{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c14b3188",
   "metadata": {},
   "source": [
    "# Tutorial: Scoring with ANS in R\n",
    "\n",
    "The following tutorial shows how to score a signature with ANS in R. The jupyter notebook is heavily based on the `AddModuleScore` usage example of the Seurat method description, see [here](https://satijalab.org/seurat/reference/addmodulescore).\n",
    "\n",
    "Equally to the Seurat \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0c66588",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: SeuratObject\n",
      "\n",
      "Loading required package: sp\n",
      "\n",
      "The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,\n",
      "which was just loaded, were retired in October 2023.\n",
      "Please refer to R-spatial evolution reports for details, especially\n",
      "https://r-spatial.org/r/2023/05/15/evolution4.html.\n",
      "It may be desirable to make the sf package available;\n",
      "package maintainers should consider adding sf to Suggests:.\n",
      "\n",
      "\n",
      "Attaching package: ‘SeuratObject’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, saveRDS\n",
      "\n",
      "\n",
      "Loading Seurat v5 beta version \n",
      "To maintain compatibility with previous workflows, new Seurat objects will use the previous object structure by default\n",
      "To use new Seurat v5 assays please run: options(Seurat.object.assay.version = 'v5')\n",
      "\n",
      "\n",
      "Attaching package: ‘zoo’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    as.Date, as.Date.numeric\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(\"Seurat\")\n",
    "source('../../src_R/adjusted_neighborhood_scoring.R')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3f23a5-c04b-4920-9f9b-2799150dee9c",
   "metadata": {},
   "source": [
    "Load small sample dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bacbcb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data(\"pbmc_small\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b82d3f-dc6b-4f77-9232-ca2764861338",
   "metadata": {},
   "source": [
    "Define one signature and store it in list object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac1cfeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "cd_features <- list(c(\n",
    "  'CD79B',\n",
    "  'CD79A',\n",
    "  'CD19',\n",
    "  'CD180',\n",
    "  'CD200',\n",
    "  'CD3D',\n",
    "  'CD2',\n",
    "  'CD3E',\n",
    "  'CD7',\n",
    "  'CD8A',\n",
    "  'CD14',\n",
    "  'CD1C',\n",
    "  'CD68',\n",
    "  'CD9',\n",
    "  'CD247'\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8d7cd3-67ca-4d17-aaf4-2e74998b6b8a",
   "metadata": {},
   "source": [
    "Score with ANS. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3ec4525",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22m`GetAssayData()` was deprecated in SeuratObject 5.0.0.\n",
      "\u001b[36mℹ\u001b[39m Please use `LayerData()` instead.”\n"
     ]
    }
   ],
   "source": [
    "pbmc_small <- AdjustedNeighborhoodScoring(\n",
    "  object = pbmc_small,\n",
    "  features = cd_features,\n",
    "  ctrl = 5, # by default ANS uses 100 control genes per signature gene\n",
    "  name = 'CD_scores_ANS'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ddef049-c6d3-47d9-88cc-8019d1122f93",
   "metadata": {},
   "source": [
    "First entries of small dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fd6edfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>orig.ident</th><th scope=col>nCount_RNA</th><th scope=col>nFeature_RNA</th><th scope=col>RNA_snn_res.0.8</th><th scope=col>letter.idents</th><th scope=col>groups</th><th scope=col>RNA_snn_res.1</th><th scope=col>CD_scores_ANS1</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ATGCCAGAACGACT</th><td>SeuratProject</td><td> 70</td><td>47</td><td>0</td><td>A</td><td>g2</td><td>0</td><td>0.7791735</td></tr>\n",
       "\t<tr><th scope=row>CATGGCCTGTGCAT</th><td>SeuratProject</td><td> 85</td><td>52</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>0.7931169</td></tr>\n",
       "\t<tr><th scope=row>GAACCTGATGAACC</th><td>SeuratProject</td><td> 87</td><td>50</td><td>1</td><td>B</td><td>g2</td><td>0</td><td>0.8425729</td></tr>\n",
       "\t<tr><th scope=row>TGACTGGATTCTCA</th><td>SeuratProject</td><td>127</td><td>56</td><td>0</td><td>A</td><td>g2</td><td>0</td><td>0.6067555</td></tr>\n",
       "\t<tr><th scope=row>AGTCAGACTGCACA</th><td>SeuratProject</td><td>173</td><td>53</td><td>0</td><td>A</td><td>g2</td><td>0</td><td>0.4678895</td></tr>\n",
       "\t<tr><th scope=row>TCTGATACACGTGT</th><td>SeuratProject</td><td> 70</td><td>48</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>1.4741454</td></tr>\n",
       "\t<tr><th scope=row>TGGTATCTAAACAG</th><td>SeuratProject</td><td> 64</td><td>36</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>0.5359506</td></tr>\n",
       "\t<tr><th scope=row>GCAGCTCTGTTTCT</th><td>SeuratProject</td><td> 72</td><td>45</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>0.5423551</td></tr>\n",
       "\t<tr><th scope=row>GATATAACACGCAT</th><td>SeuratProject</td><td> 52</td><td>36</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>1.2401691</td></tr>\n",
       "\t<tr><th scope=row>AATGTTGACAGTCA</th><td>SeuratProject</td><td>100</td><td>41</td><td>0</td><td>A</td><td>g1</td><td>0</td><td>0.2482804</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & orig.ident & nCount\\_RNA & nFeature\\_RNA & RNA\\_snn\\_res.0.8 & letter.idents & groups & RNA\\_snn\\_res.1 & CD\\_scores\\_ANS1\\\\\n",
       "  & <fct> & <dbl> & <int> & <fct> & <fct> & <chr> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\tATGCCAGAACGACT & SeuratProject &  70 & 47 & 0 & A & g2 & 0 & 0.7791735\\\\\n",
       "\tCATGGCCTGTGCAT & SeuratProject &  85 & 52 & 0 & A & g1 & 0 & 0.7931169\\\\\n",
       "\tGAACCTGATGAACC & SeuratProject &  87 & 50 & 1 & B & g2 & 0 & 0.8425729\\\\\n",
       "\tTGACTGGATTCTCA & SeuratProject & 127 & 56 & 0 & A & g2 & 0 & 0.6067555\\\\\n",
       "\tAGTCAGACTGCACA & SeuratProject & 173 & 53 & 0 & A & g2 & 0 & 0.4678895\\\\\n",
       "\tTCTGATACACGTGT & SeuratProject &  70 & 48 & 0 & A & g1 & 0 & 1.4741454\\\\\n",
       "\tTGGTATCTAAACAG & SeuratProject &  64 & 36 & 0 & A & g1 & 0 & 0.5359506\\\\\n",
       "\tGCAGCTCTGTTTCT & SeuratProject &  72 & 45 & 0 & A & g1 & 0 & 0.5423551\\\\\n",
       "\tGATATAACACGCAT & SeuratProject &  52 & 36 & 0 & A & g1 & 0 & 1.2401691\\\\\n",
       "\tAATGTTGACAGTCA & SeuratProject & 100 & 41 & 0 & A & g1 & 0 & 0.2482804\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 8\n",
       "\n",
       "| <!--/--> | orig.ident &lt;fct&gt; | nCount_RNA &lt;dbl&gt; | nFeature_RNA &lt;int&gt; | RNA_snn_res.0.8 &lt;fct&gt; | letter.idents &lt;fct&gt; | groups &lt;chr&gt; | RNA_snn_res.1 &lt;fct&gt; | CD_scores_ANS1 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| ATGCCAGAACGACT | SeuratProject |  70 | 47 | 0 | A | g2 | 0 | 0.7791735 |\n",
       "| CATGGCCTGTGCAT | SeuratProject |  85 | 52 | 0 | A | g1 | 0 | 0.7931169 |\n",
       "| GAACCTGATGAACC | SeuratProject |  87 | 50 | 1 | B | g2 | 0 | 0.8425729 |\n",
       "| TGACTGGATTCTCA | SeuratProject | 127 | 56 | 0 | A | g2 | 0 | 0.6067555 |\n",
       "| AGTCAGACTGCACA | SeuratProject | 173 | 53 | 0 | A | g2 | 0 | 0.4678895 |\n",
       "| TCTGATACACGTGT | SeuratProject |  70 | 48 | 0 | A | g1 | 0 | 1.4741454 |\n",
       "| TGGTATCTAAACAG | SeuratProject |  64 | 36 | 0 | A | g1 | 0 | 0.5359506 |\n",
       "| GCAGCTCTGTTTCT | SeuratProject |  72 | 45 | 0 | A | g1 | 0 | 0.5423551 |\n",
       "| GATATAACACGCAT | SeuratProject |  52 | 36 | 0 | A | g1 | 0 | 1.2401691 |\n",
       "| AATGTTGACAGTCA | SeuratProject | 100 | 41 | 0 | A | g1 | 0 | 0.2482804 |\n",
       "\n"
      ],
      "text/plain": [
       "               orig.ident    nCount_RNA nFeature_RNA RNA_snn_res.0.8\n",
       "ATGCCAGAACGACT SeuratProject  70        47           0              \n",
       "CATGGCCTGTGCAT SeuratProject  85        52           0              \n",
       "GAACCTGATGAACC SeuratProject  87        50           1              \n",
       "TGACTGGATTCTCA SeuratProject 127        56           0              \n",
       "AGTCAGACTGCACA SeuratProject 173        53           0              \n",
       "TCTGATACACGTGT SeuratProject  70        48           0              \n",
       "TGGTATCTAAACAG SeuratProject  64        36           0              \n",
       "GCAGCTCTGTTTCT SeuratProject  72        45           0              \n",
       "GATATAACACGCAT SeuratProject  52        36           0              \n",
       "AATGTTGACAGTCA SeuratProject 100        41           0              \n",
       "               letter.idents groups RNA_snn_res.1 CD_scores_ANS1\n",
       "ATGCCAGAACGACT A             g2     0             0.7791735     \n",
       "CATGGCCTGTGCAT A             g1     0             0.7931169     \n",
       "GAACCTGATGAACC B             g2     0             0.8425729     \n",
       "TGACTGGATTCTCA A             g2     0             0.6067555     \n",
       "AGTCAGACTGCACA A             g2     0             0.4678895     \n",
       "TCTGATACACGTGT A             g1     0             1.4741454     \n",
       "TGGTATCTAAACAG A             g1     0             0.5359506     \n",
       "GCAGCTCTGTTTCT A             g1     0             0.5423551     \n",
       "GATATAACACGCAT A             g1     0             1.2401691     \n",
       "AATGTTGACAGTCA A             g1     0             0.2482804     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(x = pbmc_small[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01e63d31-58d8-4dce-8ad8-834c237db413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ip619xUV0PN7xMj4HBGWZmVlJT88ccftLMAAKgqFDsA5SUWi48cOcImZLytBe0s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FJ4T4+/tTHQRAi6Gw03QdO3ak0+lxlVVU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\n2oIVOwBCCJGSkjJkyJDY2Ng2bdpkZWWVlZWdPHnSzc1NVFm3++6774YOHbp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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VlnPlot(object = pbmc_small, features = 'CD_scores_ANS1', group.by = 'orig.ident')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}