7.figures/f1_score_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a2aba6b-bf9a-468b-8f0f-0fe69e6dd51d",
   "metadata": {},
   "source": [
    "## Visualize F1 scores\n",
    "\n",
    "For predicting different single-cell phenotypes.\n",
    "\n",
    "Training and testing curves for the following feature spaces:\n",
    "\n",
    "1. CellProfiler\n",
    "2. DeepProfiler (LUAD)\n",
    "3. Combined CP and DP (LUAD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d366618-e78f-4d9b-a770-e4f572fe476c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(dplyr))\n",
    "suppressPackageStartupMessages(library(ggplot2))\n",
    "\n",
    "# Load figure themes and colors\n",
    "source(\"figure_themes.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cab5d391-63fb-4b7d-a552-41f82b37c91d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set output files\n",
    "output_file <- file.path(\n",
    "    \"figures\", \"f1_scores_multiclass.png\"\n",
    ")\n",
    "\n",
    "output_top_file <- file.path(\n",
    "    \"figures\", \"f1_scores_multiclass_topfeaturespace.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "950afed5-98c1-4691-bdcf-ec5335d36273",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mNew names:\n",
      "\u001b[36m•\u001b[39m `` -> `...1`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 48  6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Phenotypic_Class</th><th scope=col>F1_Score</th><th scope=col>data_split</th><th scope=col>shuffled</th><th scope=col>feature_type</th><th scope=col>feature_type_with_data_split</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>ADCCM    </td><td>0.8965517</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "\t<tr><td>Anaphase </td><td>0.8461538</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "\t<tr><td>Apoptosis</td><td>0.8095238</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "\t<tr><td>Binuclear</td><td>0.7272727</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "\t<tr><td>Elongated</td><td>0.9090909</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "\t<tr><td>Grape    </td><td>0.6363636</td><td>test</td><td>False</td><td>CP</td><td>CPtest</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " Phenotypic\\_Class & F1\\_Score & data\\_split & shuffled & feature\\_type & feature\\_type\\_with\\_data\\_split\\\\\n",
       " <chr> & <dbl> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t ADCCM     & 0.8965517 & test & False & CP & CPtest\\\\\n",
       "\t Anaphase  & 0.8461538 & test & False & CP & CPtest\\\\\n",
       "\t Apoptosis & 0.8095238 & test & False & CP & CPtest\\\\\n",
       "\t Binuclear & 0.7272727 & test & False & CP & CPtest\\\\\n",
       "\t Elongated & 0.9090909 & test & False & CP & CPtest\\\\\n",
       "\t Grape     & 0.6363636 & test & False & CP & CPtest\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 6\n",
       "\n",
       "| Phenotypic_Class &lt;chr&gt; | F1_Score &lt;dbl&gt; | data_split &lt;chr&gt; | shuffled &lt;chr&gt; | feature_type &lt;chr&gt; | feature_type_with_data_split &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| ADCCM     | 0.8965517 | test | False | CP | CPtest |\n",
       "| Anaphase  | 0.8461538 | test | False | CP | CPtest |\n",
       "| Apoptosis | 0.8095238 | test | False | CP | CPtest |\n",
       "| Binuclear | 0.7272727 | test | False | CP | CPtest |\n",
       "| Elongated | 0.9090909 | test | False | CP | CPtest |\n",
       "| Grape     | 0.6363636 | test | False | CP | CPtest |\n",
       "\n"
      ],
      "text/plain": [
       "  Phenotypic_Class F1_Score  data_split shuffled feature_type\n",
       "1 ADCCM            0.8965517 test       False    CP          \n",
       "2 Anaphase         0.8461538 test       False    CP          \n",
       "3 Apoptosis        0.8095238 test       False    CP          \n",
       "4 Binuclear        0.7272727 test       False    CP          \n",
       "5 Elongated        0.9090909 test       False    CP          \n",
       "6 Grape            0.6363636 test       False    CP          \n",
       "  feature_type_with_data_split\n",
       "1 CPtest                      \n",
       "2 CPtest                      \n",
       "3 CPtest                      \n",
       "4 CPtest                      \n",
       "5 CPtest                      \n",
       "6 CPtest                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load data\n",
    "results_dir <- file.path(\n",
    "    \"..\", \"3.evaluate_model\", \"evaluations\", \"F1_scores\"\n",
    ")\n",
    "results_file <- file.path(results_dir, \"compiled_F1_scores.tsv\")\n",
    "\n",
    "f1_score_df <- readr::read_tsv(\n",
    "    results_file,\n",
    "    col_types = readr::cols(\n",
    "        .default = \"d\",\n",
    "        \"Phenotypic_Class\" = \"c\",\n",
    "        \"data_split\" = \"c\",\n",
    "        \"shuffled\" = \"c\",\n",
    "        \"feature_type\" = \"c\"\n",
    "    )\n",
    ") %>%\n",
    "    dplyr::select(!`...1`) %>%\n",
    "    dplyr::mutate(feature_type_with_data_split = paste0(feature_type, data_split)) %>%\n",
    "    dplyr::filter(\n",
    "        data_split == \"test\",\n",
    "        shuffled == \"False\"\n",
    "    )\n",
    "\n",
    "f1_score_df$Phenotypic_Class <- dplyr::recode(f1_score_df$Phenotypic_Class, Weighted = \" OverallPerformance\")\n",
    "\n",
    "print(dim(f1_score_df))\n",
    "head(f1_score_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aeeeb3d8-4fa9-41d7-af4f-a054a63c8deb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CP_and_DPtest        CPtest        DPtest \n",
      "           10             3             3 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 16 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Phenotypic_Class</th><th scope=col>F1_Score</th><th scope=col>data_split</th><th scope=col>shuffled</th><th scope=col>feature_type</th><th scope=col>feature_type_with_data_split</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> OverallPerformance</td><td>0.8462472</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>ADCCM              </td><td>0.9333333</td><td>test</td><td>False</td><td>DP       </td><td>DPtest       </td></tr>\n",
       "\t<tr><td>Anaphase           </td><td>0.9285714</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>Apoptosis          </td><td>0.9156627</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>Binuclear          </td><td>0.7272727</td><td>test</td><td>False</td><td>CP       </td><td>CPtest       </td></tr>\n",
       "\t<tr><td>Elongated          </td><td>0.9375000</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>Grape              </td><td>0.6956522</td><td>test</td><td>False</td><td>DP       </td><td>DPtest       </td></tr>\n",
       "\t<tr><td>Hole               </td><td>0.7741935</td><td>test</td><td>False</td><td>CP       </td><td>CPtest       </td></tr>\n",
       "\t<tr><td>Interphase         </td><td>0.8455285</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>Large              </td><td>0.7407407</td><td>test</td><td>False</td><td>CP       </td><td>CPtest       </td></tr>\n",
       "\t<tr><td>Metaphase          </td><td>0.9000000</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>MetaphaseAlignment </td><td>0.8076923</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>OutOfFocus         </td><td>0.9892473</td><td>test</td><td>False</td><td>DP       </td><td>DPtest       </td></tr>\n",
       "\t<tr><td>Polylobed          </td><td>0.7636364</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>Prometaphase       </td><td>0.8679245</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "\t<tr><td>SmallIrregular     </td><td>0.9056604</td><td>test</td><td>False</td><td>CP_and_DP</td><td>CP_and_DPtest</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 16 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " Phenotypic\\_Class & F1\\_Score & data\\_split & shuffled & feature\\_type & feature\\_type\\_with\\_data\\_split\\\\\n",
       " <chr> & <dbl> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t  OverallPerformance & 0.8462472 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t ADCCM               & 0.9333333 & test & False & DP        & DPtest       \\\\\n",
       "\t Anaphase            & 0.9285714 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t Apoptosis           & 0.9156627 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t Binuclear           & 0.7272727 & test & False & CP        & CPtest       \\\\\n",
       "\t Elongated           & 0.9375000 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t Grape               & 0.6956522 & test & False & DP        & DPtest       \\\\\n",
       "\t Hole                & 0.7741935 & test & False & CP        & CPtest       \\\\\n",
       "\t Interphase          & 0.8455285 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t Large               & 0.7407407 & test & False & CP        & CPtest       \\\\\n",
       "\t Metaphase           & 0.9000000 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t MetaphaseAlignment  & 0.8076923 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t OutOfFocus          & 0.9892473 & test & False & DP        & DPtest       \\\\\n",
       "\t Polylobed           & 0.7636364 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t Prometaphase        & 0.8679245 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\t SmallIrregular      & 0.9056604 & test & False & CP\\_and\\_DP & CP\\_and\\_DPtest\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 16 × 6\n",
       "\n",
       "| Phenotypic_Class &lt;chr&gt; | F1_Score &lt;dbl&gt; | data_split &lt;chr&gt; | shuffled &lt;chr&gt; | feature_type &lt;chr&gt; | feature_type_with_data_split &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "|  OverallPerformance | 0.8462472 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| ADCCM               | 0.9333333 | test | False | DP        | DPtest        |\n",
       "| Anaphase            | 0.9285714 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| Apoptosis           | 0.9156627 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| Binuclear           | 0.7272727 | test | False | CP        | CPtest        |\n",
       "| Elongated           | 0.9375000 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| Grape               | 0.6956522 | test | False | DP        | DPtest        |\n",
       "| Hole                | 0.7741935 | test | False | CP        | CPtest        |\n",
       "| Interphase          | 0.8455285 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| Large               | 0.7407407 | test | False | CP        | CPtest        |\n",
       "| Metaphase           | 0.9000000 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| MetaphaseAlignment  | 0.8076923 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| OutOfFocus          | 0.9892473 | test | False | DP        | DPtest        |\n",
       "| Polylobed           | 0.7636364 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| Prometaphase        | 0.8679245 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "| SmallIrregular      | 0.9056604 | test | False | CP_and_DP | CP_and_DPtest |\n",
       "\n"
      ],
      "text/plain": [
       "   Phenotypic_Class    F1_Score  data_split shuffled feature_type\n",
       "1   OverallPerformance 0.8462472 test       False    CP_and_DP   \n",
       "2  ADCCM               0.9333333 test       False    DP          \n",
       "3  Anaphase            0.9285714 test       False    CP_and_DP   \n",
       "4  Apoptosis           0.9156627 test       False    CP_and_DP   \n",
       "5  Binuclear           0.7272727 test       False    CP          \n",
       "6  Elongated           0.9375000 test       False    CP_and_DP   \n",
       "7  Grape               0.6956522 test       False    DP          \n",
       "8  Hole                0.7741935 test       False    CP          \n",
       "9  Interphase          0.8455285 test       False    CP_and_DP   \n",
       "10 Large               0.7407407 test       False    CP          \n",
       "11 Metaphase           0.9000000 test       False    CP_and_DP   \n",
       "12 MetaphaseAlignment  0.8076923 test       False    CP_and_DP   \n",
       "13 OutOfFocus          0.9892473 test       False    DP          \n",
       "14 Polylobed           0.7636364 test       False    CP_and_DP   \n",
       "15 Prometaphase        0.8679245 test       False    CP_and_DP   \n",
       "16 SmallIrregular      0.9056604 test       False    CP_and_DP   \n",
       "   feature_type_with_data_split\n",
       "1  CP_and_DPtest               \n",
       "2  DPtest                      \n",
       "3  CP_and_DPtest               \n",
       "4  CP_and_DPtest               \n",
       "5  CPtest                      \n",
       "6  CP_and_DPtest               \n",
       "7  DPtest                      \n",
       "8  CPtest                      \n",
       "9  CP_and_DPtest               \n",
       "10 CPtest                      \n",
       "11 CP_and_DPtest               \n",
       "12 CP_and_DPtest               \n",
       "13 DPtest                      \n",
       "14 CP_and_DPtest               \n",
       "15 CP_and_DPtest               \n",
       "16 CP_and_DPtest               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many of the phenotypic classes are certain feature types the best?\n",
    "top_scores_df <- f1_score_df %>%\n",
    "    dplyr::arrange(desc(F1_Score)) %>%\n",
    "    dplyr::group_by(Phenotypic_Class) %>%\n",
    "    dplyr::slice(1)\n",
    "\n",
    "print(table(top_scores_df$feature_type_with_data_split))\n",
    "\n",
    "top_scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c25f02c-ad7d-4582-ae0f-407ffde45340",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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C1JPRBAAAEE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\nSd9/NGPGDPvzn/9sxxxzjNWoUSPR6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PPvqovfLKK6bP+UoqodfnnnuurV+/3jp3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\ndZfpR3d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QQAABBBBAAAEE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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1_score_gg <- (\n",
    "    ggplot(f1_score_df, aes(x = Phenotypic_Class, y = F1_Score))\n",
    "    + geom_bar(aes(fill = feature_type_with_data_split), stat = \"identity\", position = \"dodge\")\n",
    "    + theme_bw()\n",
    "    + xlab(\"Single cell phenotype\")\n",
    "    + ylab(\"F1 Score (Test set)\")\n",
    "    + scale_fill_manual(\n",
    "        name = \"Model scenario\",\n",
    "        labels = feature_type_with_data_split_labels,\n",
    "        values = feature_type_with_data_split_colors\n",
    "    )\n",
    "    + figure_theme\n",
    "    # Decrease spacing in legend and rotate text\n",
    "    + theme(\n",
    "        legend.spacing.y = unit(0.1, \"cm\"),\n",
    "        legend.box.spacing = unit(0.2, \"cm\"),\n",
    "        legend.key.size = unit(0.7, \"lines\"),\n",
    "        legend.key.width = unit(1, \"lines\"),\n",
    "        axis.text.x = element_text(angle = 90)\n",
    "    )\n",
    "    + coord_flip()\n",
    ")\n",
    "\n",
    "ggsave(output_file, f1_score_gg, height = 5, width = 4, dpi = 500)\n",
    "\n",
    "f1_score_gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "67b65be3-de9d-4189-ab71-a5a6c46c2447",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CPuI3y8BipY6t69u/vMU9lpjte4ceOsc+fO3i62atUq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lTRiNZXX30lEECAGAI4dOrUSZCCV69ePRk/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\n8FgnDFIbAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1_score_gg <- (\n",
    "    ggplot(top_scores_df, aes(x = Phenotypic_Class, y = F1_Score))\n",
    "    + geom_bar(aes(fill = feature_type_with_data_split), stat = \"identity\", position = \"dodge\")\n",
    "    + theme_bw()\n",
    "    + xlab(\"Single cell phenotype\")\n",
    "    + ylab(\"F1 Score (Test set)\\nTop feature space\")\n",
    "    + scale_fill_manual(\n",
    "        name = \"Model scenario\",\n",
    "        labels = feature_type_with_data_split_labels,\n",
    "        values = feature_type_with_data_split_colors\n",
    "    )\n",
    "    + figure_theme\n",
    "    # Decrease spacing in legend and rotate text\n",
    "    + theme(\n",
    "        legend.spacing.y = unit(0.1, \"cm\"),\n",
    "        legend.box.spacing = unit(0.2, \"cm\"),\n",
    "        legend.key.size = unit(0.7, \"lines\"),\n",
    "        legend.key.width = unit(1, \"lines\"),\n",
    "        axis.text.x = element_text(angle = 90)\n",
    "    )\n",
    "    + coord_flip()\n",
    ")\n",
    "\n",
    "ggsave(output_top_file, f1_score_gg, height = 3, width = 4, dpi = 500)\n",
    "\n",
    "f1_score_gg"
   ]
  }
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