B/B2_V5_Integration_Analysis.ipynb

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    "### Calculate LISI Index to evaluate integration after scanorama"
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   "source": [
    "#############################################\n",
    "# Prerequisites - Load Libraries"
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      "    rowSds, rowVars\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    rowSds, rowVars\n",
      "\n",
      "\n",
      "Loading required package: BiocParallel\n",
      "\n",
      "\n",
      "Attaching package: ‘patchwork’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:genefilter’:\n",
      "\n",
      "    area\n",
      "\n",
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      "The following object is masked from ‘package:cowplot’:\n",
      "\n",
      "    align_plots\n",
      "\n",
      "\n",
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      "Attaching package: ‘nnet’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:mgcv’:\n",
      "\n",
      "    multinom\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘dfidx’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:AnnotationDbi’:\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:IRanges’:\n",
      "\n",
      "    slice\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    filter\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘cvAUC’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:genefilter’:\n",
      "\n",
      "    AUC\n",
      "\n",
      "\n",
      "Loading required package: maps\n",
      "\n",
      "Loading required package: shapefiles\n",
      "\n",
      "Loading required package: foreign\n",
      "\n",
      "\n",
      "Attaching package: ‘shapefiles’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:foreign’:\n",
      "\n",
      "    read.dbf, write.dbf\n",
      "\n",
      "\n",
      "Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa\n",
      "\n",
      "Loading required package: grid\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.8.0\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "\n",
      "The new InteractiveComplexHeatmap package can directly export static \n",
      "complex heatmaps into an interactive Shiny app with zero effort. Have a try!\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘ComplexHeatmap’\n",
      "\n",
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      "\n",
      "    dist2\n",
      "\n",
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      "\n",
      "    %v%\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:plotly’:\n",
      "\n",
      "    add_heatmap\n",
      "\n",
      "\n",
      "Welcome to compositions, a package for compositional data analysis.\n",
      "Find an intro with \"? compositions\"\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘compositions’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:DelayedArray’:\n",
      "\n",
      "    scale\n",
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      "    norm\n",
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      "\n",
      "    normalize\n",
      "\n",
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      "The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    cor, cov, var\n",
      "\n",
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      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    cor, cov, var\n",
      "\n",
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      "The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    normalize, var\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:igraph’:\n",
      "\n",
      "    normalize\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    anova, cor, cov, dist, var\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    %*%, norm, scale, scale.default\n",
      "\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.15\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘circlize’\n",
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      "\n",
      "    degree\n",
      "\n",
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      "Loading required package: survival\n",
      "\n",
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      "\n",
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      "\n",
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      "Attaching package: ‘MASS’\n",
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      "\n",
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      "    area\n",
      "\n",
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      "\n",
      "    area\n",
      "\n",
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      "\n",
      "    select\n",
      "\n",
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      "The following object is masked from ‘package:clusterProfiler’:\n",
      "\n",
      "    select\n",
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      "\n",
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      "\n",
      "    select\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘TH.data’\n",
      "\n",
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      "The following object is masked from ‘package:MASS’:\n",
      "\n",
      "    geyser\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘purrr’\n",
      "\n",
      "\n",
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      "\n",
      "    map\n",
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      "\n",
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      "\n",
      "    keep\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:data.table’:\n",
      "\n",
      "    transpose\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:clusterProfiler’:\n",
      "\n",
      "    simplify\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:DelayedArray’:\n",
      "\n",
      "    simplify\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    reduce\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:IRanges’:\n",
      "\n",
      "    reduce\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:igraph’:\n",
      "\n",
      "    compose, simplify\n",
      "\n",
      "\n",
      "Attaching shinyBS\n",
      "\n",
      "Loaded glmnet 4.1-6\n",
      "\n",
      "\n",
      "Attaching package: ‘glmnet’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:compositions’:\n",
      "\n",
      "    rmult\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘ggpubr’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:enrichplot’:\n",
      "\n",
      "    color_palette\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:cowplot’:\n",
      "\n",
      "    get_legend\n",
      "\n",
      "\n",
      "corrplot 0.92 loaded\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
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      "\n",
      "    select\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:nlme’:\n",
      "\n",
      "    collapse\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:gdata’:\n",
      "\n",
      "    combine, first, last\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:Hmisc’:\n",
      "\n",
      "    src, summarize\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:data.table’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:AnnotationDbi’:\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:gridExtra’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:Biobase’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:GenomeInfoDb’:\n",
      "\n",
      "    intersect\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    collapse, desc, intersect, setdiff, slice, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    first, intersect, rename, setdiff, setequal, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:matrixStats’:\n",
      "\n",
      "    count\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:igraph’:\n",
      "\n",
      "    as_data_frame, groups, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Type 'citation(\"pROC\")' for a citation.\n",
      "\n",
      "\n",
      "Attaching package: ‘pROC’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:plotROC’:\n",
      "\n",
      "    ggroc\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:compositions’:\n",
      "\n",
      "    cov, var\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:colorspace’:\n",
      "\n",
      "    coords\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    cov, var\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    cov, var\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    var\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    cov, smooth, var\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "source('MS0_Libraries.r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b2f6599-0af3-4dc2-9a09-0f6c93d25ce9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThe `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.\n",
      "\u001b[36mℹ\u001b[39m Please use the `linewidth` argument instead.”\n"
     ]
    }
   ],
   "source": [
    "source('MS4_Plot_Config.r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e49489-2d82-450a-858c-f022cc0953f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "###############################################\n",
    "# Preqrequisites Configurations & Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "512ff311-bb45-407e-9445-abc85a9227a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path =  '../data/current'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "542fab7c-a499-44b3-95d3-2223167ccfb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_path =  '../results/current'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ddb1fb-f498-4d82-9349-bec12f66ae2f",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6504d96f-c012-4979-8d79-6f639d3db134",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data after scanorama integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33bb00fb-d227-440f-9e8e-fc5871f502b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2024-03-04 14:14:42 CET\"\n"
     ]
    }
   ],
   "source": [
    "path = paste0(result_path, '/B-Analysis/', 'B2_Scanorama_Annotations.csv')\n",
    "library_annotation_scanorama = read.csv(path)\n",
    "print(file.info(path)$mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d3738f1-6076-4475-9f9d-10055768ff33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2024-03-04 14:14:42 CET\"\n"
     ]
    }
   ],
   "source": [
    "path = paste0(result_path, '/B-Analysis/', 'B2_Scanorama_UMAP_Embedding.csv')\n",
    "umap_embedding_scanorama =  read.csv(path)\n",
    "print(file.info(path)$mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18d800d5-7fb9-4a9a-87d7-6ca1fce11e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(library_annotation_scanorama) = library_annotation_scanorama$X\n",
    "library_annotation_scanorama$X = NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6d401e5-e8f2-4bc1-b67b-7b46c5b8b725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>library_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td>L1</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td>L1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & library\\_id\\\\\n",
       "  & <chr>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 & L1\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 & L1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 1\n",
       "\n",
       "| <!--/--> | library_id &lt;chr&gt; |\n",
       "|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 | L1 |\n",
       "| AAACCCACATGACTTG-1-L1 | L1 |\n",
       "\n"
      ],
      "text/plain": [
       "                      library_id\n",
       "AAACCCACATACAGGG-1-L1 L1        \n",
       "AAACCCACATGACTTG-1-L1 L1        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(library_annotation_scanorama,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a91b374-0b16-4a15-a715-a7ef9ce8e9ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(umap_embedding_scanorama) = umap_embedding_scanorama$X\n",
    "umap_embedding_scanorama$X = NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ff4831ff-e4e1-438a-ba4d-d6da8f24c466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X1</th><th scope=col>X2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td> 3.746732</td><td>-1.571113</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td>17.418547</td><td> 5.574673</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & X1 & X2\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 &  3.746732 & -1.571113\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 & 17.418547 &  5.574673\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 2\n",
       "\n",
       "| <!--/--> | X1 &lt;dbl&gt; | X2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 |  3.746732 | -1.571113 |\n",
       "| AAACCCACATGACTTG-1-L1 | 17.418547 |  5.574673 |\n",
       "\n"
      ],
      "text/plain": [
       "                      X1        X2       \n",
       "AAACCCACATACAGGG-1-L1  3.746732 -1.571113\n",
       "AAACCCACATGACTTG-1-L1 17.418547  5.574673"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(umap_embedding_scanorama,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a11c2a0c-3bb1-4b86-a7b4-3c94dbd48e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data before scanorama integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "47108a21-4f35-4112-b34b-37603e8cc945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2024-03-04 14:48:46 CET\"\n"
     ]
    }
   ],
   "source": [
    "path = paste0(result_path, '/B-Analysis/', 'B2_Concat_Annotations.csv')\n",
    "library_annotation_concat = read.csv(path)\n",
    "print(file.info(path)$mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6dad10f3-536a-43d1-8ac4-cd24667a81c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2024-03-04 14:48:53 CET\"\n"
     ]
    }
   ],
   "source": [
    "path = paste0(result_path, '/B-Analysis/', 'B2_Concat_UMAP_Embedding.csv')\n",
    "umap_embedding_concat =  read.csv(path)\n",
    "print(file.info(path)$mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0ae79b95-620d-4a1f-b0a7-fbfc8d40055a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(library_annotation_concat) = library_annotation_concat$X\n",
    "library_annotation_concat$X = NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a588e2fe-51bb-4279-837d-5c5bb6789be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>library_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td>L1</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td>L1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & library\\_id\\\\\n",
       "  & <chr>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 & L1\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 & L1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 1\n",
       "\n",
       "| <!--/--> | library_id &lt;chr&gt; |\n",
       "|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 | L1 |\n",
       "| AAACCCACATGACTTG-1-L1 | L1 |\n",
       "\n"
      ],
      "text/plain": [
       "                      library_id\n",
       "AAACCCACATACAGGG-1-L1 L1        \n",
       "AAACCCACATGACTTG-1-L1 L1        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(library_annotation_concat,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3a5bae00-f8ad-47e5-8bbc-7150afd3590c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(umap_embedding_concat ) = umap_embedding_concat$X\n",
    "umap_embedding_concat $X = NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8497a307-f26f-4fda-bbd0-cae2a1f6e8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X1</th><th scope=col>X2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td>-3.006969</td><td>10.431176</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td> 9.068130</td><td> 7.331071</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & X1 & X2\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 & -3.006969 & 10.431176\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 &  9.068130 &  7.331071\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 2\n",
       "\n",
       "| <!--/--> | X1 &lt;dbl&gt; | X2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 | -3.006969 | 10.431176 |\n",
       "| AAACCCACATGACTTG-1-L1 |  9.068130 |  7.331071 |\n",
       "\n"
      ],
      "text/plain": [
       "                      X1        X2       \n",
       "AAACCCACATACAGGG-1-L1 -3.006969 10.431176\n",
       "AAACCCACATGACTTG-1-L1  9.068130  7.331071"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(umap_embedding_concat ,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "905abae3-21c7-48f2-a5bc-1f406d8741d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af78773-da66-4fcd-90ae-b931ed0379ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96b9bd1d-af38-4eb2-87a7-1badcd3aed9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2b6a6f9e-bb0a-4e82-96bf-bc1d0ffb7636",
   "metadata": {},
   "source": [
    "# LISI calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54e5ad0c-5d44-4787-bcb3-c294eb030529",
   "metadata": {},
   "outputs": [],
   "source": [
    "perplexity_param = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9496badd-d1bf-450e-a2b4-e124fa9aa740",
   "metadata": {},
   "outputs": [],
   "source": [
    "### For scanorama embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc1def8d-34b8-4c86-af16-0b5f0df03f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'L1'</li><li>'L2'</li><li>'L3'</li><li>'L4'</li><li>'L5'</li><li>'L6'</li><li>'L7'</li><li>'L8'</li><li>'L9'</li><li>'L10'</li><li>'L11'</li><li>'L12'</li><li>'L13'</li><li>'L14'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'L1'\n",
       "\\item 'L2'\n",
       "\\item 'L3'\n",
       "\\item 'L4'\n",
       "\\item 'L5'\n",
       "\\item 'L6'\n",
       "\\item 'L7'\n",
       "\\item 'L8'\n",
       "\\item 'L9'\n",
       "\\item 'L10'\n",
       "\\item 'L11'\n",
       "\\item 'L12'\n",
       "\\item 'L13'\n",
       "\\item 'L14'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'L1'\n",
       "2. 'L2'\n",
       "3. 'L3'\n",
       "4. 'L4'\n",
       "5. 'L5'\n",
       "6. 'L6'\n",
       "7. 'L7'\n",
       "8. 'L8'\n",
       "9. 'L9'\n",
       "10. 'L10'\n",
       "11. 'L11'\n",
       "12. 'L12'\n",
       "13. 'L13'\n",
       "14. 'L14'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"L1\"  \"L2\"  \"L3\"  \"L4\"  \"L5\"  \"L6\"  \"L7\"  \"L8\"  \"L9\"  \"L10\" \"L11\" \"L12\"\n",
       "[13] \"L13\" \"L14\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(library_annotation_scanorama$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4deb0316-cc2e-4861-85e8-fe734fcae38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = compute_lisi(umap_embedding_scanorama, library_annotation_scanorama, c('library_id'), perplexity = perplexity_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2314a950-5af2-4164-b82f-3ac3d24957d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>library_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td>5.515944</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td>6.362144</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & library\\_id\\\\\n",
       "  & <dbl>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 & 5.515944\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 & 6.362144\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 1\n",
       "\n",
       "| <!--/--> | library_id &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 | 5.515944 |\n",
       "| AAACCCACATGACTTG-1-L1 | 6.362144 |\n",
       "\n"
      ],
      "text/plain": [
       "                      library_id\n",
       "AAACCCACATACAGGG-1-L1 5.515944  \n",
       "AAACCCACATGACTTG-1-L1 6.362144  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(res,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba8521ba-9c93-45a5-b371-2364aeea38d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zrq+vrrYwEAAO4mJnQo1tXVVSTt8J49RRMGWtc0qGjv3mQRcXNza8BkAGBo5eXl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\nFDsAAABFUOwAAAAU8f/tZZjY94ghBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of res$library_id”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(res$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "69d8d2cc-057f-4c88-8efc-1f5591edbc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6.99152764307122"
      ],
      "text/latex": [
       "6.99152764307122"
      ],
      "text/markdown": [
       "6.99152764307122"
      ],
      "text/plain": [
       "[1] 6.991528"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(res$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b2ee2cb5-ddc5-475e-998c-234c41975d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "### For concat embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4f0cd2fb-f9c3-4898-af42-851f314e1f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'L1'</li><li>'L2'</li><li>'L3'</li><li>'L4'</li><li>'L5'</li><li>'L6'</li><li>'L7'</li><li>'L8'</li><li>'L9'</li><li>'L10'</li><li>'L11'</li><li>'L12'</li><li>'L13'</li><li>'L14'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'L1'\n",
       "\\item 'L2'\n",
       "\\item 'L3'\n",
       "\\item 'L4'\n",
       "\\item 'L5'\n",
       "\\item 'L6'\n",
       "\\item 'L7'\n",
       "\\item 'L8'\n",
       "\\item 'L9'\n",
       "\\item 'L10'\n",
       "\\item 'L11'\n",
       "\\item 'L12'\n",
       "\\item 'L13'\n",
       "\\item 'L14'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'L1'\n",
       "2. 'L2'\n",
       "3. 'L3'\n",
       "4. 'L4'\n",
       "5. 'L5'\n",
       "6. 'L6'\n",
       "7. 'L7'\n",
       "8. 'L8'\n",
       "9. 'L9'\n",
       "10. 'L10'\n",
       "11. 'L11'\n",
       "12. 'L12'\n",
       "13. 'L13'\n",
       "14. 'L14'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"L1\"  \"L2\"  \"L3\"  \"L4\"  \"L5\"  \"L6\"  \"L7\"  \"L8\"  \"L9\"  \"L10\" \"L11\" \"L12\"\n",
       "[13] \"L13\" \"L14\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(library_annotation_concat$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "618b5649-12ae-455b-9aef-539550fa13e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_concat = compute_lisi(umap_embedding_concat, library_annotation_concat, c('library_id'), perplexity = perplexity_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f5d1c458-2818-4fa3-8ea3-9f8b4622cb97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>library_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AAACCCACATACAGGG-1-L1</th><td>9.519807</td></tr>\n",
       "\t<tr><th scope=row>AAACCCACATGACTTG-1-L1</th><td>5.534970</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & library\\_id\\\\\n",
       "  & <dbl>\\\\\n",
       "\\hline\n",
       "\tAAACCCACATACAGGG-1-L1 & 9.519807\\\\\n",
       "\tAAACCCACATGACTTG-1-L1 & 5.534970\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 1\n",
       "\n",
       "| <!--/--> | library_id &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| AAACCCACATACAGGG-1-L1 | 9.519807 |\n",
       "| AAACCCACATGACTTG-1-L1 | 5.534970 |\n",
       "\n"
      ],
      "text/plain": [
       "                      library_id\n",
       "AAACCCACATACAGGG-1-L1 9.519807  \n",
       "AAACCCACATGACTTG-1-L1 5.534970  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(res_concat,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bbdd1e16-6ae3-45bb-8481-672c064c4e89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6.82557353282392"
      ],
      "text/latex": [
       "6.82557353282392"
      ],
      "text/markdown": [
       "6.82557353282392"
      ],
      "text/plain": [
       "[1] 6.825574"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(res_concat$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "df0d377c-f1ea-4409-8b22-94016d7b3ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HV6oYhVl9lr53rpOy0AAEB9M6BiJ2LtvWx/vNu84AVroi+po7aooyoPMHf0nRry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\nV7edHUkURwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of res_concat$library_id”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(res_concat$library_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "226e2dff-50f2-4461-bfc8-e594865644bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Combine and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "638c1cf3-58d6-4ef4-ad53-e06a13a9022c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res$type = 'scanorama_embedding'\n",
    "res_concat$type = 'concat_embedding'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b6bd3c74-8196-4f1f-aa66-3a2b2e43b95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_combined = rbind(res, res_concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5000e6fb-6b8d-4372-aa6d-68cde7e27cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "figure_name = \"SUPP9_B2_LISI_Comparison\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dff406a4-a2c6-42cc-af4b-50b001202fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sizes of the plot\n",
    "width_par = 5\n",
    "height_par = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e11605c0-e86a-4765-b8ac-d06f262e17d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7edtazukXlH\n+F0myIeuvt0GTV20Yulp666dNDL7yc8vzcy6f/n8z+zmt+2Bj6999dK7Xzp05uy/7Pz3+F0o\nIZ8evWT/TCaTmX5/kvzH+QMymcz+sx55+0wXr4YumpCmTt+SPl1S9DK5XK7QawAAAAAA6FZ2\njAIAAAAA4QijAAAAAEA4wigAAAAAEI4wCgAAAACEI4wCAAAAAOEIowAAAABAOMIoAEDP8tAF\n+2UmLtq05+Nb/+uub5z556OG7Tdw/xFHT5xx7f2/35rb89MBAID3EkYBAIpO47LzJ//dY1V/\nf99Tr73x6tr7Fp7W8bNr73q+0KsCAIBiUlLoBQAAsJf++Pi/L//vzy296UufKU+SZNCnxn3p\nO+O+VOhFAQBAcbFjFACg2JQecEB58uyqX772p0KvBAAAipYwCgBQbDJTrrxtRv9/PfuQAz81\n4ayZl19394oXmwu9JgAAKDLCKABA0cmMOvfuZ9f9vvqWSyaO2PHUknmnHnXE6f/7+fZCLwsA\nAIqI3xgFAChK/T5x5ORzj5x8bpJ0bPqPC4//68sXVM+598y+hV4WAAAUCTtGAQCKXJ+Kvxj/\nP5LtmzdvL/RKAACgeAijAADF5o/3/d1fzLnx39Y8/+rWHdu3/OHJu+bdtGrw5LMm7lfohQEA\nQPEQRgEAep7H51Zm3mPqktb3nu1/5v9aMGbjv/zD33z24KGfOGTseYv++5QfPvrTi0cWarUA\nAFCEMrlcrtBrAAAAAADoVnaMAgAAAADhCKMAAAAAQDjCKAAAAAAQjjAKAAAAAIQjjAIAAAAA\n4QijAAAAAEA4wigAAAAAEI4wCgAAAACEI4wCAAAAAOEIowAAAABAOMIoAAAAABDO/wdAwkpp\nuZxUYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=15, repr.plot.height=10)\n",
    "pdf(paste0('figures/Supplementary_Figures/', figure_name,'.pdf'), width =width_par, height = height_par)\n",
    "g =  ggplot(res_combined, aes(x =library_id, fill = type)) + geom_histogram(position = \"identity\", alpha = 0.4, bins = 50) +plot_config + xlab('LISI')\n",
    "print(g)\n",
    "dev.off()\n",
    "\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90dd085e-80cd-4579-b4b7-8b802198013e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "4.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}