state_fate_toy_example.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Toy example to design state-fate experiments"
      ],
      "metadata": {
        "id": "5bPwNGk4SeNN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although we assume many parameters here (uniform division, perfect spliting, perfect profiling, etc.), this `R` code aims to give an idea on how to think when designing state-fate experiments lineage tracing techniques (e.g. integration barcodes later expressed). For more complex designs, the user can read the Supplementary methods of [Weinreb, Rodriguez-Fraticelli et al. Science 2020](https://doi.org/10.1126/science.aaw3381), [Rodriguez-Fraticelli et al. Nature 2020](https://doi.org/10.1038/s41586-020-2503-6) or the detailed explanations and [web app](http://celltag.org/) of [Kong et al. Nature Protocols 2020](https://doi.org/10.1038/s41586-018-0744-4).\n",
        "\n",
        "If you want to run this code, please, navigate to Runtime -> Change runtime type and select R."
      ],
      "metadata": {
        "id": "LnPpci05StH1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qnRdsFywHi_H"
      },
      "outputs": [],
      "source": [
        "cells_at_begining <- paste(\"barcode\", 1:100000, sep =\"_\")\n",
        "clones <- cells_at_begining\n",
        "pool_before_splitting <- data.frame(\n",
        "    clones = rep(clones, each = 2),\n",
        "    cellID_after_division = paste(\"cell\", 1:200000, sep =\"_\"))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "head(pool_before_splitting) # visualize the structure of the data object"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "bwD46DltJ9lq",
        "outputId": "d815b842-cc40-4808-a63a-96aa429ab631"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>clones</th><th scope=col>cellID_after_division</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>barcode_1</td><td>cell_1</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>barcode_1</td><td>cell_2</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>barcode_2</td><td>cell_3</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>barcode_2</td><td>cell_4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>barcode_3</td><td>cell_5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>barcode_3</td><td>cell_6</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 2\n\n| <!--/--> | clones &lt;chr&gt; | cellID_after_division &lt;chr&gt; |\n|---|---|---|\n| 1 | barcode_1 | cell_1 |\n| 2 | barcode_1 | cell_2 |\n| 3 | barcode_2 | cell_3 |\n| 4 | barcode_2 | cell_4 |\n| 5 | barcode_3 | cell_5 |\n| 6 | barcode_3 | cell_6 |\n\n",
            "text/latex": "A data.frame: 6 × 2\n\\begin{tabular}{r|ll}\n  & clones & cellID\\_after\\_division\\\\\n  & <chr> & <chr>\\\\\n\\hline\n\t1 & barcode\\_1 & cell\\_1\\\\\n\t2 & barcode\\_1 & cell\\_2\\\\\n\t3 & barcode\\_2 & cell\\_3\\\\\n\t4 & barcode\\_2 & cell\\_4\\\\\n\t5 & barcode\\_3 & cell\\_5\\\\\n\t6 & barcode\\_3 & cell\\_6\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  clones    cellID_after_division\n",
              "1 barcode_1 cell_1               \n",
              "2 barcode_1 cell_2               \n",
              "3 barcode_2 cell_3               \n",
              "4 barcode_2 cell_4               \n",
              "5 barcode_3 cell_5               \n",
              "6 barcode_3 cell_6               "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "simulations <- lapply(1:500, function(iteration) { # the code is not parallelized for simplicity\n",
        "  sampling <- sample(1:nrow(pool_before_splitting), size = 100000, replace = FALSE) # random split\n",
        "  pool_A <- pool_before_splitting[sampling, ]\n",
        "  pool_B <- pool_before_splitting[-sampling, ]\n",
        "  experiment_on_pool_B <- sample(1:nrow(pool_B), size = 100000 * 0.01, replace = FALSE) # LD99\n",
        "  profiling_pool_A <- pool_A  # state\n",
        "  profiling_pool_B <- pool_B[experiment_on_pool_B, ]  # fate\n",
        "  sum(unique(profiling_pool_B$clones) %in% unique(profiling_pool_A$clones))\n",
        "})\n",
        "simulations <- unlist(simulations)"
      ],
      "metadata": {
        "id": "L_ZGEVPVI25J"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample(simulations, size = 10, replace = FALSE) # show the result of 10 random iterations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "FRSgZgRKSLLl",
        "outputId": "37fad27a-28bd-4a44-b03d-69c15437ab9b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "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>476</li><li>492</li><li>492</li><li>502</li><li>504</li><li>487</li><li>504</li><li>474</li><li>482</li><li>496</li></ol>\n"
            ],
            "text/markdown": "1. 476\n2. 492\n3. 492\n4. 502\n5. 504\n6. 487\n7. 504\n8. 474\n9. 482\n10. 496\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 476\n\\item 492\n\\item 492\n\\item 502\n\\item 504\n\\item 487\n\\item 504\n\\item 474\n\\item 482\n\\item 496\n\\end{enumerate*}\n",
            "text/plain": [
              " [1] 476 492 492 502 504 487 504 474 482 496"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "options(repr.plot.width = 5, repr.plot.height = 3, repr.plot.res = 320)\n",
        "plot(density(simulations))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "DwEaMwMBUZcD",
        "outputId": "8f669271-0ed4-40ff-906b-2bce9c45d8a9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “density(x = simulations)”"
            ],
            "image/png": 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IBdqW1ZjQrUV326yWCr6wLrqA9B6yK6khiFAAGbOqD8ACl8IVDvHOUardJXA/WG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\nz6kdZdv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