n",
    "    \n",
    "    myresults <- qgen(tpm_matrix_ped_final_log,\n",
    "                  design = tmp[,c(\"sample_id\", \"cohort\", \"class\", \"group\")],\n",
    "                  geneSets = Hs.H,\n",
    "                  fixed= ~ group + cohort + class, #control for batch and cohort\n",
    "                  contrast.factor = ~ class,\n",
    "                  contrast= mycontrast,\n",
    "                  design.sampleid= \"sample_id\")\n",
    "    \n",
    "    mytable <- qsTable(myresults, number = 50)\n",
    "    mytable$contrast <- mycontrast    \n",
    "    \n",
    "    return(mytable)\n",
    "    \n",
    "}, mc.cores = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathwaytable <- do.call(\"rbind\", list_pathwaytables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(pathwaytable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(pathwaytable, file = paste0(datapath, \"DEG/pathwaytable.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"DEG/pathwaytable.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>pathway.name</th><th scope=col>log.fold.change</th><th scope=col>p.Value</th><th scope=col>FDR</th><th scope=col>contrast</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>31</th><td>HALLMARK_INFLAMMATORY_RESPONSE    </td><td>0.2256761</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "\t<tr><th scope=row>46</th><td>HALLMARK_ALLOGRAFT_REJECTION      </td><td>0.2392878</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>HALLMARK_IL6_JAK_STAT3_SIGNALING  </td><td>0.2702564</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>HALLMARK_TNFA_SIGNALING_VIA_NFKB  </td><td>0.2755332</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "\t<tr><th scope=row>19</th><td>HALLMARK_INTERFERON_GAMMA_RESPONSE</td><td>0.2963696</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>HALLMARK_INTERFERON_ALPHA_RESPONSE</td><td>0.3175695</td><td>0</td><td>0</td><td>C2-Others</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & pathway.name & log.fold.change & p.Value & FDR & contrast\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t31 & HALLMARK\\_INFLAMMATORY\\_RESPONSE     & 0.2256761 & 0 & 0 & C2-Others\\\\\n",
       "\t46 & HALLMARK\\_ALLOGRAFT\\_REJECTION       & 0.2392878 & 0 & 0 & C2-Others\\\\\n",
       "\t7 & HALLMARK\\_IL6\\_JAK\\_STAT3\\_SIGNALING   & 0.2702564 & 0 & 0 & C2-Others\\\\\n",
       "\t1 & HALLMARK\\_TNFA\\_SIGNALING\\_VIA\\_NFKB   & 0.2755332 & 0 & 0 & C2-Others\\\\\n",
       "\t19 & HALLMARK\\_INTERFERON\\_GAMMA\\_RESPONSE & 0.2963696 & 0 & 0 & C2-Others\\\\\n",
       "\t18 & HALLMARK\\_INTERFERON\\_ALPHA\\_RESPONSE & 0.3175695 & 0 & 0 & C2-Others\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | pathway.name &lt;chr&gt; | log.fold.change &lt;dbl&gt; | p.Value &lt;dbl&gt; | FDR &lt;dbl&gt; | contrast &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 31 | HALLMARK_INFLAMMATORY_RESPONSE     | 0.2256761 | 0 | 0 | C2-Others |\n",
       "| 46 | HALLMARK_ALLOGRAFT_REJECTION       | 0.2392878 | 0 | 0 | C2-Others |\n",
       "| 7 | HALLMARK_IL6_JAK_STAT3_SIGNALING   | 0.2702564 | 0 | 0 | C2-Others |\n",
       "| 1 | HALLMARK_TNFA_SIGNALING_VIA_NFKB   | 0.2755332 | 0 | 0 | C2-Others |\n",
       "| 19 | HALLMARK_INTERFERON_GAMMA_RESPONSE | 0.2963696 | 0 | 0 | C2-Others |\n",
       "| 18 | HALLMARK_INTERFERON_ALPHA_RESPONSE | 0.3175695 | 0 | 0 | C2-Others |\n",
       "\n"
      ],
      "text/plain": [
       "   pathway.name                       log.fold.change p.Value FDR contrast \n",
       "31 HALLMARK_INFLAMMATORY_RESPONSE     0.2256761       0       0   C2-Others\n",
       "46 HALLMARK_ALLOGRAFT_REJECTION       0.2392878       0       0   C2-Others\n",
       "7  HALLMARK_IL6_JAK_STAT3_SIGNALING   0.2702564       0       0   C2-Others\n",
       "1  HALLMARK_TNFA_SIGNALING_VIA_NFKB   0.2755332       0       0   C2-Others\n",
       "19 HALLMARK_INTERFERON_GAMMA_RESPONSE 0.2963696       0       0   C2-Others\n",
       "18 HALLMARK_INTERFERON_ALPHA_RESPONSE 0.3175695       0       0   C2-Others"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pathwaytable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx::write.xlsx(pathwaytable, file = \"~/OneDrive - UHN/Documents/Manuscripts/IPD/v9/TableS7.xlsx\",\n",
    "                row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. \n",
       "-0.4991034 -0.0595144 -0.0008032  0.0222983  0.1043042  0.6466560 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(pathwaytable$log.fold.change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "0.0000000 0.0003008 0.0760154 0.2104938 0.3393969 0.9955480 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(pathwaytable$FDR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_tab <- pathwaytable[,c(\"pathway.name\", \"contrast\",\"log.fold.change\")]\n",
    "\n",
    "fdr_tab <- pathwaytable[,c(\"pathway.name\", \"contrast\",\"FDR\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_mat <- tidyr::spread(fc_tab, key = contrast, value = log.fold.change)\n",
    "\n",
    "fdr_mat <- tidyr::spread(fdr_tab, key = contrast, value = FDR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(fc_mat) <- fc_mat$pathway.name\n",
    "fc_mat$pathway.name <- NULL\n",
    "fc_mat <- as.matrix(fc_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(fdr_mat) <- fdr_mat$pathway.name\n",
    "fdr_mat$pathway.name <- NULL\n",
    "fdr_mat <- as.matrix(fdr_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>C1-Others</th><th scope=col>C2-Others</th><th scope=col>C3-Others</th><th scope=col>C4-Others</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HALLMARK_ADIPOGENESIS</th><td>0.0170638677549</td><td>0.46929424820587</td><td>0.133335830321</td><td>0.417995887</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ALLOGRAFT_REJECTION</th><td>0.0000000000000</td><td>0.00000000000000</td><td>0.000000720659</td><td>0.000000000</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ANDROGEN_RESPONSE</th><td>0.0749467607812</td><td>0.03899330592721</td><td>0.049373804710</td><td>0.215965851</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ANGIOGENESIS</th><td>0.0000002169008</td><td>0.00000002051886</td><td>0.004533356001</td><td>0.000000000</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_APICAL_JUNCTION</th><td>0.0221075329021</td><td>0.11995234008688</td><td>0.124608219426</td><td>0.098497288</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_APICAL_SURFACE</th><td>0.0001492474463</td><td>0.04267685421600</td><td>0.050067435291</td><td>0.006105257</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & C1-Others & C2-Others & C3-Others & C4-Others\\\\\n",
       "\\hline\n",
       "\tHALLMARK\\_ADIPOGENESIS & 0.0170638677549 & 0.46929424820587 & 0.133335830321 & 0.417995887\\\\\n",
       "\tHALLMARK\\_ALLOGRAFT\\_REJECTION & 0.0000000000000 & 0.00000000000000 & 0.000000720659 & 0.000000000\\\\\n",
       "\tHALLMARK\\_ANDROGEN\\_RESPONSE & 0.0749467607812 & 0.03899330592721 & 0.049373804710 & 0.215965851\\\\\n",
       "\tHALLMARK\\_ANGIOGENESIS & 0.0000002169008 & 0.00000002051886 & 0.004533356001 & 0.000000000\\\\\n",
       "\tHALLMARK\\_APICAL\\_JUNCTION & 0.0221075329021 & 0.11995234008688 & 0.124608219426 & 0.098497288\\\\\n",
       "\tHALLMARK\\_APICAL\\_SURFACE & 0.0001492474463 & 0.04267685421600 & 0.050067435291 & 0.006105257\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | C1-Others | C2-Others | C3-Others | C4-Others |\n",
       "|---|---|---|---|---|\n",
       "| HALLMARK_ADIPOGENESIS | 0.0170638677549 | 0.46929424820587 | 0.133335830321 | 0.417995887 |\n",
       "| HALLMARK_ALLOGRAFT_REJECTION | 0.0000000000000 | 0.00000000000000 | 0.000000720659 | 0.000000000 |\n",
       "| HALLMARK_ANDROGEN_RESPONSE | 0.0749467607812 | 0.03899330592721 | 0.049373804710 | 0.215965851 |\n",
       "| HALLMARK_ANGIOGENESIS | 0.0000002169008 | 0.00000002051886 | 0.004533356001 | 0.000000000 |\n",
       "| HALLMARK_APICAL_JUNCTION | 0.0221075329021 | 0.11995234008688 | 0.124608219426 | 0.098497288 |\n",
       "| HALLMARK_APICAL_SURFACE | 0.0001492474463 | 0.04267685421600 | 0.050067435291 | 0.006105257 |\n",
       "\n"
      ],
      "text/plain": [
       "                             C1-Others       C2-Others        C3-Others     \n",
       "HALLMARK_ADIPOGENESIS        0.0170638677549 0.46929424820587 0.133335830321\n",
       "HALLMARK_ALLOGRAFT_REJECTION 0.0000000000000 0.00000000000000 0.000000720659\n",
       "HALLMARK_ANDROGEN_RESPONSE   0.0749467607812 0.03899330592721 0.049373804710\n",
       "HALLMARK_ANGIOGENESIS        0.0000002169008 0.00000002051886 0.004533356001\n",
       "HALLMARK_APICAL_JUNCTION     0.0221075329021 0.11995234008688 0.124608219426\n",
       "HALLMARK_APICAL_SURFACE      0.0001492474463 0.04267685421600 0.050067435291\n",
       "                             C4-Others  \n",
       "HALLMARK_ADIPOGENESIS        0.417995887\n",
       "HALLMARK_ALLOGRAFT_REJECTION 0.000000000\n",
       "HALLMARK_ANDROGEN_RESPONSE   0.215965851\n",
       "HALLMARK_ANGIOGENESIS        0.000000000\n",
       "HALLMARK_APICAL_JUNCTION     0.098497288\n",
       "HALLMARK_APICAL_SURFACE      0.006105257"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(fdr_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>50</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 50\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 50\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 50  4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(fc_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove those pathways with fdr > 0.1 in all clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_mat_pathways <- fc_mat[rownames(fdr_mat)[rowSums(fdr_mat > 0.1) <= 2],]\n",
    "\n",
    "fdr_mat_pathways <- fdr_mat[rownames(fdr_mat)[rowSums(fdr_mat > 0.1) <= 2],]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>29</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 29\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 29\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 29  4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(fc_mat_pathways)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>C1-Others</th><th scope=col>C2-Others</th><th scope=col>C3-Others</th><th scope=col>C4-Others</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HALLMARK_ALLOGRAFT_REJECTION</th><td>0.6227865</td><td>0.23928784</td><td>-0.16645024</td><td>-0.43922019</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ANDROGEN_RESPONSE</th><td>0.1047807</td><td>0.08529859</td><td>-0.07434887</td><td>-0.05947901</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_ANGIOGENESIS</th><td>0.3413720</td><td>0.24674186</td><td>-0.12708994</td><td>-0.34185104</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_APICAL_JUNCTION</th><td>0.1170657</td><td>0.05774794</td><td>-0.05087814</td><td>-0.06774862</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_APICAL_SURFACE</th><td>0.1588116</td><td>0.06282899</td><td>-0.05471159</td><td>-0.09412805</td></tr>\n",
       "\t<tr><th scope=row>HALLMARK_APOPTOSIS</th><td>0.3312808</td><td>0.17732190</td><td>-0.11822580</td><td>-0.25385839</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & C1-Others & C2-Others & C3-Others & C4-Others\\\\\n",
       "\\hline\n",
       "\tHALLMARK\\_ALLOGRAFT\\_REJECTION & 0.6227865 & 0.23928784 & -0.16645024 & -0.43922019\\\\\n",
       "\tHALLMARK\\_ANDROGEN\\_RESPONSE & 0.1047807 & 0.08529859 & -0.07434887 & -0.05947901\\\\\n",
       "\tHALLMARK\\_ANGIOGENESIS & 0.3413720 & 0.24674186 & -0.12708994 & -0.34185104\\\\\n",
       "\tHALLMARK\\_APICAL\\_JUNCTION & 0.1170657 & 0.05774794 & -0.05087814 & -0.06774862\\\\\n",
       "\tHALLMARK\\_APICAL\\_SURFACE & 0.1588116 & 0.06282899 & -0.05471159 & -0.09412805\\\\\n",
       "\tHALLMARK\\_APOPTOSIS & 0.3312808 & 0.17732190 & -0.11822580 & -0.25385839\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | C1-Others | C2-Others | C3-Others | C4-Others |\n",
       "|---|---|---|---|---|\n",
       "| HALLMARK_ALLOGRAFT_REJECTION | 0.6227865 | 0.23928784 | -0.16645024 | -0.43922019 |\n",
       "| HALLMARK_ANDROGEN_RESPONSE | 0.1047807 | 0.08529859 | -0.07434887 | -0.05947901 |\n",
       "| HALLMARK_ANGIOGENESIS | 0.3413720 | 0.24674186 | -0.12708994 | -0.34185104 |\n",
       "| HALLMARK_APICAL_JUNCTION | 0.1170657 | 0.05774794 | -0.05087814 | -0.06774862 |\n",
       "| HALLMARK_APICAL_SURFACE | 0.1588116 | 0.06282899 | -0.05471159 | -0.09412805 |\n",
       "| HALLMARK_APOPTOSIS | 0.3312808 | 0.17732190 | -0.11822580 | -0.25385839 |\n",
       "\n"
      ],
      "text/plain": [
       "                             C1-Others C2-Others  C3-Others   C4-Others  \n",
       "HALLMARK_ALLOGRAFT_REJECTION 0.6227865 0.23928784 -0.16645024 -0.43922019\n",
       "HALLMARK_ANDROGEN_RESPONSE   0.1047807 0.08529859 -0.07434887 -0.05947901\n",
       "HALLMARK_ANGIOGENESIS        0.3413720 0.24674186 -0.12708994 -0.34185104\n",
       "HALLMARK_APICAL_JUNCTION     0.1170657 0.05774794 -0.05087814 -0.06774862\n",
       "HALLMARK_APICAL_SURFACE      0.1588116 0.06282899 -0.05471159 -0.09412805\n",
       "HALLMARK_APOPTOSIS           0.3312808 0.17732190 -0.11822580 -0.25385839"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(fc_mat_pathways)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean up row and colnames\n",
    "rownames(fc_mat_pathways) <- gsub(\"HALLMARK_\", \"\", rownames(fc_mat_pathways))\n",
    "rownames(fc_mat_pathways) <- gsub(\"_\", \" \", rownames(fc_mat_pathways))\n",
    "\n",
    "colnames(fc_mat_pathways) <- gsub(\"-Others\", \"\", colnames(fc_mat_pathways))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "-0.499103 -0.103879  0.008798  0.030468  0.158927  0.646656 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(as.vector(fc_mat_pathways))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_fun= colorRamp2(c(-0.6, 0, 0.6), c(\"blue\", \"white\", \"red\"))\n",
    "    \n",
    "pathways_hm = Heatmap(fc_mat_pathways,\n",
    "                       #titles and names   \n",
    "                       name = \"Log2_FC\",   \n",
    "                       show_row_names = TRUE,\n",
    "                       show_column_names = TRUE,     \n",
    "                       #clusters and orders  \n",
    "                       cluster_columns = TRUE,\n",
    "                       cluster_rows = TRUE,\n",
    "                       show_column_dend = TRUE,\n",
    "                       #aesthestics\n",
    "                       col = col_fun,\n",
    "                       column_names_gp = gpar(fontsize = 10),\n",
    "                       row_names_gp = gpar(fontsize = 10),\n",
    "                       height = unit(29, \"cm\"), width = unit(4, \"cm\"),\n",
    "                       column_title_gp = gpar(fontsize = 10),\n",
    "                       column_title = NULL,\n",
    "                       row_title = NULL,\n",
    "\n",
    "           \n",
    "                    column_names_rot = 45,\n",
    "                      show_heatmap_legend = FALSE\n",
    "                      \n",
    "                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "            cell_fun = function(j, i, x, y, width, height, fill){\n",
    "                          if(fdr_mat_pathways[i, j] > 0.10)\n",
    "                              grid.points(x, y, pch = 7,size = unit(13,\"mm\")\n",
    "                                        )},"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'ROOT'</li><li>'global'</li><li>'global_layout'</li><li>'global-heatmaplist'</li><li>'main_heatmap_list'</li><li>'heatmap_Log2_FC'</li><li>'Log2_FC_heatmap_body_wrap'</li><li>'Log2_FC_heatmap_body_1_1'</li><li>'Log2_FC_dend_row_1'</li><li>'Log2_FC_dend_column_1'</li><li>'Log2_FC_row_names_1'</li><li>'Log2_FC_column_names_1'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'ROOT'\n",
       "\\item 'global'\n",
       "\\item 'global\\_layout'\n",
       "\\item 'global-heatmaplist'\n",
       "\\item 'main\\_heatmap\\_list'\n",
       "\\item 'heatmap\\_Log2\\_FC'\n",
       "\\item 'Log2\\_FC\\_heatmap\\_body\\_wrap'\n",
       "\\item 'Log2\\_FC\\_heatmap\\_body\\_1\\_1'\n",
       "\\item 'Log2\\_FC\\_dend\\_row\\_1'\n",
       "\\item 'Log2\\_FC\\_dend\\_column\\_1'\n",
       "\\item 'Log2\\_FC\\_row\\_names\\_1'\n",
       "\\item 'Log2\\_FC\\_column\\_names\\_1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ROOT'\n",
       "2. 'global'\n",
       "3. 'global_layout'\n",
       "4. 'global-heatmaplist'\n",
       "5. 'main_heatmap_list'\n",
       "6. 'heatmap_Log2_FC'\n",
       "7. 'Log2_FC_heatmap_body_wrap'\n",
       "8. 'Log2_FC_heatmap_body_1_1'\n",
       "9. 'Log2_FC_dend_row_1'\n",
       "10. 'Log2_FC_dend_column_1'\n",
       "11. 'Log2_FC_row_names_1'\n",
       "12. 'Log2_FC_column_names_1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ROOT\"                      \"global\"                   \n",
       " [3] \"global_layout\"             \"global-heatmaplist\"       \n",
       " [5] \"main_heatmap_list\"         \"heatmap_Log2_FC\"          \n",
       " [7] \"Log2_FC_heatmap_body_wrap\" \"Log2_FC_heatmap_body_1_1\" \n",
       " [9] \"Log2_FC_dend_row_1\"        \"Log2_FC_dend_column_1\"    \n",
       "[11] \"Log2_FC_row_names_1\"       \"Log2_FC_column_names_1\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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btZv1g0Sdm10ue58INjeVFrt1xT7D55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XHWc9RCI/jBuSJayb6BPWo5uhYeuEgIC4GmLh3ZYAACAASURBVI0PAwLutRw4UFfE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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(pathways_hm)\n",
    "\n",
    "list_components()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"heatmap_pathways_MSigDB.pdf\"),\n",
    "   width = 10, height = 45)\n",
    "\n",
    "\n",
    "draw(pathways_hm)\n",
    "\n",
    "decorate_heatmap_body(\"Log2_FC\", {\n",
    "    grid.rect(x = 0, y = 1, just = c(\"left\", \"top\"),\n",
    "          width = 1,height = 0.345, gp = gpar(col = \"black\", lwd = 5))\n",
    "})\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_fun= colorRamp2(c(-0.6, 0, 0.6), c(\"blue\", \"white\", \"red\"))\n",
    "\n",
    "qusage_lgd = Legend(col_fun = col_fun, title = \"Fold change\\n(Log2)\",\n",
    "                   at = c(-0.6, 0, 0.6), legend_height = unit(2, \"cm\"))\n",
    "\n",
    "  pdf(file = paste0(plotpath,\"qusage_legend.pdf\"),\n",
    "      width = 8, \n",
    "      height = 8,\n",
    "      useDingbats = FALSE)\n",
    "  draw(qusage_lgd)\n",
    "  dev.off() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate with proteomics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input gene list to cptact data viewer and download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteom <- data.table::fread(file = paste0(datapath, \"Proteomics/CPTAC3-pbt_HALLMARK_genes_4proteomics.csv\"), sep = \",\",\n",
    "                             check.names = F, header = T, na.strings = c(\"\", \"NA\"), \n",
    "                             stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteom <- as.data.frame(proteom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteom <- proteom[ grepl(\"proteo\",proteom$idx),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames_proteom <- proteom$`Gene symbol`\n",
    "\n",
    "rownames(proteom) <- proteom$`Gene symbol`\n",
    "proteom$`Gene symbol` <- NULL\n",
    "proteom$idx <- NULL\n",
    "proteom$`Data type` <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteom <- as.matrix(proteom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteom <- apply(proteom,2, as.numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(proteom) <- rownames_proteom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "-2.0723 -0.7495 -0.1233  0.0000  0.6953  2.8014 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(proteom[1,]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(proteom) <- gsub(\"X\", \"\", colnames(proteom))\n",
    "colnames(proteom) <- gsub(\"[.]\", \"-\", colnames(proteom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 218 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>7316-872</th><th scope=col>7316-2151</th><th scope=col>7316-2170</th><th scope=col>7316-2146</th><th scope=col>7316-3319</th><th scope=col>7316-130</th><th scope=col>7316-2648</th><th scope=col>7316-2144</th><th scope=col>7316-384</th><th scope=col>7316-443</th><th scope=col>⋯</th><th scope=col>7316-957</th><th scope=col>7316-156</th><th scope=col>7316-154</th><th scope=col>7316-932</th><th scope=col>7316-368</th><th scope=col>7316-146</th><th scope=col>7316-475</th><th scope=col>7316-488</th><th scope=col>7316-954</th><th scope=col>7316-2663</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A2M</th><td> 1.17027033</td><td>-0.5743360</td><td> 0.59496849</td><td>-0.6335644</td><td> 0.6997003</td><td>-0.83742105</td><td>-2.0722576</td><td>-0.006544646</td><td> 1.2974038</td><td> 0.6979533</td><td>⋯</td><td> 1.6602089</td><td>-0.05780405</td><td> 1.66167839</td><td>-1.6368700</td><td> 0.9715630</td><td> 0.885687257</td><td>-0.054627732</td><td> 0.7410713</td><td> 0.1919530</td><td>-1.094637979</td></tr>\n",
       "\t<tr><th scope=row>ABCB1</th><td>-0.08381773</td><td> 0.4717423</td><td> 0.08782766</td><td>-0.5069743</td><td>-0.2725235</td><td> 1.14993700</td><td>-0.4286241</td><td> 0.297925775</td><td>-2.7538323</td><td>-0.9601827</td><td>⋯</td><td>-0.3829181</td><td>-0.56124478</td><td> 0.14832536</td><td> 0.5554954</td><td>-0.2554827</td><td>-0.003093275</td><td>-0.100305943</td><td> 0.3498220</td><td> 0.3497704</td><td>-0.008693533</td></tr>\n",
       "\t<tr><th scope=row>ABCE1</th><td>-0.96223316</td><td>-1.5030361</td><td>-1.69113011</td><td>-0.1859401</td><td>-0.4820084</td><td>-3.34370824</td><td>-1.2929395</td><td>-0.177119458</td><td>-1.2893723</td><td> 0.4852315</td><td>⋯</td><td> 1.5011056</td><td> 0.51069457</td><td> 0.34370090</td><td>-0.8081195</td><td> 1.6206934</td><td> 0.984334910</td><td> 1.037286490</td><td> 0.3146364</td><td>-0.4907370</td><td>-0.071388368</td></tr>\n",
       "\t<tr><th scope=row>ABI1</th><td>-0.72795362</td><td> 0.9738262</td><td>-1.30277822</td><td>-0.8145938</td><td>-1.4309733</td><td>-0.92663925</td><td>-1.5725272</td><td>-0.746616212</td><td>-0.5254130</td><td>-0.2546989</td><td>⋯</td><td> 1.3292764</td><td> 0.13421933</td><td> 0.02902334</td><td> 0.9220527</td><td> 0.8975631</td><td> 1.694717193</td><td> 1.503893315</td><td> 1.3250068</td><td> 1.3729214</td><td> 1.336956716</td></tr>\n",
       "\t<tr><th scope=row>ACACA</th><td> 0.26814355</td><td>-0.2418990</td><td> 0.07250943</td><td>-1.5814215</td><td>-1.8205667</td><td> 0.06561383</td><td>-2.0507721</td><td>-0.407322123</td><td>-2.0954588</td><td>-1.1982217</td><td>⋯</td><td>-0.4373494</td><td> 2.26186969</td><td>-1.42696810</td><td> 2.1766821</td><td>-0.4053189</td><td>-0.017007153</td><td>-0.001847593</td><td>-0.4182093</td><td>-0.4174602</td><td>-1.013486751</td></tr>\n",
       "\t<tr><th scope=row>ACHE</th><td>-0.89310870</td><td> 0.4170447</td><td>-0.61106041</td><td>-0.9032629</td><td>-0.4612296</td><td> 0.18356565</td><td>-1.0152332</td><td>-0.326212420</td><td> 0.5634417</td><td> 0.1389767</td><td>⋯</td><td> 0.2255436</td><td>-0.06896869</td><td> 0.24250405</td><td>-0.1847278</td><td> 0.4008109</td><td> 0.058523880</td><td> 0.097098904</td><td> 0.1881703</td><td> 0.6694573</td><td> 0.627180792</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 218 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & 7316-872 & 7316-2151 & 7316-2170 & 7316-2146 & 7316-3319 & 7316-130 & 7316-2648 & 7316-2144 & 7316-384 & 7316-443 & ⋯ & 7316-957 & 7316-156 & 7316-154 & 7316-932 & 7316-368 & 7316-146 & 7316-475 & 7316-488 & 7316-954 & 7316-2663\\\\\n",
       "\\hline\n",
       "\tA2M &  1.17027033 & -0.5743360 &  0.59496849 & -0.6335644 &  0.6997003 & -0.83742105 & -2.0722576 & -0.006544646 &  1.2974038 &  0.6979533 & ⋯ &  1.6602089 & -0.05780405 &  1.66167839 & -1.6368700 &  0.9715630 &  0.885687257 & -0.054627732 &  0.7410713 &  0.1919530 & -1.094637979\\\\\n",
       "\tABCB1 & -0.08381773 &  0.4717423 &  0.08782766 & -0.5069743 & -0.2725235 &  1.14993700 & -0.4286241 &  0.297925775 & -2.7538323 & -0.9601827 & ⋯ & -0.3829181 & -0.56124478 &  0.14832536 &  0.5554954 & -0.2554827 & -0.003093275 & -0.100305943 &  0.3498220 &  0.3497704 & -0.008693533\\\\\n",
       "\tABCE1 & -0.96223316 & -1.5030361 & -1.69113011 & -0.1859401 & -0.4820084 & -3.34370824 & -1.2929395 & -0.177119458 & -1.2893723 &  0.4852315 & ⋯ &  1.5011056 &  0.51069457 &  0.34370090 & -0.8081195 &  1.6206934 &  0.984334910 &  1.037286490 &  0.3146364 & -0.4907370 & -0.071388368\\\\\n",
       "\tABI1 & -0.72795362 &  0.9738262 & -1.30277822 & -0.8145938 & -1.4309733 & -0.92663925 & -1.5725272 & -0.746616212 & -0.5254130 & -0.2546989 & ⋯ &  1.3292764 &  0.13421933 &  0.02902334 &  0.9220527 &  0.8975631 &  1.694717193 &  1.503893315 &  1.3250068 &  1.3729214 &  1.336956716\\\\\n",
       "\tACACA &  0.26814355 & -0.2418990 &  0.07250943 & -1.5814215 & -1.8205667 &  0.06561383 & -2.0507721 & -0.407322123 & -2.0954588 & -1.1982217 & ⋯ & -0.4373494 &  2.26186969 & -1.42696810 &  2.1766821 & -0.4053189 & -0.017007153 & -0.001847593 & -0.4182093 & -0.4174602 & -1.013486751\\\\\n",
       "\tACHE & -0.89310870 &  0.4170447 & -0.61106041 & -0.9032629 & -0.4612296 &  0.18356565 & -1.0152332 & -0.326212420 &  0.5634417 &  0.1389767 & ⋯ &  0.2255436 & -0.06896869 &  0.24250405 & -0.1847278 &  0.4008109 &  0.058523880 &  0.097098904 &  0.1881703 &  0.6694573 &  0.627180792\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 218 of type dbl\n",
       "\n",
       "| <!--/--> | 7316-872 | 7316-2151 | 7316-2170 | 7316-2146 | 7316-3319 | 7316-130 | 7316-2648 | 7316-2144 | 7316-384 | 7316-443 | ⋯ | 7316-957 | 7316-156 | 7316-154 | 7316-932 | 7316-368 | 7316-146 | 7316-475 | 7316-488 | 7316-954 | 7316-2663 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| A2M |  1.17027033 | -0.5743360 |  0.59496849 | -0.6335644 |  0.6997003 | -0.83742105 | -2.0722576 | -0.006544646 |  1.2974038 |  0.6979533 | ⋯ |  1.6602089 | -0.05780405 |  1.66167839 | -1.6368700 |  0.9715630 |  0.885687257 | -0.054627732 |  0.7410713 |  0.1919530 | -1.094637979 |\n",
       "| ABCB1 | -0.08381773 |  0.4717423 |  0.08782766 | -0.5069743 | -0.2725235 |  1.14993700 | -0.4286241 |  0.297925775 | -2.7538323 | -0.9601827 | ⋯ | -0.3829181 | -0.56124478 |  0.14832536 |  0.5554954 | -0.2554827 | -0.003093275 | -0.100305943 |  0.3498220 |  0.3497704 | -0.008693533 |\n",
       "| ABCE1 | -0.96223316 | -1.5030361 | -1.69113011 | -0.1859401 | -0.4820084 | -3.34370824 | -1.2929395 | -0.177119458 | -1.2893723 |  0.4852315 | ⋯ |  1.5011056 |  0.51069457 |  0.34370090 | -0.8081195 |  1.6206934 |  0.984334910 |  1.037286490 |  0.3146364 | -0.4907370 | -0.071388368 |\n",
       "| ABI1 | -0.72795362 |  0.9738262 | -1.30277822 | -0.8145938 | -1.4309733 | -0.92663925 | -1.5725272 | -0.746616212 | -0.5254130 | -0.2546989 | ⋯ |  1.3292764 |  0.13421933 |  0.02902334 |  0.9220527 |  0.8975631 |  1.694717193 |  1.503893315 |  1.3250068 |  1.3729214 |  1.336956716 |\n",
       "| ACACA |  0.26814355 | -0.2418990 |  0.07250943 | -1.5814215 | -1.8205667 |  0.06561383 | -2.0507721 | -0.407322123 | -2.0954588 | -1.1982217 | ⋯ | -0.4373494 |  2.26186969 | -1.42696810 |  2.1766821 | -0.4053189 | -0.017007153 | -0.001847593 | -0.4182093 | -0.4174602 | -1.013486751 |\n",
       "| ACHE | -0.89310870 |  0.4170447 | -0.61106041 | -0.9032629 | -0.4612296 |  0.18356565 | -1.0152332 | -0.326212420 |  0.5634417 |  0.1389767 | ⋯ |  0.2255436 | -0.06896869 |  0.24250405 | -0.1847278 |  0.4008109 |  0.058523880 |  0.097098904 |  0.1881703 |  0.6694573 |  0.627180792 |\n",
       "\n"
      ],
      "text/plain": [
       "      7316-872    7316-2151  7316-2170   7316-2146  7316-3319  7316-130   \n",
       "A2M    1.17027033 -0.5743360  0.59496849 -0.6335644  0.6997003 -0.83742105\n",
       "ABCB1 -0.08381773  0.4717423  0.08782766 -0.5069743 -0.2725235  1.14993700\n",
       "ABCE1 -0.96223316 -1.5030361 -1.69113011 -0.1859401 -0.4820084 -3.34370824\n",
       "ABI1  -0.72795362  0.9738262 -1.30277822 -0.8145938 -1.4309733 -0.92663925\n",
       "ACACA  0.26814355 -0.2418990  0.07250943 -1.5814215 -1.8205667  0.06561383\n",
       "ACHE  -0.89310870  0.4170447 -0.61106041 -0.9032629 -0.4612296  0.18356565\n",
       "      7316-2648  7316-2144    7316-384   7316-443   ⋯ 7316-957   7316-156   \n",
       "A2M   -2.0722576 -0.006544646  1.2974038  0.6979533 ⋯  1.6602089 -0.05780405\n",
       "ABCB1 -0.4286241  0.297925775 -2.7538323 -0.9601827 ⋯ -0.3829181 -0.56124478\n",
       "ABCE1 -1.2929395 -0.177119458 -1.2893723  0.4852315 ⋯  1.5011056  0.51069457\n",
       "ABI1  -1.5725272 -0.746616212 -0.5254130 -0.2546989 ⋯  1.3292764  0.13421933\n",
       "ACACA -2.0507721 -0.407322123 -2.0954588 -1.1982217 ⋯ -0.4373494  2.26186969\n",
       "ACHE  -1.0152332 -0.326212420  0.5634417  0.1389767 ⋯  0.2255436 -0.06896869\n",
       "      7316-154    7316-932   7316-368   7316-146     7316-475     7316-488  \n",
       "A2M    1.66167839 -1.6368700  0.9715630  0.885687257 -0.054627732  0.7410713\n",
       "ABCB1  0.14832536  0.5554954 -0.2554827 -0.003093275 -0.100305943  0.3498220\n",
       "ABCE1  0.34370090 -0.8081195  1.6206934  0.984334910  1.037286490  0.3146364\n",
       "ABI1   0.02902334  0.9220527  0.8975631  1.694717193  1.503893315  1.3250068\n",
       "ACACA -1.42696810  2.1766821 -0.4053189 -0.017007153 -0.001847593 -0.4182093\n",
       "ACHE   0.24250405 -0.1847278  0.4008109  0.058523880  0.097098904  0.1881703\n",
       "      7316-954   7316-2663   \n",
       "A2M    0.1919530 -1.094637979\n",
       "ABCB1  0.3497704 -0.008693533\n",
       "ABCE1 -0.4907370 -0.071388368\n",
       "ABI1   1.3729214  1.336956716\n",
       "ACACA -0.4174602 -1.013486751\n",
       "ACHE   0.6694573  0.627180792"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(proteom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_proteom <- metadata_IC[metadata_IC$sample_id %in% colnames(proteom),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>147</li><li>24</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 147\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 147\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 147  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_proteom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  47 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
      "Myeloid-driven  0.902              -              -             \n",
      "Pediatric cold  0.012              0.000000000068 -             \n",
      "Immune excluded 0.000031481441     0.000000000019 0.031         \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "coag <- hallmark_IC_stats_ridge(proteom, metadata_IC_proteom, \"HALLMARK_COAGULATION\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1), y = c(3.3, 4.3))\n",
    "\n",
    "coag <- coag + geom_text(data = label.df,aes(x = x, y = y), label = c(\"*\", \"***\"), size = 15) +\n",
    "ggtitle(expression(~underline(\"Coagulation (n = 47)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank(), plot.margin = unit(c(1,0,0,0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  27 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
      "Myeloid-driven  1.0000             -              -             \n",
      "Pediatric cold  0.00001569102      0.00000007442  -             \n",
      "Immune excluded 0.00000000572      0.00000000012  0.0055        \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "emt <- hallmark_IC_stats_ridge(proteom, metadata_IC_proteom, \"HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1), y = c(3.3, 4.3))\n",
    "\n",
    "emt <- emt + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"EMT (n = 27)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank(), plot.margin = unit(c(1,0,0,0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]   7 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
      "Myeloid-driven  1.00000            -              -             \n",
      "Pediatric cold  0.00069            0.0000077      -             \n",
      "Immune excluded 0.0000193          0.0000015      0.20270       \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "angio <- hallmark_IC_stats_ridge(proteom, metadata_IC_proteom, \"HALLMARK_ANGIOGENESIS\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1), y = c(3.3, 4.3))\n",
    "\n",
    "angio <- angio + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"Angiogenesis (n = 7)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank(), plot.margin = unit(c(1,0,0,0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 100 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed  Myeloid-driven      Pediatric cold\n",
      "Myeloid-driven  0.62582             -                   -             \n",
      "Pediatric cold  0.00000000225047096 0.00000000000122628 -             \n",
      "Immune excluded 0.00000000000003834 0.00000000000000038 0.00021       \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "ifng <- hallmark_IC_stats_ridge(proteom, metadata_IC_proteom, \"HALLMARK_INTERFERON_GAMMA_RESPONSE\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1), y = c(3.3, 4.3))\n",
    "\n",
    "ifng <- ifng + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"IFN-gamma (n = 100)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  56 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven    Pediatric cold\n",
      "Myeloid-driven  1.0000             -                 -             \n",
      "Pediatric cold  0.000000053170279  0.000000000013452 -             \n",
      "Immune excluded 0.000000000004458  0.000000000000024 0.0011        \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "ifna <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_INTERFERON_ALPHA_RESPONSE\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3, 4.3))\n",
    "\n",
    "ifna <- ifna + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"IFN-alpha (n = 56)\"))) +labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  54 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven    Pediatric cold\n",
      "Myeloid-driven  1.0000             -                 -             \n",
      "Pediatric cold  0.000048969297330  0.000000000002942 -             \n",
      "Immune excluded 0.000000014165357  0.000000000000086 0.0046        \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "inflam <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_INFLAMMATORY_RESPONSE\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3,4.3))\n",
    "\n",
    "inflam <- inflam + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"Inflammatory (n = 54)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  23 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven  Pediatric cold\n",
      "Myeloid-driven  1.00000            -               -             \n",
      "Pediatric cold  0.00020            0.0000000032803 -             \n",
      "Immune excluded 0.0000000068966    0.0000000000005 0.00066       \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "il6 <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_IL6_JAK_STAT3_SIGNALING\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3, 4.3))\n",
    "\n",
    "il6 <- il6 + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"IL6/JAK/STAT3 signaling (n = 23)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 123 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven    Pediatric cold\n",
      "Myeloid-driven  1.0000             -                 -             \n",
      "Pediatric cold  0.000027204190288  0.000000000002875 -             \n",
      "Immune excluded 0.000000005782373  0.000000000000053 0.0035        \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "complement <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_COMPLEMENT\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3, 4.3))\n",
    "\n",
    "complement <- complement + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"Complement (n = 123)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank(), plot.margin = unit(c(1,0,0,0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  51 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
      "Myeloid-driven  1.0000             -              -             \n",
      "Pediatric cold  0.0002             0.000000020650 -             \n",
      "Immune excluded 0.000000038661     0.000000000022 0.0033        \n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "tnfa <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_TNFA_SIGNALING_VIA_NFKB\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3, 4.3))\n",
    "\n",
    "tnfa <- tnfa + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"TNFa signaling via NFkB (n = 51)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank(),plot.margin = unit(c(1,0,0,0), \"cm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  72 147\n",
      "\n",
      "\tPairwise comparisons using t tests with pooled SD \n",
      "\n",
      "data:  metadata$pathway_mean and metadata$immune_cluster \n",
      "\n",
      "                Pediatric inflamed   Myeloid-driven       Pediatric cold    \n",
      "Myeloid-driven  0.25                 -                    -                 \n",
      "Pediatric cold  0.0000000000049200   0.0000000000000018   -                 \n",
      "Immune excluded < 0.0000000000000002 < 0.0000000000000002 0.0000463610805322\n",
      "\n",
      "P value adjustment method: bonferroni \n"
     ]
    }
   ],
   "source": [
    "allograft <- hallmark_IC_stats_ridge(proteom,metadata_IC_proteom, \"HALLMARK_ALLOGRAFT_REJECTION\")\n",
    "\n",
    "label.df <- data.frame(x = c(1,1),y = c(3.3, 4.3))\n",
    "\n",
    "allograft <- allograft + geom_text(data = label.df,aes(x = x, y = y), label = \"***\", size = 15) +\n",
    "ggtitle(expression(~underline(\"Allograft rejection (n = 72)\"))) + labs(x = \"Average protein z score\") +\n",
    "theme(axis.text.y = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Picking joint bandwidth of 0.126\n",
      "\n",
      "Picking joint bandwidth of 0.136\n",
      "\n",
      "Picking joint bandwidth of 0.0936\n",
      "\n",
      "Picking joint bandwidth of 0.114\n",
      "\n",
      "Picking joint bandwidth of 0.12\n",
      "\n",
      "Picking joint bandwidth of 0.105\n",
      "\n",
      "Picking joint bandwidth of 0.119\n",
      "\n",
      "Picking joint bandwidth of 0.146\n",
      "\n",
      "Picking joint bandwidth of 0.188\n",
      "\n",
      "Picking joint bandwidth of 0.164\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"IPD_clusters_HALLMARK_proteomics.pdf\"),\n",
    "   width = 30, height = 20,\n",
    "   useDingbats = FALSE)\n",
    "\n",
    "grid.arrange(ifng, ifna,allograft, il6, inflam, tnfa, complement, emt, angio, coag,nrow=2)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control for cancer type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_mat_pathway <- as.matrix(proteom[rownames(proteom) %in% Hs.H[[\"HALLMARK_INTERFERON_GAMMA_RESPONSE\"]], \n",
    "                                            rownames(metadata_IC_proteom)])\n",
    "    pathway_mean <- colMeans(protein_mat_pathway)\n",
    "    \n",
    "    metadata_IC_proteom$pathway_mean <- pathway_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfit <- aov(pathway_mean~ immune_cluster+cohort,metadata_IC_proteom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "aov(formula = pathway_mean ~ immune_cluster + cohort, data = metadata_IC_proteom)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.75818 -0.21674  0.02252  0.16094  0.80904 \n",
       "\n",
       "Coefficients:\n",
       "                 Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)       0.27020    0.14403   1.876 0.062871 .  \n",
       "immune_clusterC2 -0.10677    0.09988  -1.069 0.286985    \n",
       "immune_clusterC3 -0.33272    0.09327  -3.567 0.000503 ***\n",
       "immune_clusterC4 -0.76453    0.11056  -6.915 1.82e-10 ***\n",
       "cohortCPH         0.30024    0.15781   1.903 0.059273 .  \n",
       "cohortEPN         0.09347    0.13339   0.701 0.484691    \n",
       "cohortMB         -0.24240    0.14895  -1.627 0.106026    \n",
       "cohortpedHGG      0.08803    0.15094   0.583 0.560747    \n",
       "cohortpedLGG      0.15259    0.11691   1.305 0.194108    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.2963 on 132 degrees of freedom\n",
       "Multiple R-squared:  0.5714,\tAdjusted R-squared:  0.5454 \n",
       "F-statistic: 21.99 on 8 and 132 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary.lm(myfit)"
   ]
  }
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