10.gene-expression-signatures/4.visualize-signature-separation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Separation Analysis\n",
    "\n",
    "**Gregory Way 2019**\n",
    "\n",
    "Visualizing GTEx Sex and TARGET MYCN Status Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“package ‘dplyr’ was built under R version 3.4.4”Warning message:\n",
      "“package ‘ggplot2’ was built under R version 3.4.4”Warning message:\n",
      "“package ‘cowplot’ was built under R version 3.4.4”"
     ]
    }
   ],
   "source": [
    "suppressPackageStartupMessages(library(dplyr))\n",
    "suppressPackageStartupMessages(library(ggplot2))\n",
    "suppressPackageStartupMessages(library(cowplot))\n",
    "suppressPackageStartupMessages(library(svglite))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'results/nbl_mycn_separation_target_t_test.tsv'</li>\n",
       "\t<li>'results/sex_separation_gtex_t_test.tsv'</li>\n",
       "\t<li>'results/sex_separation_tcga_t_test.tsv'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'results/nbl\\_mycn\\_separation\\_target\\_t\\_test.tsv'\n",
       "\\item 'results/sex\\_separation\\_gtex\\_t\\_test.tsv'\n",
       "\\item 'results/sex\\_separation\\_tcga\\_t\\_test.tsv'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'results/nbl_mycn_separation_target_t_test.tsv'\n",
       "2. 'results/sex_separation_gtex_t_test.tsv'\n",
       "3. 'results/sex_separation_tcga_t_test.tsv'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"results/nbl_mycn_separation_target_t_test.tsv\"\n",
       "[2] \"results/sex_separation_gtex_t_test.tsv\"       \n",
       "[3] \"results/sex_separation_tcga_t_test.tsv\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Store t test results and identify top features\n",
    "t_test_results_files <- list.files(\"results\", full.names = TRUE)\n",
    "t_test_results_files <- t_test_results_files[grepl('t_test', t_test_results_files)]\n",
    "t_test_results_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store t test results and identify top features\n",
    "best_result_list <- list()\n",
    "line_gg_list <- list()\n",
    "for (t_test_file in t_test_results_files) {\n",
    "  full_results_df <- readr::read_tsv(t_test_file,\n",
    "                                     col_types = readr::cols(\n",
    "                                       t_stat = readr::col_double(),\n",
    "                                       t_p = readr::col_double(),\n",
    "                                       algorithm = readr::col_character(),\n",
    "                                       feature_num = readr::col_double(),\n",
    "                                       z_dim = readr::col_double(),\n",
    "                                       signal = readr::col_character(),\n",
    "                                       seed = readr::col_double(),\n",
    "                                       neg_log_p = readr::col_double()\n",
    "                                       )\n",
    "                                     )\n",
    "  \n",
    "  top_results_df <- full_results_df %>%\n",
    "    dplyr::group_by(algorithm, z_dim, signal) %>%\n",
    "    dplyr::filter(neg_log_p == max(neg_log_p),\n",
    "                  signal == \"signal\")\n",
    "  \n",
    "  # Create factors for plotting\n",
    "  top_results_df$z_dim <-\n",
    "    factor(top_results_df$z_dim,\n",
    "           levels =\n",
    "             sort(as.numeric(paste(unique(top_results_df$z_dim))))\n",
    "    )\n",
    "  \n",
    "  top_results_df$algorithm <-\n",
    "    factor(top_results_df$algorithm,\n",
    "           levels = c(\"pca\", \"ica\", \"nmf\", \"dae\", \"vae\"))\n",
    "  \n",
    "  panel_gg <- ggplot(top_results_df,\n",
    "                       aes(x = z_dim,\n",
    "                           y = neg_log_p,\n",
    "                           color = algorithm,\n",
    "                           group = algorithm)) +\n",
    "    geom_smooth(aes(fill = algorithm),\n",
    "                lwd = 0.3,\n",
    "                alpha = 0.4,\n",
    "                method = 'loess',\n",
    "                formula = 'y ~ x') +\n",
    "    geom_point(size = 0.5) +\n",
    "    scale_color_manual(name = \"Algorithm\",\n",
    "                       values = c(\"#e41a1c\",\n",
    "                                  \"#377eb8\",\n",
    "                                  \"#4daf4a\",\n",
    "                                  \"#984ea3\",\n",
    "                                  \"#ff7f00\"),\n",
    "                       labels = c(\"pca\" = \"PCA\",\n",
    "                                  \"ica\" = \"ICA\",\n",
    "                                  \"nmf\" = \"NMF\",\n",
    "                                  \"dae\" = \"DAE\",\n",
    "                                  \"vae\" = \"VAE\")) +\n",
    "    scale_fill_manual(name = \"Algorithm\",\n",
    "                      values = c(\"#e41a1c\",\n",
    "                                 \"#377eb8\",\n",
    "                                 \"#4daf4a\",\n",
    "                                 \"#984ea3\",\n",
    "                                 \"#ff7f00\"),\n",
    "                      labels = c(\"pca\" = \"PCA\",\n",
    "                                 \"ica\" = \"ICA\",\n",
    "                                 \"nmf\" = \"NMF\",\n",
    "                                 \"dae\" = \"DAE\",\n",
    "                                 \"vae\" = \"VAE\")) +\n",
    "    theme_bw() +\n",
    "    ylab(\"-log10 P Value\") +\n",
    "    xlab(\"k Dimensions\") +\n",
    "    theme(axis.title.x = element_text(size = 7),\n",
    "          axis.title.y = element_text(size = 7),\n",
    "          axis.text.x = element_text(angle = 90,\n",
    "                                     size = 5),\n",
    "          axis.text.y = element_text(size = 6),\n",
    "          plot.title = element_text(size = 8,\n",
    "                                    hjust = 0.5),\n",
    "          legend.text = element_text(size = 5),\n",
    "          legend.title = element_text(size = 6),\n",
    "          legend.key.size = unit(0.4, \"lines\"))\n",
    "  \n",
    "  \n",
    "  best_result_df <- top_results_df %>%\n",
    "    dplyr::arrange(desc(neg_log_p)) %>%\n",
    "    dplyr::ungroup() %>%\n",
    "    dplyr::top_n(n = 1, wt = neg_log_p)\n",
    "  \n",
    "  best_result_list[[t_test_file]] <- best_result_df\n",
    "  line_gg_list[[t_test_file]] <- panel_gg\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot GTEx Sex Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  SUBJID = col_character(),\n",
      "  SEX = col_double(),\n",
      "  AGE = col_character(),\n",
      "  DTHHRDY = col_double()\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>SUBJID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>DTHHRDY</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GTEX-1117F</td><td>2         </td><td>60-69     </td><td>4         </td></tr>\n",
       "\t<tr><td>GTEX-111CU</td><td>1         </td><td>50-59     </td><td>0         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " SUBJID & SEX & AGE & DTHHRDY\\\\\n",
       "\\hline\n",
       "\t GTEX-1117F & 2          & 60-69      & 4         \\\\\n",
       "\t GTEX-111CU & 1          & 50-59      & 0         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "SUBJID | SEX | AGE | DTHHRDY | \n",
       "|---|---|\n",
       "| GTEX-1117F | 2          | 60-69      | 4          | \n",
       "| GTEX-111CU | 1          | 50-59      | 0          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  SUBJID     SEX AGE   DTHHRDY\n",
       "1 GTEX-1117F 2   60-69 4      \n",
       "2 GTEX-111CU 1   50-59 0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file <- file.path(\"..\", \"0.expression-download\", \"download\", \"GTEx_v7_Annotations_SubjectPhenotypesDS.txt\")\n",
    "gtex_sex_df <- readr::read_tsv(file)\n",
    "head(gtex_sex_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>SAMPID</th><th scope=col>SMATSSCR</th><th scope=col>SMCENTER</th><th scope=col>SMPTHNTS</th><th scope=col>SMRIN</th><th scope=col>SMTS</th><th scope=col>SMTSD</th><th scope=col>SMUBRID</th><th scope=col>SMTSISCH</th><th scope=col>SMTSPAX</th><th scope=col>⋯</th><th scope=col>SME1PCTS</th><th scope=col>SMRRNART</th><th scope=col>SME1MPRT</th><th scope=col>SMNUM5CD</th><th scope=col>SMDPMPRT</th><th scope=col>SME2PCTS</th><th scope=col>SUBJID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>DTHHRDY</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GTEX-1117F-0003-SM-58Q7G</td><td>NA                      </td><td>B1                      </td><td>NA                      </td><td>NA                      </td><td>Blood                   </td><td>Whole Blood             </td><td>0013756                 </td><td>1188.0                  </td><td>NA                      </td><td>⋯                       </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>GTEX-1117F              </td><td>2                       </td><td>60-69                   </td><td>4                       </td></tr>\n",
       "\t<tr><td>GTEX-1117F-0003-SM-5DWSB</td><td>NA                      </td><td>B1                      </td><td>NA                      </td><td>NA                      </td><td>Blood                   </td><td>Whole Blood             </td><td>0013756                 </td><td>1188.0                  </td><td>NA                      </td><td>⋯                       </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>NA                      </td><td>GTEX-1117F              </td><td>2                       </td><td>60-69                   </td><td>4                       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " SAMPID & SMATSSCR & SMCENTER & SMPTHNTS & SMRIN & SMTS & SMTSD & SMUBRID & SMTSISCH & SMTSPAX & ⋯ & SME1PCTS & SMRRNART & SME1MPRT & SMNUM5CD & SMDPMPRT & SME2PCTS & SUBJID & SEX & AGE & DTHHRDY\\\\\n",
       "\\hline\n",
       "\t GTEX-1117F-0003-SM-58Q7G & NA                       & B1                       & NA                       & NA                       & Blood                    & Whole Blood              & 0013756                  & 1188.0                   & NA                       & ⋯                        & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & GTEX-1117F               & 2                        & 60-69                    & 4                       \\\\\n",
       "\t GTEX-1117F-0003-SM-5DWSB & NA                       & B1                       & NA                       & NA                       & Blood                    & Whole Blood              & 0013756                  & 1188.0                   & NA                       & ⋯                        & NA                       & NA                       & NA                       & NA                       & NA                       & NA                       & GTEX-1117F               & 2                        & 60-69                    & 4                       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "SAMPID | SMATSSCR | SMCENTER | SMPTHNTS | SMRIN | SMTS | SMTSD | SMUBRID | SMTSISCH | SMTSPAX | ⋯ | SME1PCTS | SMRRNART | SME1MPRT | SMNUM5CD | SMDPMPRT | SME2PCTS | SUBJID | SEX | AGE | DTHHRDY | \n",
       "|---|---|\n",
       "| GTEX-1117F-0003-SM-58Q7G | NA                       | B1                       | NA                       | NA                       | Blood                    | Whole Blood              | 0013756                  | 1188.0                   | NA                       | ⋯                        | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | GTEX-1117F               | 2                        | 60-69                    | 4                        | \n",
       "| GTEX-1117F-0003-SM-5DWSB | NA                       | B1                       | NA                       | NA                       | Blood                    | Whole Blood              | 0013756                  | 1188.0                   | NA                       | ⋯                        | NA                       | NA                       | NA                       | NA                       | NA                       | NA                       | GTEX-1117F               | 2                        | 60-69                    | 4                        | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  SAMPID                   SMATSSCR SMCENTER SMPTHNTS SMRIN SMTS  SMTSD      \n",
       "1 GTEX-1117F-0003-SM-58Q7G NA       B1       NA       NA    Blood Whole Blood\n",
       "2 GTEX-1117F-0003-SM-5DWSB NA       B1       NA       NA    Blood Whole Blood\n",
       "  SMUBRID SMTSISCH SMTSPAX ⋯ SME1PCTS SMRRNART SME1MPRT SMNUM5CD SMDPMPRT\n",
       "1 0013756 1188.0   NA      ⋯ NA       NA       NA       NA       NA      \n",
       "2 0013756 1188.0   NA      ⋯ NA       NA       NA       NA       NA      \n",
       "  SME2PCTS SUBJID     SEX AGE   DTHHRDY\n",
       "1 NA       GTEX-1117F 2   60-69 4      \n",
       "2 NA       GTEX-1117F 2   60-69 4      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file <- file.path(\"..\", \"0.expression-download\", \"download\", \"GTEx_v7_Annotations_SampleAttributesDS.txt\")\n",
    "gtex_pheno_df <- readr::read_tsv(file, col_types = readr::cols(.default = readr::col_character(),\n",
    "                                                               \"SMGTC\" = readr::col_character()))\n",
    "\n",
    "full_gtex_ids <- c()\n",
    "for (gtex_id in strsplit(gtex_pheno_df$SAMPID, \"-\")) {\n",
    "  gtex_id_unlist <- unlist(gtex_id)\n",
    "  sample_id <- paste0(gtex_id_unlist[1], \"-\", gtex_id_unlist[2])\n",
    "  full_gtex_ids <- c(full_gtex_ids, sample_id)\n",
    "}\n",
    "\n",
    "gtex_pheno_df$SUBJID <- full_gtex_ids\n",
    "gtex_pheno_df <- gtex_pheno_df %>%\n",
    "    dplyr::left_join(gtex_sex_df, by = \"SUBJID\")\n",
    "\n",
    "head(gtex_pheno_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_gtex_k_dim <- paste(best_result_list[[t_test_results_files[2]]]$z_dim[1])\n",
    "best_gtex_seed <- paste(best_result_list[[t_test_results_files[2]]]$seed[1])\n",
    "best_gtex_feature <- paste0(paste(best_result_list[[t_test_results_files[2]]]$algorithm[1]),\n",
    "                            \"_\", \n",
    "                            paste(best_result_list[[t_test_results_files[2]]]$feature_num[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"200\"\n",
      "[1] \"451283\"\n",
      "[1] \"nmf_111\"\n"
     ]
    }
   ],
   "source": [
    "print(best_gtex_k_dim)\n",
    "print(best_gtex_seed)\n",
    "print(best_gtex_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>sample_id</th><th scope=col>nmf_111</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GTEX-ZTX8-1126-SM-51MRM     </td><td>0.0000000                   </td></tr>\n",
       "\t<tr><td>GTEX-Y3IK-2426-SM-4WWDU     </td><td>0.0000000                   </td></tr>\n",
       "\t<tr><td>GTEX-X62O-0826-SM-46MW8     </td><td>0.3224531                   </td></tr>\n",
       "\t<tr><td>GTEX-13O3O-0011-R5b-SM-5KM44</td><td>0.0000000                   </td></tr>\n",
       "\t<tr><td>GTEX-X15G-1926-SM-4PQZQ     </td><td>0.0000000                   </td></tr>\n",
       "\t<tr><td>GTEX-13OVJ-0011-R7a-SM-5L3G1</td><td>0.0000000                   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " sample\\_id & nmf\\_111\\\\\n",
       "\\hline\n",
       "\t GTEX-ZTX8-1126-SM-51MRM      & 0.0000000                   \\\\\n",
       "\t GTEX-Y3IK-2426-SM-4WWDU      & 0.0000000                   \\\\\n",
       "\t GTEX-X62O-0826-SM-46MW8      & 0.3224531                   \\\\\n",
       "\t GTEX-13O3O-0011-R5b-SM-5KM44 & 0.0000000                   \\\\\n",
       "\t GTEX-X15G-1926-SM-4PQZQ      & 0.0000000                   \\\\\n",
       "\t GTEX-13OVJ-0011-R7a-SM-5L3G1 & 0.0000000                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "sample_id | nmf_111 | \n",
       "|---|---|---|---|---|---|\n",
       "| GTEX-ZTX8-1126-SM-51MRM      | 0.0000000                    | \n",
       "| GTEX-Y3IK-2426-SM-4WWDU      | 0.0000000                    | \n",
       "| GTEX-X62O-0826-SM-46MW8      | 0.3224531                    | \n",
       "| GTEX-13O3O-0011-R5b-SM-5KM44 | 0.0000000                    | \n",
       "| GTEX-X15G-1926-SM-4PQZQ      | 0.0000000                    | \n",
       "| GTEX-13OVJ-0011-R7a-SM-5L3G1 | 0.0000000                    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  sample_id                    nmf_111  \n",
       "1 GTEX-ZTX8-1126-SM-51MRM      0.0000000\n",
       "2 GTEX-Y3IK-2426-SM-4WWDU      0.0000000\n",
       "3 GTEX-X62O-0826-SM-46MW8      0.3224531\n",
       "4 GTEX-13O3O-0011-R5b-SM-5KM44 0.0000000\n",
       "5 GTEX-X15G-1926-SM-4PQZQ      0.0000000\n",
       "6 GTEX-13OVJ-0011-R7a-SM-5L3G1 0.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_file <- file.path(\"..\",\n",
    "                          \"2.sequential-compression\",\n",
    "                          \"results\",\n",
    "                          \"GTEX_results\",\n",
    "                          \"ensemble_z_matrices\",\n",
    "                          paste0(\"gtex_components_\", best_gtex_k_dim),\n",
    "                          paste0(\"model_\", best_gtex_seed, \"_z_test_matrix.tsv.gz\"))\n",
    "\n",
    "top_gtex_feature_df <- readr::read_tsv(feature_file,\n",
    "                                       col_types = readr::cols(\n",
    "                                           .default = readr::col_double(),\n",
    "                                           sample_id = readr::col_character()\n",
    "                                       )\n",
    "                                      ) %>%\n",
    "    dplyr::select(sample_id, !!best_gtex_feature)\n",
    "\n",
    "head(top_gtex_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Adrenal Gland'</li>\n",
       "\t<li>'Blood Vessel'</li>\n",
       "\t<li>'Brain'</li>\n",
       "\t<li>'Breast'</li>\n",
       "\t<li>'Colon'</li>\n",
       "\t<li>'Esophagus'</li>\n",
       "\t<li>'Heart'</li>\n",
       "\t<li>'Lung'</li>\n",
       "\t<li>'Muscle'</li>\n",
       "\t<li>'Pancreas'</li>\n",
       "\t<li>'Skin'</li>\n",
       "\t<li>'Spleen'</li>\n",
       "\t<li>'Stomach'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Adrenal Gland'\n",
       "\\item 'Blood Vessel'\n",
       "\\item 'Brain'\n",
       "\\item 'Breast'\n",
       "\\item 'Colon'\n",
       "\\item 'Esophagus'\n",
       "\\item 'Heart'\n",
       "\\item 'Lung'\n",
       "\\item 'Muscle'\n",
       "\\item 'Pancreas'\n",
       "\\item 'Skin'\n",
       "\\item 'Spleen'\n",
       "\\item 'Stomach'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Adrenal Gland'\n",
       "2. 'Blood Vessel'\n",
       "3. 'Brain'\n",
       "4. 'Breast'\n",
       "5. 'Colon'\n",
       "6. 'Esophagus'\n",
       "7. 'Heart'\n",
       "8. 'Lung'\n",
       "9. 'Muscle'\n",
       "10. 'Pancreas'\n",
       "11. 'Skin'\n",
       "12. 'Spleen'\n",
       "13. 'Stomach'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Adrenal Gland\" \"Blood Vessel\"  \"Brain\"         \"Breast\"       \n",
       " [5] \"Colon\"         \"Esophagus\"     \"Heart\"         \"Lung\"         \n",
       " [9] \"Muscle\"        \"Pancreas\"      \"Skin\"          \"Spleen\"       \n",
       "[13] \"Stomach\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balance_file <- file.path(\"results\", \"balanced_gtex_tissues.tsv\")\n",
    "balanced_gtex_tissues <- readr::read_tsv(balance_file, col_types = readr::cols()) %>%\n",
    "    dplyr::pull(SMTS)\n",
    "\n",
    "balanced_gtex_tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_gtex_feature_df <- top_gtex_feature_df %>%\n",
    "    dplyr::left_join(gtex_pheno_df, by = c(\"sample_id\" = \"SAMPID\")) %>%\n",
    "    dplyr::filter(SMTS %in% balanced_gtex_tissues)\n",
    "\n",
    "top_gtex_feature_df$SEX <- dplyr::recode_factor(top_gtex_feature_df$SEX, `1` = \"Male\", `2` = \"Female\")\n",
    "top_gtex_feature_df$SEX <- factor(top_gtex_feature_df$SEX, levels = c(\"Female\", \"Male\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWelch Two Sample t-test\n",
       "\n",
       "data:  male_scores and female_scores\n",
       "t = 44.507, df = 499.01, p-value < 2.2e-16\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.3461038 0.3780718\n",
       "sample estimates:\n",
       "   mean of x    mean of y \n",
       "3.621372e-01 4.940517e-05 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_scores <- top_gtex_feature_df %>%\n",
    "    dplyr::filter(SEX == \"Male\") %>%\n",
    "    dplyr::pull(!!best_gtex_feature)\n",
    "female_scores <- top_gtex_feature_df %>%\n",
    "    dplyr::filter(SEX == \"Female\") %>%\n",
    "    dplyr::pull(!!best_gtex_feature)\n",
    "\n",
    "gtex_ttest_result <- t.test(male_scores, female_scores, var.equal = FALSE, paired = FALSE)\n",
    "gtex_ttest_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x7f9ugiIsoxO4R5EZFiPWDI62/526kB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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gtex_sex_gg <- ggplot(top_gtex_feature_df,\n",
    "                      aes_string(y = best_gtex_feature,\n",
    "                                 x = \"SEX\",\n",
    "                                 fill = \"SEX\")) +\n",
    "  geom_boxplot(alpha = 0.5,\n",
    "               outlier.alpha = 0) +\n",
    "  geom_jitter(aes(color = SEX),\n",
    "              size = 0.2,\n",
    "              alpha = 0.6,\n",
    "              width = 0.3) +\n",
    "  scale_fill_manual(labels = c(\"Male\" = \"Male\",\n",
    "                               \"Female\" = \"Female\"),\n",
    "                    values = c(\"Male\" = \"#42c8f4\",\n",
    "                               \"Female\" = \"#f489c9\")) +\n",
    "  scale_color_manual(labels = c(\"Male\" = \"Male\",\n",
    "                                \"Female\" = \"Female\"),\n",
    "                     values = c(\"Male\" = \"#42c8f4\",\n",
    "                                \"Female\" = \"#f489c9\")) +\n",
    "  theme_bw() +\n",
    "  ylab(\"NMF Feature 111 (k = 200)\") +\n",
    "  xlab(\"\") +\n",
    "  ylim(c(-0.5, 1.7)) +\n",
    "  ggtitle(\"GTEx Sex\") +\n",
    "  theme(axis.title.x = element_text(size = 7),\n",
    "        axis.title.y = element_text(size = 7),\n",
    "        axis.text.x = element_text(size = 6),\n",
    "        axis.text.y = element_text(size = 6),\n",
    "        plot.title = element_text(size = 8,\n",
    "                                  hjust = 0.5),\n",
    "        legend.position = \"none\") +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_gtex_feature_df[, best_gtex_feature]) + 0.15,\n",
    "                     xend=2,\n",
    "                     yend=max(top_gtex_feature_df[, best_gtex_feature]) + 0.15),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_gtex_feature_df[, best_gtex_feature]) + 0.15,\n",
    "                     xend=1,\n",
    "                     yend=max(top_gtex_feature_df[, best_gtex_feature])),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=2,\n",
    "                     y=max(top_gtex_feature_df[, best_gtex_feature]) + 0.15,\n",
    "                     xend=2,\n",
    "                     yend=max(top_gtex_feature_df[, best_gtex_feature])),\n",
    "                 size=0.4) +\n",
    "    geom_text(x=1.5,\n",
    "              y = (max(top_gtex_feature_df[, best_gtex_feature]) + 0.25),\n",
    "              size = 3.0,\n",
    "              label = paste0(\"t = \",\n",
    "                            round(gtex_ttest_result$statistic, 1),\n",
    "                            \"; p\",\n",
    "                            format.pval(gtex_ttest_result$p.value, digits = 2)))\n",
    "\n",
    "gtex_sex_gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot TCGA Sex Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New names:\n",
      "* `` -> `..1`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 11160    34\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>..1</th><th scope=col>bcr_patient_barcode</th><th scope=col>type</th><th scope=col>age_at_initial_pathologic_diagnosis</th><th scope=col>gender</th><th scope=col>race</th><th scope=col>ajcc_pathologic_tumor_stage</th><th scope=col>clinical_stage</th><th scope=col>histological_type</th><th scope=col>histological_grade</th><th scope=col>⋯</th><th scope=col>residual_tumor</th><th scope=col>OS</th><th scope=col>OS.time</th><th scope=col>DSS</th><th scope=col>DSS.time</th><th scope=col>DFI</th><th scope=col>DFI.time</th><th scope=col>PFI</th><th scope=col>PFI.time</th><th scope=col>Redaction</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                   </td><td>TCGA-OR-A5J1                        </td><td>ACC                                 </td><td>58                                  </td><td>MALE                                </td><td>WHITE                               </td><td>Stage II                            </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>1                                   </td><td>1355                                </td><td>1                                   </td><td>1355                                </td><td>1                                   </td><td>754                                 </td><td>1                                   </td><td>754                                 </td><td>NA                                  </td></tr>\n",
       "\t<tr><td>2                                   </td><td>TCGA-OR-A5J2                        </td><td>ACC                                 </td><td>44                                  </td><td>FEMALE                              </td><td>WHITE                               </td><td>Stage IV                            </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>1                                   </td><td>1677                                </td><td>1                                   </td><td>1677                                </td><td>NA                                  </td><td>NA                                  </td><td>1                                   </td><td>289                                 </td><td>NA                                  </td></tr>\n",
       "\t<tr><td>3                                   </td><td>TCGA-OR-A5J3                        </td><td>ACC                                 </td><td>23                                  </td><td>FEMALE                              </td><td>WHITE                               </td><td>Stage III                           </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>0                                   </td><td>2091                                </td><td>0                                   </td><td>2091                                </td><td>1                                   </td><td>53                                  </td><td>1                                   </td><td>53                                  </td><td>NA                                  </td></tr>\n",
       "\t<tr><td>4                                   </td><td>TCGA-OR-A5J4                        </td><td>ACC                                 </td><td>23                                  </td><td>FEMALE                              </td><td>WHITE                               </td><td>Stage IV                            </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>1                                   </td><td>423                                 </td><td>1                                   </td><td>423                                 </td><td>NA                                  </td><td>NA                                  </td><td>1                                   </td><td>126                                 </td><td>NA                                  </td></tr>\n",
       "\t<tr><td>5                                   </td><td>TCGA-OR-A5J5                        </td><td>ACC                                 </td><td>30                                  </td><td>MALE                                </td><td>WHITE                               </td><td>Stage III                           </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>1                                   </td><td>365                                 </td><td>1                                   </td><td>365                                 </td><td>NA                                  </td><td>NA                                  </td><td>1                                   </td><td>50                                  </td><td>NA                                  </td></tr>\n",
       "\t<tr><td>6                                   </td><td>TCGA-OR-A5J6                        </td><td>ACC                                 </td><td>29                                  </td><td>FEMALE                              </td><td>BLACK OR AFRICAN AMERICAN           </td><td>Stage II                            </td><td>[Not Applicable]                    </td><td>Adrenocortical carcinoma- Usual Type</td><td>[Not Available]                     </td><td>⋯                                   </td><td>NA                                  </td><td>0                                   </td><td>2703                                </td><td>0                                   </td><td>2703                                </td><td>0                                   </td><td>2703                                </td><td>0                                   </td><td>2703                                </td><td>NA                                  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllll}\n",
       " ..1 & bcr\\_patient\\_barcode & type & age\\_at\\_initial\\_pathologic\\_diagnosis & gender & race & ajcc\\_pathologic\\_tumor\\_stage & clinical\\_stage & histological\\_type & histological\\_grade & ⋯ & residual\\_tumor & OS & OS.time & DSS & DSS.time & DFI & DFI.time & PFI & PFI.time & Redaction\\\\\n",
       "\\hline\n",
       "\t 1                                    & TCGA-OR-A5J1                         & ACC                                  & 58                                   & MALE                                 & WHITE                                & Stage II                             & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 1                                    & 1355                                 & 1                                    & 1355                                 & 1                                    & 754                                  & 1                                    & 754                                  & NA                                  \\\\\n",
       "\t 2                                    & TCGA-OR-A5J2                         & ACC                                  & 44                                   & FEMALE                               & WHITE                                & Stage IV                             & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 1                                    & 1677                                 & 1                                    & 1677                                 & NA                                   & NA                                   & 1                                    & 289                                  & NA                                  \\\\\n",
       "\t 3                                    & TCGA-OR-A5J3                         & ACC                                  & 23                                   & FEMALE                               & WHITE                                & Stage III                            & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 0                                    & 2091                                 & 0                                    & 2091                                 & 1                                    & 53                                   & 1                                    & 53                                   & NA                                  \\\\\n",
       "\t 4                                    & TCGA-OR-A5J4                         & ACC                                  & 23                                   & FEMALE                               & WHITE                                & Stage IV                             & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 1                                    & 423                                  & 1                                    & 423                                  & NA                                   & NA                                   & 1                                    & 126                                  & NA                                  \\\\\n",
       "\t 5                                    & TCGA-OR-A5J5                         & ACC                                  & 30                                   & MALE                                 & WHITE                                & Stage III                            & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 1                                    & 365                                  & 1                                    & 365                                  & NA                                   & NA                                   & 1                                    & 50                                   & NA                                  \\\\\n",
       "\t 6                                    & TCGA-OR-A5J6                         & ACC                                  & 29                                   & FEMALE                               & BLACK OR AFRICAN AMERICAN            & Stage II                             & {[}Not Applicable{]}                 & Adrenocortical carcinoma- Usual Type & {[}Not Available{]}                  & ⋯                                    & NA                                   & 0                                    & 2703                                 & 0                                    & 2703                                 & 0                                    & 2703                                 & 0                                    & 2703                                 & NA                                  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "..1 | bcr_patient_barcode | type | age_at_initial_pathologic_diagnosis | gender | race | ajcc_pathologic_tumor_stage | clinical_stage | histological_type | histological_grade | ⋯ | residual_tumor | OS | OS.time | DSS | DSS.time | DFI | DFI.time | PFI | PFI.time | Redaction | \n",
       "|---|---|---|---|---|---|\n",
       "| 1                                    | TCGA-OR-A5J1                         | ACC                                  | 58                                   | MALE                                 | WHITE                                | Stage II                             | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 1                                    | 1355                                 | 1                                    | 1355                                 | 1                                    | 754                                  | 1                                    | 754                                  | NA                                   | \n",
       "| 2                                    | TCGA-OR-A5J2                         | ACC                                  | 44                                   | FEMALE                               | WHITE                                | Stage IV                             | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 1                                    | 1677                                 | 1                                    | 1677                                 | NA                                   | NA                                   | 1                                    | 289                                  | NA                                   | \n",
       "| 3                                    | TCGA-OR-A5J3                         | ACC                                  | 23                                   | FEMALE                               | WHITE                                | Stage III                            | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 0                                    | 2091                                 | 0                                    | 2091                                 | 1                                    | 53                                   | 1                                    | 53                                   | NA                                   | \n",
       "| 4                                    | TCGA-OR-A5J4                         | ACC                                  | 23                                   | FEMALE                               | WHITE                                | Stage IV                             | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 1                                    | 423                                  | 1                                    | 423                                  | NA                                   | NA                                   | 1                                    | 126                                  | NA                                   | \n",
       "| 5                                    | TCGA-OR-A5J5                         | ACC                                  | 30                                   | MALE                                 | WHITE                                | Stage III                            | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 1                                    | 365                                  | 1                                    | 365                                  | NA                                   | NA                                   | 1                                    | 50                                   | NA                                   | \n",
       "| 6                                    | TCGA-OR-A5J6                         | ACC                                  | 29                                   | FEMALE                               | BLACK OR AFRICAN AMERICAN            | Stage II                             | [Not Applicable]                     | Adrenocortical carcinoma- Usual Type | [Not Available]                      | ⋯                                    | NA                                   | 0                                    | 2703                                 | 0                                    | 2703                                 | 0                                    | 2703                                 | 0                                    | 2703                                 | NA                                   | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  ..1 bcr_patient_barcode type age_at_initial_pathologic_diagnosis gender\n",
       "1 1   TCGA-OR-A5J1        ACC  58                                  MALE  \n",
       "2 2   TCGA-OR-A5J2        ACC  44                                  FEMALE\n",
       "3 3   TCGA-OR-A5J3        ACC  23                                  FEMALE\n",
       "4 4   TCGA-OR-A5J4        ACC  23                                  FEMALE\n",
       "5 5   TCGA-OR-A5J5        ACC  30                                  MALE  \n",
       "6 6   TCGA-OR-A5J6        ACC  29                                  FEMALE\n",
       "  race                      ajcc_pathologic_tumor_stage clinical_stage  \n",
       "1 WHITE                     Stage II                    [Not Applicable]\n",
       "2 WHITE                     Stage IV                    [Not Applicable]\n",
       "3 WHITE                     Stage III                   [Not Applicable]\n",
       "4 WHITE                     Stage IV                    [Not Applicable]\n",
       "5 WHITE                     Stage III                   [Not Applicable]\n",
       "6 BLACK OR AFRICAN AMERICAN Stage II                    [Not Applicable]\n",
       "  histological_type                    histological_grade ⋯ residual_tumor OS\n",
       "1 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             1 \n",
       "2 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             1 \n",
       "3 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             0 \n",
       "4 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             1 \n",
       "5 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             1 \n",
       "6 Adrenocortical carcinoma- Usual Type [Not Available]    ⋯ NA             0 \n",
       "  OS.time DSS DSS.time DFI DFI.time PFI PFI.time Redaction\n",
       "1 1355    1   1355     1   754      1   754      NA       \n",
       "2 1677    1   1677     NA  NA       1   289      NA       \n",
       "3 2091    0   2091     1   53       1   53       NA       \n",
       "4 423     1   423      NA  NA       1   126      NA       \n",
       "5 365     1   365      NA  NA       1   50       NA       \n",
       "6 2703    0   2703     0   2703     0   2703     NA       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load TCGA phenotype data\n",
    "file <- file.path(\"..\", \"0.expression-download\", \"download\", \"TCGA-CDR-SupplementalTableS1.xlsx\")\n",
    "tcga_pheno_df <- readxl::read_xlsx(file, sheet=1, col_types = \"text\")\n",
    "\n",
    "print(dim(tcga_pheno_df))\n",
    "head(tcga_pheno_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_tcga_k_dim <- paste(best_result_list[[t_test_results_files[3]]]$z_dim)\n",
    "best_tcga_seed <- paste(best_result_list[[t_test_results_files[3]]]$seed)\n",
    "best_tcga_feature <- paste0(paste(best_result_list[[t_test_results_files[3]]]$algorithm),\n",
    "                            \"_\", \n",
    "                            paste(best_result_list[[t_test_results_files[3]]]$feature_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"90\"\n",
      "[1] \"165158\"\n",
      "[1] \"ica_53\"\n"
     ]
    }
   ],
   "source": [
    "print(best_tcga_k_dim)\n",
    "print(best_tcga_seed)\n",
    "print(best_tcga_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>sample_id</th><th scope=col>ica_53</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TCGA-CN-5365-01</td><td>-0.008143607   </td></tr>\n",
       "\t<tr><td>TCGA-LP-A7HU-01</td><td>-0.016654754   </td></tr>\n",
       "\t<tr><td>TCGA-22-5491-11</td><td> 0.004007585   </td></tr>\n",
       "\t<tr><td>TCGA-CS-6667-01</td><td> 0.001055000   </td></tr>\n",
       "\t<tr><td>TCGA-20-1684-01</td><td> 0.047395277   </td></tr>\n",
       "\t<tr><td>TCGA-AK-3453-01</td><td> 0.029171493   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " sample\\_id & ica\\_53\\\\\n",
       "\\hline\n",
       "\t TCGA-CN-5365-01 & -0.008143607   \\\\\n",
       "\t TCGA-LP-A7HU-01 & -0.016654754   \\\\\n",
       "\t TCGA-22-5491-11 &  0.004007585   \\\\\n",
       "\t TCGA-CS-6667-01 &  0.001055000   \\\\\n",
       "\t TCGA-20-1684-01 &  0.047395277   \\\\\n",
       "\t TCGA-AK-3453-01 &  0.029171493   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "sample_id | ica_53 | \n",
       "|---|---|---|---|---|---|\n",
       "| TCGA-CN-5365-01 | -0.008143607    | \n",
       "| TCGA-LP-A7HU-01 | -0.016654754    | \n",
       "| TCGA-22-5491-11 |  0.004007585    | \n",
       "| TCGA-CS-6667-01 |  0.001055000    | \n",
       "| TCGA-20-1684-01 |  0.047395277    | \n",
       "| TCGA-AK-3453-01 |  0.029171493    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  sample_id       ica_53      \n",
       "1 TCGA-CN-5365-01 -0.008143607\n",
       "2 TCGA-LP-A7HU-01 -0.016654754\n",
       "3 TCGA-22-5491-11  0.004007585\n",
       "4 TCGA-CS-6667-01  0.001055000\n",
       "5 TCGA-20-1684-01  0.047395277\n",
       "6 TCGA-AK-3453-01  0.029171493"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_file <- file.path(\"..\",\n",
    "                          \"2.sequential-compression\",\n",
    "                          \"results\",\n",
    "                          \"TCGA_results\",\n",
    "                          \"ensemble_z_matrices\",\n",
    "                          paste0(\"tcga_components_\", best_tcga_k_dim),\n",
    "                          paste0(\"model_\", best_tcga_seed, \"_z_test_matrix.tsv.gz\"))\n",
    "\n",
    "top_tcga_feature_df <- readr::read_tsv(feature_file,\n",
    "                                       col_types = readr::cols(\n",
    "                                           .default = readr::col_double(),\n",
    "                                           sample_id = readr::col_character()\n",
    "                                       )\n",
    ") %>%\n",
    "  dplyr::select(sample_id, !!best_tcga_feature)\n",
    "\n",
    "head(top_tcga_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'COAD'</li>\n",
       "\t<li>'DLBC'</li>\n",
       "\t<li>'GBM'</li>\n",
       "\t<li>'HNSC'</li>\n",
       "\t<li>'KICH'</li>\n",
       "\t<li>'KIRC'</li>\n",
       "\t<li>'KIRP'</li>\n",
       "\t<li>'LAML'</li>\n",
       "\t<li>'LGG'</li>\n",
       "\t<li>'LUAD'</li>\n",
       "\t<li>'PCPG'</li>\n",
       "\t<li>'READ'</li>\n",
       "\t<li>'SARC'</li>\n",
       "\t<li>'SKCM'</li>\n",
       "\t<li>'THYM'</li>\n",
       "\t<li>'UVM'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'COAD'\n",
       "\\item 'DLBC'\n",
       "\\item 'GBM'\n",
       "\\item 'HNSC'\n",
       "\\item 'KICH'\n",
       "\\item 'KIRC'\n",
       "\\item 'KIRP'\n",
       "\\item 'LAML'\n",
       "\\item 'LGG'\n",
       "\\item 'LUAD'\n",
       "\\item 'PCPG'\n",
       "\\item 'READ'\n",
       "\\item 'SARC'\n",
       "\\item 'SKCM'\n",
       "\\item 'THYM'\n",
       "\\item 'UVM'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'COAD'\n",
       "2. 'DLBC'\n",
       "3. 'GBM'\n",
       "4. 'HNSC'\n",
       "5. 'KICH'\n",
       "6. 'KIRC'\n",
       "7. 'KIRP'\n",
       "8. 'LAML'\n",
       "9. 'LGG'\n",
       "10. 'LUAD'\n",
       "11. 'PCPG'\n",
       "12. 'READ'\n",
       "13. 'SARC'\n",
       "14. 'SKCM'\n",
       "15. 'THYM'\n",
       "16. 'UVM'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"COAD\" \"DLBC\" \"GBM\"  \"HNSC\" \"KICH\" \"KIRC\" \"KIRP\" \"LAML\" \"LGG\"  \"LUAD\"\n",
       "[11] \"PCPG\" \"READ\" \"SARC\" \"SKCM\" \"THYM\" \"UVM\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balance_file <- file.path(\"results\", \"balanced_tcga_tissues.tsv\")\n",
    "balanced_tcga_tissues <- readr::read_tsv(balance_file, col_types = readr::cols()) %>%\n",
    "    dplyr::pull(type)\n",
    "\n",
    "balanced_tcga_tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>sample_id</th><th scope=col>ica_53</th><th scope=col>tcga_id</th><th scope=col>..1</th><th scope=col>type</th><th scope=col>age_at_initial_pathologic_diagnosis</th><th scope=col>gender</th><th scope=col>race</th><th scope=col>ajcc_pathologic_tumor_stage</th><th scope=col>clinical_stage</th><th scope=col>⋯</th><th scope=col>residual_tumor</th><th scope=col>OS</th><th scope=col>OS.time</th><th scope=col>DSS</th><th scope=col>DSS.time</th><th scope=col>DFI</th><th scope=col>DFI.time</th><th scope=col>PFI</th><th scope=col>PFI.time</th><th scope=col>Redaction</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TCGA-CN-5365-01 </td><td>-0.008143607    </td><td>TCGA-CN-5365    </td><td>3329            </td><td>HNSC            </td><td>38              </td><td>Male            </td><td>WHITE           </td><td>Stage IVB       </td><td>Stage IVC       </td><td>⋯               </td><td>NA              </td><td>1               </td><td>351             </td><td>1               </td><td>351             </td><td>NA              </td><td>NA              </td><td>1               </td><td>351             </td><td>NA              </td></tr>\n",
       "\t<tr><td>TCGA-CS-6667-01 </td><td> 0.001055000    </td><td>TCGA-CS-6667    </td><td>4927            </td><td>LGG             </td><td>39              </td><td>Female          </td><td>WHITE           </td><td>[Not Available] </td><td>[Not Available] </td><td>⋯               </td><td>NA              </td><td>0               </td><td>1469            </td><td>0               </td><td>1469            </td><td>NA              </td><td>NA              </td><td>0               </td><td>1469            </td><td>NA              </td></tr>\n",
       "\t<tr><td>TCGA-AK-3453-01 </td><td> 0.029171493    </td><td>TCGA-AK-3453    </td><td>3954            </td><td>KIRC            </td><td>58              </td><td>Female          </td><td>WHITE           </td><td>Stage II        </td><td>[Not Applicable]</td><td>⋯               </td><td>NA              </td><td>0               </td><td>2531            </td><td>0               </td><td>2531            </td><td>0               </td><td>2531            </td><td>0               </td><td>2531            </td><td>NA              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllll}\n",
       " sample\\_id & ica\\_53 & tcga\\_id & ..1 & type & age\\_at\\_initial\\_pathologic\\_diagnosis & gender & race & ajcc\\_pathologic\\_tumor\\_stage & clinical\\_stage & ⋯ & residual\\_tumor & OS & OS.time & DSS & DSS.time & DFI & DFI.time & PFI & PFI.time & Redaction\\\\\n",
       "\\hline\n",
       "\t TCGA-CN-5365-01  & -0.008143607     & TCGA-CN-5365     & 3329             & HNSC             & 38               & Male             & WHITE            & Stage IVB        & Stage IVC        & ⋯                & NA               & 1                & 351              & 1                & 351              & NA               & NA               & 1                & 351              & NA              \\\\\n",
       "\t TCGA-CS-6667-01      &  0.001055000         & TCGA-CS-6667         & 4927                 & LGG                  & 39                   & Female               & WHITE                & {[}Not Available{]}  & {[}Not Available{]}  & ⋯                    & NA                   & 0                    & 1469                 & 0                    & 1469                 & NA                   & NA                   & 0                    & 1469                 & NA                  \\\\\n",
       "\t TCGA-AK-3453-01      &  0.029171493         & TCGA-AK-3453         & 3954                 & KIRC                 & 58                   & Female               & WHITE                & Stage II             & {[}Not Applicable{]} & ⋯                    & NA                   & 0                    & 2531                 & 0                    & 2531                 & 0                    & 2531                 & 0                    & 2531                 & NA                  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "sample_id | ica_53 | tcga_id | ..1 | type | age_at_initial_pathologic_diagnosis | gender | race | ajcc_pathologic_tumor_stage | clinical_stage | ⋯ | residual_tumor | OS | OS.time | DSS | DSS.time | DFI | DFI.time | PFI | PFI.time | Redaction | \n",
       "|---|---|---|\n",
       "| TCGA-CN-5365-01  | -0.008143607     | TCGA-CN-5365     | 3329             | HNSC             | 38               | Male             | WHITE            | Stage IVB        | Stage IVC        | ⋯                | NA               | 1                | 351              | 1                | 351              | NA               | NA               | 1                | 351              | NA               | \n",
       "| TCGA-CS-6667-01  |  0.001055000     | TCGA-CS-6667     | 4927             | LGG              | 39               | Female           | WHITE            | [Not Available]  | [Not Available]  | ⋯                | NA               | 0                | 1469             | 0                | 1469             | NA               | NA               | 0                | 1469             | NA               | \n",
       "| TCGA-AK-3453-01  |  0.029171493     | TCGA-AK-3453     | 3954             | KIRC             | 58               | Female           | WHITE            | Stage II         | [Not Applicable] | ⋯                | NA               | 0                | 2531             | 0                | 2531             | 0                | 2531             | 0                | 2531             | NA               | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  sample_id       ica_53       tcga_id      ..1  type\n",
       "1 TCGA-CN-5365-01 -0.008143607 TCGA-CN-5365 3329 HNSC\n",
       "2 TCGA-CS-6667-01  0.001055000 TCGA-CS-6667 4927 LGG \n",
       "3 TCGA-AK-3453-01  0.029171493 TCGA-AK-3453 3954 KIRC\n",
       "  age_at_initial_pathologic_diagnosis gender race  ajcc_pathologic_tumor_stage\n",
       "1 38                                  Male   WHITE Stage IVB                  \n",
       "2 39                                  Female WHITE [Not Available]            \n",
       "3 58                                  Female WHITE Stage II                   \n",
       "  clinical_stage   ⋯ residual_tumor OS OS.time DSS DSS.time DFI DFI.time PFI\n",
       "1 Stage IVC        ⋯ NA             1  351     1   351      NA  NA       1  \n",
       "2 [Not Available]  ⋯ NA             0  1469    0   1469     NA  NA       0  \n",
       "3 [Not Applicable] ⋯ NA             0  2531    0   2531     0   2531     0  \n",
       "  PFI.time Redaction\n",
       "1 351      NA       \n",
       "2 1469     NA       \n",
       "3 2531     NA       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_tcga_ids <- c()\n",
    "for (tcga_id in strsplit(top_tcga_feature_df$sample_id, \"-\")) {\n",
    "  tcga_id_unlist <- unlist(tcga_id)\n",
    "  sample_id <- paste0(tcga_id_unlist[1], \"-\", tcga_id_unlist[2], \"-\", tcga_id_unlist[3])\n",
    "  full_tcga_ids <- c(full_tcga_ids, sample_id)\n",
    "}\n",
    "\n",
    "top_tcga_feature_df$tcga_id <- full_tcga_ids\n",
    "top_tcga_feature_df <- top_tcga_feature_df %>%\n",
    "  dplyr::left_join(tcga_pheno_df, by = c(\"tcga_id\" = \"bcr_patient_barcode\"))\n",
    "\n",
    "top_tcga_feature_df$gender[is.na(top_tcga_feature_df$gender)] <- \"Unlabeled\"\n",
    "top_tcga_feature_df$gender <- dplyr::recode_factor(top_tcga_feature_df$gender,\n",
    "                                                   \"MALE\" = \"Male\",\n",
    "                                                   \"FEMALE\" = \"Female\")\n",
    "top_tcga_feature_df$gender <- factor(top_tcga_feature_df$gender,\n",
    "                                     levels = c(\"Female\", \"Male\", \"Unlabeled\"))\n",
    "\n",
    "top_tcga_feature_df <- top_tcga_feature_df %>%\n",
    "    dplyr::filter(type %in% balanced_tcga_tissues)\n",
    "\n",
    "head(top_tcga_feature_df, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWelch Two Sample t-test\n",
       "\n",
       "data:  male_scores and female_scores\n",
       "t = 4.8641, df = 451.8, p-value = 1.591e-06\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.003762159 0.008863171\n",
       "sample estimates:\n",
       "   mean of x    mean of y \n",
       " 0.001153948 -0.005158717 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_scores <- top_tcga_feature_df %>%\n",
    "    dplyr::filter(gender == \"Male\") %>%\n",
    "    dplyr::pull(!!best_tcga_feature)\n",
    "female_scores <- top_tcga_feature_df %>%\n",
    "    dplyr::filter(gender == \"Female\") %>%\n",
    "    dplyr::pull(!!best_tcga_feature)\n",
    "\n",
    "tcga_ttest_result <- t.test(male_scores, female_scores, var.equal = FALSE, paired = FALSE)\n",
    "tcga_ttest_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RQZYz0lYkNHRDJajdVaONry5+xr7NJO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++MS737pznc1qcS6nJkff9ms/1pRdEWhb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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcga_sex_gg <- ggplot(top_tcga_feature_df,\n",
    "                      aes_string(y = best_tcga_feature,\n",
    "                                 x = \"gender\",\n",
    "                                 fill = \"gender\")) +\n",
    "  geom_boxplot(alpha = 0.5,\n",
    "               outlier.alpha = 0) +\n",
    "  geom_jitter(aes(color = gender),\n",
    "              size = 0.2,\n",
    "              alpha = 0.6,\n",
    "              width = 0.3) +\n",
    "  scale_fill_manual(labels = c(\"Male\" = \"Male\",\n",
    "                               \"Female\" = \"Female\",\n",
    "                               \"Unlabeled\" = \"Unlabeled\"),\n",
    "                    values = c(\"Male\" = \"#42c8f4\",\n",
    "                               \"Female\" = \"#f489c9\",\n",
    "                               \"Unlabeled\" = \"grey25\")) +\n",
    "  scale_color_manual(labels = c(\"Male\" = \"Male\",\n",
    "                                \"Female\" = \"Female\",\n",
    "                                \"Unlabeled\" = \"Unlabeled\"),\n",
    "                     values = c(\"Male\" = \"#42c8f4\",\n",
    "                                \"Female\" = \"#f489c9\",\n",
    "                                \"Unlabeled\" = \"grey25\")) +\n",
    "  theme_bw() +\n",
    "  ylim(c(-0.075, 0.075)) +\n",
    "  ylab(\"ICA Feature 53 (k = 90)\") +\n",
    "  xlab(\"\") +\n",
    "  ggtitle(\"TCGA Sex\") +\n",
    "  theme(axis.title.x = element_text(size = 7),\n",
    "        axis.title.y = element_text(size = 7),\n",
    "        axis.text.x = element_text(size = 6),\n",
    "        axis.text.y = element_text(size = 6),\n",
    "        plot.title = element_text(size = 8,\n",
    "                                  hjust = 0.5),\n",
    "        legend.position = \"none\") +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_tcga_feature_df[, best_tcga_feature]) + 0.005,\n",
    "                     xend=2,\n",
    "                     yend=max(top_tcga_feature_df[, best_tcga_feature]) + 0.005),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_tcga_feature_df[, best_tcga_feature]) + 0.005,\n",
    "                     xend=1,\n",
    "                     yend=max(top_tcga_feature_df[, best_tcga_feature])),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=2,\n",
    "                     y=max(top_tcga_feature_df[, best_tcga_feature]) + 0.005,\n",
    "                     xend=2,\n",
    "                     yend=max(top_tcga_feature_df[, best_tcga_feature])),\n",
    "                 size=0.4) +\n",
    "    geom_text(x=1.5,\n",
    "              y = (max(top_tcga_feature_df[, best_tcga_feature]) + 0.012),\n",
    "              size = 3.0,\n",
    "              label = paste0(\"t = \",\n",
    "                            round(tcga_ttest_result$statistic, 1),\n",
    "                            \"; p = \",\n",
    "                            format.pval(tcga_ttest_result$p.value, digits = 2)))\n",
    "\n",
    "tcga_sex_gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot TARGET MYCN Amplification Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_target_k_dim <- paste(best_result_list[[t_test_results_files[1]]]$z_dim)\n",
    "best_target_seed <- paste(best_result_list[[t_test_results_files[1]]]$seed)\n",
    "best_target_feature <- paste0(paste(best_result_list[[t_test_results_files[1]]]$algorithm),\n",
    "                              \"_\",\n",
    "                              paste(best_result_list[[t_test_results_files[1]]]$feature_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"50\"\n",
      "[1] \"451283\"\n",
      "[1] \"vae_12\"\n"
     ]
    }
   ],
   "source": [
    "print(best_target_k_dim)\n",
    "print(best_target_seed)\n",
    "print(best_target_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_file <- file.path(\"..\",\n",
    "                          \"2.sequential-compression\",\n",
    "                          \"results\",\n",
    "                          \"TARGET_results\",\n",
    "                          \"ensemble_z_matrices\",\n",
    "                          paste0(\"target_components_\", best_target_k_dim),\n",
    "                          paste0(\"model_\", best_target_seed, \"_z_matrix.tsv.gz\"))\n",
    "\n",
    "top_mycn_feature_df <- readr::read_tsv(feature_file,\n",
    "                                       col_types = readr::cols(\n",
    "                                           .default = readr::col_double(),\n",
    "                                           sample_id = readr::col_character()\n",
    "                                       )\n",
    "                                      ) %>%\n",
    "  dplyr::select(sample_id, !!best_target_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_character(),\n",
      "  `Age at Diagnosis in Days` = col_double(),\n",
      "  `Event Free Survival Time in Days` = col_double(),\n",
      "  `Overall Survival Time in Days` = col_double(),\n",
      "  `Year of Diagnosis` = col_double(),\n",
      "  `Year of Last Follow Up` = col_double(),\n",
      "  `Ploidy Value` = col_double()\n",
      ")\n",
      "See spec(...) for full column specifications.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>sample_id</th><th scope=col>vae_12</th><th scope=col>target_id</th><th scope=col>Gender</th><th scope=col>Race</th><th scope=col>Ethnicity</th><th scope=col>Age at Diagnosis in Days</th><th scope=col>First Event</th><th scope=col>Event Free Survival Time in Days</th><th scope=col>Vital Status</th><th scope=col>⋯</th><th scope=col>Histology</th><th scope=col>Grade</th><th scope=col>MKI</th><th scope=col>Diagnostic Category</th><th scope=col>ICDO</th><th scope=col>ICDO Description</th><th scope=col>COG Risk Group</th><th scope=col>Site Relapse</th><th scope=col>Comment</th><th scope=col>target_update</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TARGET-30-PARSBI-01                                         </td><td>2.575049                                                    </td><td>PARSBI                                                      </td><td>Female                                                      </td><td>White                                                       </td><td>Not Hispanic or Latino                                      </td><td>2390                                                        </td><td>Relapse                                                     </td><td>1377                                                        </td><td>Dead                                                        </td><td>⋯                                                           </td><td>Unfavorable                                                 </td><td>Undifferentiated or Poorly Differentiated                   </td><td>Low                                                         </td><td>Neuroblastoma                                               </td><td>C76.2                                                       </td><td>Abdomen, NOS  Abdominal wall, NOS  Intra-abdominal site, NOS</td><td>High Risk                                                   </td><td>Other metastatic sites                                      </td><td>NA                                                          </td><td>old                                                         </td></tr>\n",
       "\t<tr><td>TARGET-30-PATBMM-01                                         </td><td>3.673986                                                    </td><td>PATBMM                                                      </td><td>Male                                                        </td><td>White                                                       </td><td>Not Hispanic or Latino                                      </td><td>1112                                                        </td><td>Relapse                                                     </td><td> 653                                                        </td><td>Alive                                                       </td><td>⋯                                                           </td><td>Unfavorable                                                 </td><td>Undifferentiated or Poorly Differentiated                   </td><td>Low                                                         </td><td>Ganglioneuroblastoma, nodular                               </td><td>C74.9                                                       </td><td>Adrenal gland, NOS  Suprarenal gland  Adrenal, NOS          </td><td>High Risk                                                   </td><td>Bone                                                        </td><td>NA                                                          </td><td>old                                                         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllll}\n",
       " sample\\_id & vae\\_12 & target\\_id & Gender & Race & Ethnicity & Age at Diagnosis in Days & First Event & Event Free Survival Time in Days & Vital Status & ⋯ & Histology & Grade & MKI & Diagnostic Category & ICDO & ICDO Description & COG Risk Group & Site Relapse & Comment & target\\_update\\\\\n",
       "\\hline\n",
       "\t TARGET-30-PARSBI-01                                          & 2.575049                                                     & PARSBI                                                       & Female                                                       & White                                                        & Not Hispanic or Latino                                       & 2390                                                         & Relapse                                                      & 1377                                                         & Dead                                                         & ⋯                                                            & Unfavorable                                                  & Undifferentiated or Poorly Differentiated                    & Low                                                          & Neuroblastoma                                                & C76.2                                                        & Abdomen, NOS  Abdominal wall, NOS  Intra-abdominal site, NOS & High Risk                                                    & Other metastatic sites                                       & NA                                                           & old                                                         \\\\\n",
       "\t TARGET-30-PATBMM-01                                          & 3.673986                                                     & PATBMM                                                       & Male                                                         & White                                                        & Not Hispanic or Latino                                       & 1112                                                         & Relapse                                                      &  653                                                         & Alive                                                        & ⋯                                                            & Unfavorable                                                  & Undifferentiated or Poorly Differentiated                    & Low                                                          & Ganglioneuroblastoma, nodular                                & C74.9                                                        & Adrenal gland, NOS  Suprarenal gland  Adrenal, NOS           & High Risk                                                    & Bone                                                         & NA                                                           & old                                                         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "sample_id | vae_12 | target_id | Gender | Race | Ethnicity | Age at Diagnosis in Days | First Event | Event Free Survival Time in Days | Vital Status | ⋯ | Histology | Grade | MKI | Diagnostic Category | ICDO | ICDO Description | COG Risk Group | Site Relapse | Comment | target_update | \n",
       "|---|---|\n",
       "| TARGET-30-PARSBI-01                                          | 2.575049                                                     | PARSBI                                                       | Female                                                       | White                                                        | Not Hispanic or Latino                                       | 2390                                                         | Relapse                                                      | 1377                                                         | Dead                                                         | ⋯                                                            | Unfavorable                                                  | Undifferentiated or Poorly Differentiated                    | Low                                                          | Neuroblastoma                                                | C76.2                                                        | Abdomen, NOS  Abdominal wall, NOS  Intra-abdominal site, NOS | High Risk                                                    | Other metastatic sites                                       | NA                                                           | old                                                          | \n",
       "| TARGET-30-PATBMM-01                                          | 3.673986                                                     | PATBMM                                                       | Male                                                         | White                                                        | Not Hispanic or Latino                                       | 1112                                                         | Relapse                                                      |  653                                                         | Alive                                                        | ⋯                                                            | Unfavorable                                                  | Undifferentiated or Poorly Differentiated                    | Low                                                          | Ganglioneuroblastoma, nodular                                | C74.9                                                        | Adrenal gland, NOS  Suprarenal gland  Adrenal, NOS           | High Risk                                                    | Bone                                                         | NA                                                           | old                                                          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  sample_id           vae_12   target_id Gender Race  Ethnicity             \n",
       "1 TARGET-30-PARSBI-01 2.575049 PARSBI    Female White Not Hispanic or Latino\n",
       "2 TARGET-30-PATBMM-01 3.673986 PATBMM    Male   White Not Hispanic or Latino\n",
       "  Age at Diagnosis in Days First Event Event Free Survival Time in Days\n",
       "1 2390                     Relapse     1377                            \n",
       "2 1112                     Relapse      653                            \n",
       "  Vital Status ⋯ Histology   Grade                                     MKI\n",
       "1 Dead         ⋯ Unfavorable Undifferentiated or Poorly Differentiated Low\n",
       "2 Alive        ⋯ Unfavorable Undifferentiated or Poorly Differentiated Low\n",
       "  Diagnostic Category           ICDO \n",
       "1 Neuroblastoma                 C76.2\n",
       "2 Ganglioneuroblastoma, nodular C74.9\n",
       "  ICDO Description                                             COG Risk Group\n",
       "1 Abdomen, NOS  Abdominal wall, NOS  Intra-abdominal site, NOS High Risk     \n",
       "2 Adrenal gland, NOS  Suprarenal gland  Adrenal, NOS           High Risk     \n",
       "  Site Relapse           Comment target_update\n",
       "1 Other metastatic sites NA      old          \n",
       "2 Bone                   NA      old          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load TARGET phenotype data\n",
    "file <- file.path(\"..\", \"0.expression-download\", \"data\", \"2017-09-30-TARGET update harmonized.txt\")\n",
    "nbl_pheno_df <- readr::read_tsv(file)\n",
    "\n",
    "full_target_ids <- c()\n",
    "for (target_id in strsplit(top_mycn_feature_df$sample_id, \"-\")) {\n",
    "  target_id_unlist <- unlist(target_id)\n",
    "  full_target_ids <- c(full_target_ids, target_id_unlist[3])\n",
    "}\n",
    "\n",
    "top_mycn_feature_df$target_id <- full_target_ids\n",
    "top_mycn_feature_df <- top_mycn_feature_df %>% \n",
    "  dplyr::inner_join(nbl_pheno_df, by = c(\"target_id\" = \"usi\")) %>%\n",
    "    dplyr::rename(\"mycn_status\" = \"MYCN status\")\n",
    "\n",
    "head(top_mycn_feature_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWelch Two Sample t-test\n",
       "\n",
       "data:  amp_scores and noamp_scores\n",
       "t = -13.051, df = 61.99, p-value < 2.2e-16\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -3.786899 -2.780917\n",
       "sample estimates:\n",
       "mean of x mean of y \n",
       "0.4818781 3.7657864 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amp_scores <- top_mycn_feature_df %>%\n",
    "    dplyr::filter(mycn_status == \"Amplified\") %>%\n",
    "    dplyr::pull(!!best_target_feature)\n",
    "noamp_scores <- top_mycn_feature_df %>%\n",
    "    dplyr::filter(mycn_status == \"Not Amplified\") %>%\n",
    "    dplyr::pull(!!best_target_feature)\n",
    "\n",
    "mycn_ttest_result <- t.test(amp_scores, noamp_scores, var.equal = FALSE, paired = FALSE)\n",
    "mycn_ttest_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lAz900xfpOImGKahmGYJheIiJtq37Yl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Ezr2alcps0VVEXJK6uTOc0rcUbMdGxBEMK4xw4AKtuc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pcqebF4fz3x7ff+/x/VN5rSdY97rVZ62L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\n4jTEcpU63dPn1lf0FgJJKJ03VmBUzNLZwEqofMrvXttbKSJn1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8DoVjajObryD8/sGghE/M5n/rCqcs6s5Gu0rZk2ve7UD3yo4fEf/KHz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\nAABASPw3UfNnGlK37f0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mycn_amp_gg <- ggplot(top_mycn_feature_df,\n",
    "                      aes_string(y = best_target_feature,\n",
    "                                 x = \"mycn_status\",\n",
    "                                 fill = \"mycn_status\")) +\n",
    "    geom_boxplot(alpha = 0.5,\n",
    "               outlier.alpha = 0) +\n",
    "    geom_jitter(aes(color = mycn_status),\n",
    "              alpha = 0.7,\n",
    "              size = 0.9,\n",
    "              width = 0.2) +\n",
    "    scale_fill_manual(labels = c(\"Amplified\" = \"Amplified\",\n",
    "                                 \"Not Amplified\" = \"Not Amplified\",\n",
    "                                 \"Unknown\" = \"Unknown\"),\n",
    "                    values = c(\"Amplified\" = \"#d95f02\",\n",
    "                               \"Not Amplified\" = \"#1b9e77\",\n",
    "                               \"Unknown\" = \"grey25\")) +\n",
    "    scale_color_manual(labels = c(\"Amplified\" = \"Amplified\",\n",
    "                                \"Not Amplified\" = \"Not Amplified\",\n",
    "                                \"Unknown\" = \"Unknown\"),\n",
    "                     values = c(\"Amplified\" = \"#d95f02\",\n",
    "                                \"Not Amplified\" = \"#1b9e77\",\n",
    "                                \"Unknown\" = \"grey25\")) +\n",
    "    theme_bw() +\n",
    "    ylab(\"VAE Feature 12 (k = 50)\") +\n",
    "    xlab(\"\") +\n",
    "    ggtitle(\"TARGET NBL MYCN Status\") +\n",
    "    ylim(c(-0.5, 8.0)) +\n",
    "    theme(axis.title.x = element_text(size = 7),\n",
    "       axis.title.y = element_text(size = 7),\n",
    "       axis.text.x = element_text(size = 6),\n",
    "       axis.text.y = element_text(size = 6),\n",
    "       plot.title = element_text(size = 8,\n",
    "                                 hjust = 0.5),\n",
    "       legend.position = \"none\") +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_mycn_feature_df[, best_target_feature]) + 0.8,\n",
    "                     xend=2,\n",
    "                     yend=max(top_mycn_feature_df[, best_target_feature]) + 0.8),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(top_mycn_feature_df[, best_target_feature]) + 0.8,\n",
    "                     xend=1,\n",
    "                     yend=max(top_mycn_feature_df[, best_target_feature]) + 0.3),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=2,\n",
    "                     y=max(top_mycn_feature_df[, best_target_feature]) + 0.8,\n",
    "                     xend=2,\n",
    "                     yend=max(top_mycn_feature_df[, best_target_feature]) + 0.3),\n",
    "                 size=0.4) +\n",
    "    geom_text(x=1.5,\n",
    "              y = (max(top_mycn_feature_df[, best_target_feature]) + 1.25),\n",
    "              size = 3.0,\n",
    "              label = paste0(\"t = \",\n",
    "                            round(mycn_ttest_result$statistic, 1),\n",
    "                            \"; p = \",\n",
    "                            format.pval(mycn_ttest_result$p.value, digits = 2)))\n",
    "\n",
    "mycn_amp_gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot MYCN Signature Applied to NBL Cell Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Missing column names filled in: 'X1' [1]”Parsed with column specification:\n",
      "cols(\n",
      "  X1 = col_double(),\n",
      "  vae_12 = col_double(),\n",
      "  `Cell Line` = col_character(),\n",
      "  `MYCN status` = col_character(),\n",
      "  `1p36 del` = col_character(),\n",
      "  `3p26 del` = col_character(),\n",
      "  `11q23 del` = col_character(),\n",
      "  `17q21-qter unbal gain` = col_character(),\n",
      "  `ALK mutation` = col_character(),\n",
      "  `p53 mutation` = col_character()\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X1</th><th scope=col>vae_12</th><th scope=col>Cell Line</th><th scope=col>mycn_status</th><th scope=col>1p36 del</th><th scope=col>3p26 del</th><th scope=col>11q23 del</th><th scope=col>17q21-qter unbal gain</th><th scope=col>ALK mutation</th><th scope=col>p53 mutation</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0                                                  </td><td>27.22712                                           </td><td>CHP134                                             </td><td>Amplified                                          </td><td>LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22-pter</td><td>Gain/AI p26.3                                      </td><td>None                                               </td><td>Gain q12-qter                                      </td><td>WT                                                 </td><td>WT                                                 </td></tr>\n",
       "\t<tr><td>1                                                  </td><td>25.43260                                           </td><td>CHP212                                             </td><td>Amplified                                          </td><td>Loss p13.2-pter                                    </td><td>Gain/AI p26.3                                      </td><td>cnLOH 23.3                                         </td><td>Gain q12-qter                                      </td><td>WT                                                 </td><td>WT                                                 </td></tr>\n",
       "\t<tr><td>2                                                  </td><td>22.03650                                           </td><td>COGN415                                            </td><td>Amplified                                          </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>F1174L                                             </td><td>WT                                                 </td></tr>\n",
       "\t<tr><td>3                                                  </td><td>24.63928                                           </td><td>COGN440                                            </td><td>Amplified                                          </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>WT                                                 </td><td>WT                                                 </td></tr>\n",
       "\t<tr><td>4                                                  </td><td>23.80450                                           </td><td>COGN453                                            </td><td>Amplified                                          </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>F1174L                                             </td><td>WT                                                 </td></tr>\n",
       "\t<tr><td>5                                                  </td><td>26.21906                                           </td><td>COGN496                                            </td><td>Amplified                                          </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td><td>Unknown                                            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " X1 & vae\\_12 & Cell Line & mycn\\_status & 1p36 del & 3p26 del & 11q23 del & 17q21-qter unbal gain & ALK mutation & p53 mutation\\\\\n",
       "\\hline\n",
       "\t 0                                                   & 27.22712                                            & CHP134                                              & Amplified                                           & LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22-pter & Gain/AI p26.3                                       & None                                                & Gain q12-qter                                       & WT                                                  & WT                                                 \\\\\n",
       "\t 1                                                   & 25.43260                                            & CHP212                                              & Amplified                                           & Loss p13.2-pter                                     & Gain/AI p26.3                                       & cnLOH 23.3                                          & Gain q12-qter                                       & WT                                                  & WT                                                 \\\\\n",
       "\t 2                                                   & 22.03650                                            & COGN415                                             & Amplified                                           & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                             & F1174L                                              & WT                                                 \\\\\n",
       "\t 3                                                   & 24.63928                                            & COGN440                                             & Amplified                                           & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                             & WT                                                  & WT                                                 \\\\\n",
       "\t 4                                                   & 23.80450                                            & COGN453                                             & Amplified                                           & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                             & F1174L                                              & WT                                                 \\\\\n",
       "\t 5                                                   & 26.21906                                            & COGN496                                             & Amplified                                           & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                             & Unknown                                            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "X1 | vae_12 | Cell Line | mycn_status | 1p36 del | 3p26 del | 11q23 del | 17q21-qter unbal gain | ALK mutation | p53 mutation | \n",
       "|---|---|---|---|---|---|\n",
       "| 0                                                   | 27.22712                                            | CHP134                                              | Amplified                                           | LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22-pter | Gain/AI p26.3                                       | None                                                | Gain q12-qter                                       | WT                                                  | WT                                                  | \n",
       "| 1                                                   | 25.43260                                            | CHP212                                              | Amplified                                           | Loss p13.2-pter                                     | Gain/AI p26.3                                       | cnLOH 23.3                                          | Gain q12-qter                                       | WT                                                  | WT                                                  | \n",
       "| 2                                                   | 22.03650                                            | COGN415                                             | Amplified                                           | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | F1174L                                              | WT                                                  | \n",
       "| 3                                                   | 24.63928                                            | COGN440                                             | Amplified                                           | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | WT                                                  | WT                                                  | \n",
       "| 4                                                   | 23.80450                                            | COGN453                                             | Amplified                                           | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | F1174L                                              | WT                                                  | \n",
       "| 5                                                   | 26.21906                                            | COGN496                                             | Amplified                                           | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | Unknown                                             | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  X1 vae_12   Cell Line mycn_status\n",
       "1 0  27.22712 CHP134    Amplified  \n",
       "2 1  25.43260 CHP212    Amplified  \n",
       "3 2  22.03650 COGN415   Amplified  \n",
       "4 3  24.63928 COGN440   Amplified  \n",
       "5 4  23.80450 COGN453   Amplified  \n",
       "6 5  26.21906 COGN496   Amplified  \n",
       "  1p36 del                                            3p26 del      11q23 del \n",
       "1 LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22-pter Gain/AI p26.3 None      \n",
       "2 Loss p13.2-pter                                     Gain/AI p26.3 cnLOH 23.3\n",
       "3 Unknown                                             Unknown       Unknown   \n",
       "4 Unknown                                             Unknown       Unknown   \n",
       "5 Unknown                                             Unknown       Unknown   \n",
       "6 Unknown                                             Unknown       Unknown   \n",
       "  17q21-qter unbal gain ALK mutation p53 mutation\n",
       "1 Gain q12-qter         WT           WT          \n",
       "2 Gain q12-qter         WT           WT          \n",
       "3 Unknown               F1174L       WT          \n",
       "4 Unknown               WT           WT          \n",
       "5 Unknown               F1174L       WT          \n",
       "6 Unknown               Unknown      Unknown     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file <- file.path(\"results\", \"mycn_nbl_scores.tsv\")\n",
    "mycn_validation_df <- readr::read_tsv(file) %>%\n",
    "    dplyr::rename(\"mycn_status\" = \"MYCN status\")\n",
    "\n",
    "mycn_validation_df$mycn_status <- dplyr::recode_factor(mycn_validation_df$mycn_status,\n",
    "                                                       \"Non-amplified\" = \"Not Amplified\")\n",
    "mycn_validation_df$mycn_status <- factor(mycn_validation_df$mycn_status,\n",
    "                                         levels = c(\"Amplified\", \"Not Amplified\"))\n",
    "head(mycn_validation_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWelch Two Sample t-test\n",
       "\n",
       "data:  amp_scores and noamp_scores\n",
       "t = -3.1652, df = 23.192, p-value = 0.004294\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -10.044977  -2.106709\n",
       "sample estimates:\n",
       "mean of x mean of y \n",
       " 22.58503  28.66088 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amp_scores <- mycn_validation_df %>%\n",
    "    dplyr::filter(mycn_status == \"Amplified\") %>%\n",
    "    dplyr::pull(!!best_target_feature)\n",
    "noamp_scores <- mycn_validation_df %>%\n",
    "    dplyr::filter(mycn_status == \"Not Amplified\") %>%\n",
    "    dplyr::pull(!!best_target_feature)\n",
    "\n",
    "mycn_val_ttest_result <- t.test(amp_scores, noamp_scores, var.equal = FALSE, paired = FALSE)\n",
    "mycn_val_ttest_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NSvze2NpJFtnmu41XGDFJ2UCh1E2n0H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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mycn_validation_gg <-\n",
    "    ggplot(mycn_validation_df,\n",
    "           aes_string(y = best_target_feature,\n",
    "                      x = \"mycn_status\",\n",
    "                      fill = \"mycn_status\")) +\n",
    "    geom_boxplot(alpha = 0.5,\n",
    "               outlier.alpha = 0) +\n",
    "    geom_jitter(aes(color = mycn_status),\n",
    "              alpha = 0.7,\n",
    "              size = 0.9,\n",
    "              width = 0.2) +\n",
    "    theme_bw() +\n",
    "    scale_fill_manual(labels = c(\"Amplified\" = \"Amplified\",\n",
    "                               \"Not Amplified\" = \"Not Amplified\"),\n",
    "                    values = c(\"Amplified\" = \"#d95f02\",\n",
    "                               \"Not Amplified\" = \"#1b9e77\")) +\n",
    "    scale_color_manual(labels = c(\"Amplified\" = \"Amplified\",\n",
    "                                \"Not Amplified\" = \"Not Amplified\"),\n",
    "                     values = c(\"Amplified\" = \"#d95f02\",\n",
    "                                \"Not Amplified\" = \"#1b9e77\")) +\n",
    "    ylab(\"VAE Feature 12 (k = 50)\") +\n",
    "    xlab(\"\") +\n",
    "    ggtitle(\"NBL Cell Lines MYCN Status\") +\n",
    "    ylim(c(8, 36.5)) +\n",
    "    theme(axis.title.x = element_text(size = 7),\n",
    "       axis.title.y = element_text(size = 7),\n",
    "       axis.text.x = element_text(size = 6),\n",
    "       axis.text.y = element_text(size = 6),\n",
    "       plot.title = element_text(size = 8,\n",
    "                                 hjust = 0.5),\n",
    "       legend.position = \"none\") +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(mycn_validation_df[, best_target_feature]) + 1.2,\n",
    "                     xend=2,\n",
    "                     yend=max(mycn_validation_df[, best_target_feature]) + 1.2),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=1,\n",
    "                     y=max(mycn_validation_df[, best_target_feature]) + 1.2,\n",
    "                     xend=1,\n",
    "                     yend=max(mycn_validation_df[, best_target_feature]) + 0.4),\n",
    "                 size=0.4) +\n",
    "    geom_segment(aes(x=2,\n",
    "                     y=max(mycn_validation_df[, best_target_feature]) + 1.2,\n",
    "                     xend=2,\n",
    "                     yend=max(mycn_validation_df[, best_target_feature]) + 0.4),\n",
    "                 size=0.4) +\n",
    "    geom_text(x=1.5,\n",
    "              y = (max(mycn_validation_df[, best_target_feature]) + 2.75),\n",
    "              size = 3.0,\n",
    "              label = paste0(\"t = \",\n",
    "                            round(mycn_val_ttest_result$statistic, 1),\n",
    "                            \"; p = \",\n",
    "                            format.pval(mycn_val_ttest_result$p.value, digits = 2)))\n",
    "\n",
    "mycn_validation_gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QbfWlc73O+4/MQKr71tsToq6+ruFq0mkjZtq5hneQaSESLLMH6XZbh/wHfY4a1Y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K8TgAACEQC9G1i1rrjLqkkdROxuTGCPv4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\ny/GWlqYnv3fXcy0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5qU1jVvxtoN7No4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\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etrZvMuABC8Of6FNxN/wUj3a2DqkxHN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a9/c8fh7t2blzzx3fuffsmTPnoEQlCR6z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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot multi panel figure\n",
    "line_gg_legend <- cowplot::get_legend(line_gg_list[[1]])\n",
    "\n",
    "full_line_gg <- cowplot::plot_grid(\n",
    "    line_gg_list[[2]] +\n",
    "        theme(legend.position = \"none\") +\n",
    "    ggtitle(\"GTEx Sex\"),\n",
    "        line_gg_list[[3]] +\n",
    "    theme(legend.position = \"none\") +\n",
    "        ggtitle(\"TCGA Sex\"),\n",
    "    labels = c(\"a\", \"c\")\n",
    ")\n",
    "\n",
    "full_line_gg <- cowplot::plot_grid(full_line_gg,\n",
    "                                   line_gg_legend,\n",
    "                                   rel_widths = c(1, 0.08),\n",
    "                                   ncol = 2)\n",
    "\n",
    "box_gg <- cowplot::plot_grid(\n",
    "    gtex_sex_gg,\n",
    "    tcga_sex_gg,\n",
    "    labels = c(\"b\", \"d\"),\n",
    "    ncol = 2\n",
    ")\n",
    "\n",
    "\n",
    "full_nbl_gg <- cowplot::plot_grid(\n",
    "    line_gg_list[[1]] +\n",
    "        ggtitle(\"TARGET NBL MYCN Status\") +\n",
    "        theme(legend.position = \"bottom\"),\n",
    "    mycn_amp_gg +\n",
    "        ggtitle(\"TARGET NBL MYCN Status\"),\n",
    "    mycn_validation_gg +\n",
    "        ggtitle(\"NBL Cell Lines MYCN Status\"),\n",
    "    labels = c(\"e\", \"f\", \"g\"),\n",
    "    ncol = 3\n",
    ")\n",
    "\n",
    "\n",
    "main_plot <- cowplot::plot_grid(\n",
    "    full_line_gg,\n",
    "    box_gg,\n",
    "    full_nbl_gg,\n",
    "    nrow = 3\n",
    ")\n",
    "\n",
    "main_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(extension in c('.png', '.pdf', '.svg')) {\n",
    "  fig_file <- paste0(\"full_separation_plot\", extension)\n",
    "  fig_file <- file.path(\"figures\", fig_file)\n",
    "  cowplot::save_plot(filename = fig_file,\n",
    "                     plot = main_plot,\n",
    "                     base_height = 180,\n",
    "                     base_width = 170,\n",
    "                     units = \"mm\",\n",
    "                     dpi = 500)\n",
    "}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R [conda env:biobombe]",
   "language": "R",
   "name": "conda-env-biobombe-r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}