Scripts/notebook/SwitchErrorTest_PhaseExtenderSetA_02.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "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>'HapMap3_r2_b36_2009.zip'</li><li>'README.md'</li><li>'Rplots.pdf'</li><li>'Scripts'</li><li>'SwitchPoints_SetA02_withOutHaplotypeBreaks.png'</li><li>'data'</li><li>'makeHapFile.py'</li><li>'merge_haplotypePandas.py'</li><li>'phase-Extender.py'</li><li>'phase-Extender2.py'</li><li>'plot_haplotype_stats.py'</li><li>'requirements.txt'</li><li>'temp5'</li><li>'temp6'</li><li>'tests'</li><li>'vcf_to_table-v2.py'</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 'HapMap3\\_r2\\_b36\\_2009.zip'\n",
              "\\item 'README.md'\n",
              "\\item 'Rplots.pdf'\n",
              "\\item 'Scripts'\n",
              "\\item 'SwitchPoints\\_SetA02\\_withOutHaplotypeBreaks.png'\n",
              "\\item 'data'\n",
              "\\item 'makeHapFile.py'\n",
              "\\item 'merge\\_haplotypePandas.py'\n",
              "\\item 'phase-Extender.py'\n",
              "\\item 'phase-Extender2.py'\n",
              "\\item 'plot\\_haplotype\\_stats.py'\n",
              "\\item 'requirements.txt'\n",
              "\\item 'temp5'\n",
              "\\item 'temp6'\n",
              "\\item 'tests'\n",
              "\\item 'vcf\\_to\\_table-v2.py'\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 'HapMap3_r2_b36_2009.zip'\n",
              "2. 'README.md'\n",
              "3. 'Rplots.pdf'\n",
              "4. 'Scripts'\n",
              "5. 'SwitchPoints_SetA02_withOutHaplotypeBreaks.png'\n",
              "6. 'data'\n",
              "7. 'makeHapFile.py'\n",
              "8. 'merge_haplotypePandas.py'\n",
              "9. 'phase-Extender.py'\n",
              "10. 'phase-Extender2.py'\n",
              "11. 'plot_haplotype_stats.py'\n",
              "12. 'requirements.txt'\n",
              "13. 'temp5'\n",
              "14. 'temp6'\n",
              "15. 'tests'\n",
              "16. 'vcf_to_table-v2.py'\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              " [1] \"HapMap3_r2_b36_2009.zip\"                       \n",
              " [2] \"README.md\"                                     \n",
              " [3] \"Rplots.pdf\"                                    \n",
              " [4] \"Scripts\"                                       \n",
              " [5] \"SwitchPoints_SetA02_withOutHaplotypeBreaks.png\"\n",
              " [6] \"data\"                                          \n",
              " [7] \"makeHapFile.py\"                                \n",
              " [8] \"merge_haplotypePandas.py\"                      \n",
              " [9] \"phase-Extender.py\"                             \n",
              "[10] \"phase-Extender2.py\"                            \n",
              "[11] \"plot_haplotype_stats.py\"                       \n",
              "[12] \"requirements.txt\"                              \n",
              "[13] \"temp5\"                                         \n",
              "[14] \"temp6\"                                         \n",
              "[15] \"tests\"                                         \n",
              "[16] \"vcf_to_table-v2.py\"                            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "setwd(\"../..\")\n",
        "list.files()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ZQZQchDm_zZb",
        "outputId": "172d1eee-7f9e-4aed-f1a5-7ff3053b180b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>CHROM</th><th scope=col>POS</th><th scope=col>NA12891.PI</th><th scope=col>NA12891.PG_al</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>20</td><td> 9795</td><td>1</td><td>T|G</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>20</td><td>11799</td><td>1</td><td>T|C</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>20</td><td>17094</td><td>1</td><td>A|G</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>20</td><td>24962</td><td>1</td><td>C|T</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>20</td><td>28655</td><td>1</td><td>G|A</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>20</td><td>35416</td><td>1</td><td>C|A</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.frame: 6 × 4\n",
              "\\begin{tabular}{r|llll}\n",
              "  & CHROM & POS & NA12891.PI & NA12891.PG\\_al\\\\\n",
              "  & <int> & <int> & <dbl> & <chr>\\\\\n",
              "\\hline\n",
              "\t1 & 20 &  9795 & 1 & T\\textbar{}G\\\\\n",
              "\t2 & 20 & 11799 & 1 & T\\textbar{}C\\\\\n",
              "\t3 & 20 & 17094 & 1 & A\\textbar{}G\\\\\n",
              "\t4 & 20 & 24962 & 1 & C\\textbar{}T\\\\\n",
              "\t5 & 20 & 28655 & 1 & G\\textbar{}A\\\\\n",
              "\t6 & 20 & 35416 & 1 & C\\textbar{}A\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.frame: 6 × 4\n",
              "\n",
              "| <!--/--> | CHROM &lt;int&gt; | POS &lt;int&gt; | NA12891.PI &lt;dbl&gt; | NA12891.PG_al &lt;chr&gt; |\n",
              "|---|---|---|---|---|\n",
              "| 1 | 20 |  9795 | 1 | T|G |\n",
              "| 2 | 20 | 11799 | 1 | T|C |\n",
              "| 3 | 20 | 17094 | 1 | A|G |\n",
              "| 4 | 20 | 24962 | 1 | C|T |\n",
              "| 5 | 20 | 28655 | 1 | G|A |\n",
              "| 6 | 20 | 35416 | 1 | C|A |\n",
              "\n"
            ],
            "text/plain": [
              "  CHROM POS   NA12891.PI NA12891.PG_al\n",
              "1 20     9795 1          T|G          \n",
              "2 20    11799 1          T|C          \n",
              "3 20    17094 1          A|G          \n",
              "4 20    24962 1          C|T          \n",
              "5 20    28655 1          G|A          \n",
              "6 20    35416 1          C|A          "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "truthHaplotype_NA12891 <- read.table('data/SetA/truth_Haplotype_NA12891.txt', header = TRUE)\n",
        "head(truthHaplotype_NA12891)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sCmscMkgCgJk"
      },
      "outputs": [],
      "source": [
        "\n",
        "# change the name of the header\n",
        "colnames(truthHaplotype_NA12891)[colnames(truthHaplotype_NA12891)==\"NA12891.PI\"] <- \"true.NA12891.PI\"\n",
        "colnames(truthHaplotype_NA12891)[colnames(truthHaplotype_NA12891)==\"NA12891.PG_al\"] <- \"true.NA12891.PG_al\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "BOEGM4EvDDY9",
        "outputId": "b9f32a6c-4ce4-49ce-9288-004e0b2cb3ef"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>CHROM</th><th scope=col>POS</th><th scope=col>true.NA12891.PI</th><th scope=col>true.NA12891.PG_al</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>20</td><td> 9795</td><td>1</td><td>T|G</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>20</td><td>11799</td><td>1</td><td>T|C</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>20</td><td>17094</td><td>1</td><td>A|G</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>20</td><td>24962</td><td>1</td><td>C|T</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>20</td><td>28655</td><td>1</td><td>G|A</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>20</td><td>35416</td><td>1</td><td>C|A</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.frame: 6 × 4\n",
              "\\begin{tabular}{r|llll}\n",
              "  & CHROM & POS & true.NA12891.PI & true.NA12891.PG\\_al\\\\\n",
              "  & <int> & <int> & <dbl> & <chr>\\\\\n",
              "\\hline\n",
              "\t1 & 20 &  9795 & 1 & T\\textbar{}G\\\\\n",
              "\t2 & 20 & 11799 & 1 & T\\textbar{}C\\\\\n",
              "\t3 & 20 & 17094 & 1 & A\\textbar{}G\\\\\n",
              "\t4 & 20 & 24962 & 1 & C\\textbar{}T\\\\\n",
              "\t5 & 20 & 28655 & 1 & G\\textbar{}A\\\\\n",
              "\t6 & 20 & 35416 & 1 & C\\textbar{}A\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.frame: 6 × 4\n",
              "\n",
              "| <!--/--> | CHROM &lt;int&gt; | POS &lt;int&gt; | true.NA12891.PI &lt;dbl&gt; | true.NA12891.PG_al &lt;chr&gt; |\n",
              "|---|---|---|---|---|\n",
              "| 1 | 20 |  9795 | 1 | T|G |\n",
              "| 2 | 20 | 11799 | 1 | T|C |\n",
              "| 3 | 20 | 17094 | 1 | A|G |\n",
              "| 4 | 20 | 24962 | 1 | C|T |\n",
              "| 5 | 20 | 28655 | 1 | G|A |\n",
              "| 6 | 20 | 35416 | 1 | C|A |\n",
              "\n"
            ],
            "text/plain": [
              "  CHROM POS   true.NA12891.PI true.NA12891.PG_al\n",
              "1 20     9795 1               T|G               \n",
              "2 20    11799 1               T|C               \n",
              "3 20    17094 1               A|G               \n",
              "4 20    24962 1               C|T               \n",
              "5 20    28655 1               G|A               \n",
              "6 20    35416 1               C|A               "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "head(truthHaplotype_NA12891)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "MV5uwuoMF_Yl",
        "outputId": "7076bc0c-48f7-43c5-9276-de13d376f685"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 7</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>CHROM</th><th scope=col>POS</th><th scope=col>REF</th><th scope=col>all.alleles</th><th scope=col>NA12891.PI</th><th scope=col>NA12891.PG_al</th><th scope=col>log2odds</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>20</td><td> 9795</td><td>G</td><td>G,T</td><td>1</td><td>T|G</td><td>.</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>20</td><td>11799</td><td>C</td><td>C,T</td><td>1</td><td>T|C</td><td>.</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>20</td><td>17094</td><td>A</td><td>A,G</td><td>1</td><td>A|G</td><td>.</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>20</td><td>24962</td><td>C</td><td>C,T</td><td>1</td><td>C|T</td><td>.</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>20</td><td>28655</td><td>A</td><td>A,G</td><td>1</td><td>G|A</td><td>.</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>20</td><td>35416</td><td>A</td><td>A,C</td><td>1</td><td>C|A</td><td>.</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.frame: 6 × 7\n",
              "\\begin{tabular}{r|lllllll}\n",
              "  & CHROM & POS & REF & all.alleles & NA12891.PI & NA12891.PG\\_al & log2odds\\\\\n",
              "  & <int> & <int> & <chr> & <chr> & <dbl> & <chr> & <chr>\\\\\n",
              "\\hline\n",
              "\t1 & 20 &  9795 & G & G,T & 1 & T\\textbar{}G & .\\\\\n",
              "\t2 & 20 & 11799 & C & C,T & 1 & T\\textbar{}C & .\\\\\n",
              "\t3 & 20 & 17094 & A & A,G & 1 & A\\textbar{}G & .\\\\\n",
              "\t4 & 20 & 24962 & C & C,T & 1 & C\\textbar{}T & .\\\\\n",
              "\t5 & 20 & 28655 & A & A,G & 1 & G\\textbar{}A & .\\\\\n",
              "\t6 & 20 & 35416 & A & A,C & 1 & C\\textbar{}A & .\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.frame: 6 × 7\n",
              "\n",
              "| <!--/--> | CHROM &lt;int&gt; | POS &lt;int&gt; | REF &lt;chr&gt; | all.alleles &lt;chr&gt; | NA12891.PI &lt;dbl&gt; | NA12891.PG_al &lt;chr&gt; | log2odds &lt;chr&gt; |\n",
              "|---|---|---|---|---|---|---|---|\n",
              "| 1 | 20 |  9795 | G | G,T | 1 | T|G | . |\n",
              "| 2 | 20 | 11799 | C | C,T | 1 | T|C | . |\n",
              "| 3 | 20 | 17094 | A | A,G | 1 | A|G | . |\n",
              "| 4 | 20 | 24962 | C | C,T | 1 | C|T | . |\n",
              "| 5 | 20 | 28655 | A | A,G | 1 | G|A | . |\n",
              "| 6 | 20 | 35416 | A | A,C | 1 | C|A | . |\n",
              "\n"
            ],
            "text/plain": [
              "  CHROM POS   REF all.alleles NA12891.PI NA12891.PG_al log2odds\n",
              "1 20     9795 G   G,T         1          T|G           .       \n",
              "2 20    11799 C   C,T         1          T|C           .       \n",
              "3 20    17094 A   A,G         1          A|G           .       \n",
              "4 20    24962 C   C,T         1          C|T           .       \n",
              "5 20    28655 A   A,G         1          G|A           .       \n",
              "6 20    35416 A   A,C         1          C|A           .       "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Import \"phased haplotype\" for data/SetA\n",
        "phased_SetA_NA12891 <- read.table('data/SetA_02/phased_NA12891_SetA_run02/extended_haplotype_NA12891.txt', header = TRUE)\n",
        "# or, we can use the file \"extended_haplotype_NA12891.txt\" from the folder \"/data/SetA/phased_NA12891_SetA_run01/\"\n",
        "head(phased_SetA_NA12891)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "rnq3cqitMU10",
        "outputId": "90defd3e-193d-431d-da51-3631776a4902"
      },
      "outputs": [],
      "source": [
        "colnames(phased_SetA_NA12891)[colnames(phased_SetA_NA12891)==\"NA12891.PI\"] <- \"phased.NA12891.PI\"\n",
        "colnames(phased_SetA_NA12891)[colnames(phased_SetA_NA12891)==\"NA12891.PG_al\"] <- \"phased.NA12891.PG_al\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 9</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>CHROM</th><th scope=col>POS</th><th scope=col>true.NA12891.PI</th><th scope=col>true.NA12891.PG_al</th><th scope=col>REF</th><th scope=col>all.alleles</th><th scope=col>phased.NA12891.PI</th><th scope=col>phased.NA12891.PG_al</th><th scope=col>log2odds</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&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;dbl&gt;</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>20</td><td>10003480</td><td>270</td><td>T|G</td><td>G</td><td>G,T</td><td>371</td><td>G|T</td><td>693.02322</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>20</td><td>10007072</td><td>270</td><td>A|C</td><td>A</td><td>A,C</td><td>371</td><td>C|A</td><td>693.02322</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>20</td><td>10008681</td><td>270</td><td>C|T</td><td>C</td><td>C,T</td><td>371</td><td>T|C</td><td>693.02322</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>20</td><td>10012529</td><td>270</td><td>G|A</td><td>A</td><td>A,G</td><td>371</td><td>A|G</td><td>693.02322</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>20</td><td>10015264</td><td>270</td><td>A|G</td><td>A</td><td>A,G</td><td>371</td><td>G|A</td><td>693.02322</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>20</td><td>10016488</td><td>271</td><td>T|C</td><td>C</td><td>C,T</td><td>371</td><td>C|T</td><td>693.02322</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A data.frame: 6 × 9\n",
              "\\begin{tabular}{r|lllllllll}\n",
              "  & CHROM & POS & true.NA12891.PI & true.NA12891.PG\\_al & REF & all.alleles & phased.NA12891.PI & phased.NA12891.PG\\_al & log2odds\\\\\n",
              "  & <int> & <int> & <dbl> & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr>\\\\\n",
              "\\hline\n",
              "\t1 & 20 & 10003480 & 270 & T\\textbar{}G & G & G,T & 371 & G\\textbar{}T & 693.02322\\\\\n",
              "\t2 & 20 & 10007072 & 270 & A\\textbar{}C & A & A,C & 371 & C\\textbar{}A & 693.02322\\\\\n",
              "\t3 & 20 & 10008681 & 270 & C\\textbar{}T & C & C,T & 371 & T\\textbar{}C & 693.02322\\\\\n",
              "\t4 & 20 & 10012529 & 270 & G\\textbar{}A & A & A,G & 371 & A\\textbar{}G & 693.02322\\\\\n",
              "\t5 & 20 & 10015264 & 270 & A\\textbar{}G & A & A,G & 371 & G\\textbar{}A & 693.02322\\\\\n",
              "\t6 & 20 & 10016488 & 271 & T\\textbar{}C & C & C,T & 371 & C\\textbar{}T & 693.02322\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A data.frame: 6 × 9\n",
              "\n",
              "| <!--/--> | CHROM &lt;int&gt; | POS &lt;int&gt; | true.NA12891.PI &lt;dbl&gt; | true.NA12891.PG_al &lt;chr&gt; | REF &lt;chr&gt; | all.alleles &lt;chr&gt; | phased.NA12891.PI &lt;dbl&gt; | phased.NA12891.PG_al &lt;chr&gt; | log2odds &lt;chr&gt; |\n",
              "|---|---|---|---|---|---|---|---|---|---|\n",
              "| 1 | 20 | 10003480 | 270 | T|G | G | G,T | 371 | G|T | 693.02322 |\n",
              "| 2 | 20 | 10007072 | 270 | A|C | A | A,C | 371 | C|A | 693.02322 |\n",
              "| 3 | 20 | 10008681 | 270 | C|T | C | C,T | 371 | T|C | 693.02322 |\n",
              "| 4 | 20 | 10012529 | 270 | G|A | A | A,G | 371 | A|G | 693.02322 |\n",
              "| 5 | 20 | 10015264 | 270 | A|G | A | A,G | 371 | G|A | 693.02322 |\n",
              "| 6 | 20 | 10016488 | 271 | T|C | C | C,T | 371 | C|T | 693.02322 |\n",
              "\n"
            ],
            "text/plain": [
              "  CHROM POS      true.NA12891.PI true.NA12891.PG_al REF all.alleles\n",
              "1 20    10003480 270             T|G                G   G,T        \n",
              "2 20    10007072 270             A|C                A   A,C        \n",
              "3 20    10008681 270             C|T                C   C,T        \n",
              "4 20    10012529 270             G|A                A   A,G        \n",
              "5 20    10015264 270             A|G                A   A,G        \n",
              "6 20    10016488 271             T|C                C   C,T        \n",
              "  phased.NA12891.PI phased.NA12891.PG_al log2odds \n",
              "1 371               G|T                  693.02322\n",
              "2 371               C|A                  693.02322\n",
              "3 371               T|C                  693.02322\n",
              "4 371               A|G                  693.02322\n",
              "5 371               G|A                  693.02322\n",
              "6 371               C|T                  693.02322"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "\n",
        "## Merge the truth and phased data set to identify switch errors\n",
        "merged.data <- merge(truthHaplotype_NA12891, phased_SetA_NA12891,\n",
        "                     by=c(\"CHROM\", \"POS\"))\n",
        "head(merged.data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "u4bUDZv-Mq7N"
      },
      "outputs": [],
      "source": [
        "merged.data <- merged.data[order(merged.data$POS),]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "rownames(merged.data) <- NULL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "MOhLoA7RMv9_"
      },
      "outputs": [],
      "source": [
        "# Now, compare the \"truth haplotype\" with \"phased haplotype\" and ...\n",
        "# ... find the sites where haplotype phasing switched\n",
        "merged.data$match <- ifelse((merged.data$true.NA12891.PG_al == merged.data$phased.NA12891.PG_al), 0, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "Qn3tWuFfN9nD",
        "outputId": "7f0051d5-45c4-48df-890e-f3604ca82385"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<strong>quartz_off_screen:</strong> 3"
            ],
            "text/latex": [
              "\\textbf{quartz\\textbackslash{}\\_off\\textbackslash{}\\_screen:} 3"
            ],
            "text/markdown": [
              "**quartz_off_screen:** 3"
            ],
            "text/plain": [
              "quartz_off_screen \n",
              "                3 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<strong>pdf:</strong> 2"
            ],
            "text/latex": [
              "\\textbf{pdf:} 2"
            ],
            "text/markdown": [
              "**pdf:** 2"
            ],
            "text/plain": [
              "pdf \n",
              "  2 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "Plot with title “Switch points over the genomic coordinates \n",
              "     (before accounting for haplotype breaks).”"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Now, plot the \"Switch Error\" points as png\n",
        "filename <- \"data/rplots/SwitchPoints_SetA02_withOutHaplotypeBreaks.png\"\n",
        "plot(merged.data$POS, merged.data$match, main = \"Switch points over the genomic coordinates \n",
        "     (before accounting for haplotype breaks).\", type = \"s\", \n",
        "     xlab = \"genomic position\", ylab = \"switch errors\")\n",
        "#title(main = \"Switch points\", xlab = 'genomic coordinates', ylab = 'switch points')\n",
        "dev.copy(device = png, filename = filename, width = 1600, height = 600)\n",
        "dev.off()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "shndu02EPNPm",
        "outputId": "99642406-7bef-4b4d-fcf1-6cb0f65a2c5c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "0.0224498241258018"
            ],
            "text/latex": [
              "0.0224498241258018"
            ],
            "text/markdown": [
              "0.0224498241258018"
            ],
            "text/plain": [
              "[1] 0.02244982"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "# Compute switch error rates \n",
        "number_of_hets <- length(merged.data$match)\n",
        "num_of_switch <- 0\n",
        "prev_match <- merged.data$match[1]\n",
        "for (item in merged.data$match){\n",
        "  curr_match = item\n",
        "  if (curr_match != prev_match){\n",
        "    num_of_switch = num_of_switch + 1\n",
        "    \n",
        "    # reset the \"previous match\" value\n",
        "    prev_match = curr_match}}\n",
        "\n",
        "switch_err_rate = num_of_switch/(number_of_hets)\n",
        "switch_err_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "37Z-rroYQYhn",
        "outputId": "76d0fdbf-0215-43e2-cf5a-7802bdadbff9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "9666"
            ],
            "text/latex": [
              "9666"
            ],
            "text/markdown": [
              "9666"
            ],
            "text/plain": [
              "[1] 9666"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## So, now we compute switch point by including changes in \"PI\" values.\n",
        "# Now, the \"switch points\" are addressed when :\n",
        "  # we see 0 -> 1, or when 1 -> 0 in the \"match\" data\n",
        "  # PI of the haplotype block changes\n",
        "    # ** So, now we need to account for the match and also for change in haplotype block index.\n",
        "\n",
        "# the length (number of rows) of the dataframe\n",
        "seq_len <- length(merged.data$match)\n",
        "seq_len\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "H4cFZEVyQaQw"
      },
      "outputs": [],
      "source": [
        "# set empty variables \n",
        "previous_match <- 0\n",
        "hap_size <- 0\n",
        "haplotype_sizes <- integer()  # to store the size of haplotype extended truly.\n",
        "previous_pi <- 1 \n",
        "haplotype_breaks <- integer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hn0hjMebQitg"
      },
      "outputs": [],
      "source": [
        "\n",
        "# run a for loop to find switch points \n",
        "for (ith in c(1:seq_len)){\n",
        "  current_match = merged.data$match[ith]\n",
        "  current_pi = merged.data$phased.NA12891.PI[ith]\n",
        "  if (previous_match == current_match & previous_pi == current_pi) {\n",
        "    hap_size = hap_size + 1}\n",
        "  else if (previous_match != current_match | previous_pi != current_pi) {\n",
        "    # store the haplotype size of properly phased (extended) block\n",
        "    haplotype_sizes <- c(haplotype_sizes, hap_size)\n",
        "    hap_size <- 1}  # reset the haplotype size to 1\n",
        "  \n",
        "  # store the last haplotype size when the for loop reaches the end\n",
        "  if (ith == seq_len){\n",
        "    haplotype_sizes <- c(haplotype_sizes, hap_size)}\n",
        "  \n",
        "  # also find the genomic positions where haplotype breaks occur \n",
        "  if (previous_pi != current_pi){\n",
        "    haplotype_breaks <- c(haplotype_breaks, merged.data$POS[ith])}\n",
        "  \n",
        "  # update the previous match and pi values for next for-loop \n",
        "  previous_match = current_match\n",
        "  previous_pi = current_pi }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "qoq9koAOQkeZ",
        "outputId": "3ad1b70c-0f37-4808-cf4e-102da15a2f2a"
      },
      "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>32</li><li>19</li><li>23</li><li>6</li><li>7</li><li>10</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 32\n",
              "\\item 19\n",
              "\\item 23\n",
              "\\item 6\n",
              "\\item 7\n",
              "\\item 10\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 32\n",
              "2. 19\n",
              "3. 23\n",
              "4. 6\n",
              "5. 7\n",
              "6. 10\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] 32 19 23  6  7 10"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "## Identify switch points and compute \"switch error\" rates. \n",
        "# the total number of haplotype blocks represent frequecy of switch points\n",
        "freq_of_switch = length(haplotype_sizes)\n",
        "total_possible_switch = sum(haplotype_sizes) # or number of hets site\n",
        "head(haplotype_sizes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "QfucJzCWQswU",
        "outputId": "558e7977-933d-44f3-c566-f3699505591e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "0.0245189323401614"
            ],
            "text/latex": [
              "0.0245189323401614"
            ],
            "text/markdown": [
              "0.0245189323401614"
            ],
            "text/plain": [
              "[1] 0.02451893"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# calculate switch error rate\n",
        "switch_error_rate = (freq_of_switch/total_possible_switch)\n",
        "switch_error_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "oPQcezl8QvjN"
      },
      "outputs": [],
      "source": [
        "## Now, add another column that represent the fully accounted haplotype switchpoints.\n",
        "phase_state = 0\n",
        "match_by_pi <- integer()\n",
        "for (sizes in haplotype_sizes) {\n",
        "  match_by_pi <- c(match_by_pi, rep(phase_state, sizes))\n",
        "  \n",
        "  # change the phase state for next loop\n",
        "  if (phase_state == 0){\n",
        "    phase_state = 1}\n",
        "  else if (phase_state == 1){\n",
        "    phase_state = 0} }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "NTgGMgeBR9us"
      },
      "outputs": [],
      "source": [
        "\n",
        "## add data \"match02\" to the dataframe\n",
        "merged.data$match_by_pi <- match_by_pi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3EwlelJSMTl"
      },
      "source": [
        "\n",
        "## Now, make switch points plot by accounting for the haplotype breaks. \n",
        "# for that we will create another column with updated matches between truth and phased haplotypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "-bUs1FUaSA0Q",
        "outputId": "7e121bcc-ed8b-4281-ded6-f9c539bbb922"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<strong>quartz_off_screen:</strong> 3"
            ],
            "text/latex": [
              "\\textbf{quartz\\textbackslash{}\\_off\\textbackslash{}\\_screen:} 3"
            ],
            "text/markdown": [
              "**quartz_off_screen:** 3"
            ],
            "text/plain": [
              "quartz_off_screen \n",
              "                3 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<strong>pdf:</strong> 2"
            ],
            "text/latex": [
              "\\textbf{pdf:} 2"
            ],
            "text/markdown": [
              "**pdf:** 2"
            ],
            "text/plain": [
              "pdf \n",
              "  2 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "Plot with title “Switch points over the genomic coordinates with haplotype breaks.”"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "filename <- \"data/rplots/SwitchPoints_SetA02_withHaplotypeBreaks.png\"\n",
        "plot(merged.data$POS, merged.data$match_by_pi, main = \"Switch points over the genomic coordinates with haplotype breaks.\", type = \"s\", \n",
        "     xlab = \"genomic position\", ylab = \"switch errors\")\n",
        "\n",
        "## We can now overlay the haplotype breaks position on the top of switch points plot\n",
        "abline(v=haplotype_breaks, col='red')\n",
        "dev.copy(device = png, filename = filename, width = 1600, height = 600)\n",
        "\n",
        "dev.off()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6KrFuqzSQ8w"
      },
      "source": [
        "## We can now overlay the haplotype breaks position on the top of switch points plot for convenience let's convert this haplotype size list into integer array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "_Te0eoTJSS6c",
        "outputId": "4402a7e7-c32e-41e7-9abb-91001da7970e"
      },
      "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>32</li><li>19</li><li>23</li><li>6</li><li>7</li><li>10</li></ol>\n"
            ],
            "text/latex": [
              "\\begin{enumerate*}\n",
              "\\item 32\n",
              "\\item 19\n",
              "\\item 23\n",
              "\\item 6\n",
              "\\item 7\n",
              "\\item 10\n",
              "\\end{enumerate*}\n"
            ],
            "text/markdown": [
              "1. 32\n",
              "2. 19\n",
              "3. 23\n",
              "4. 6\n",
              "5. 7\n",
              "6. 10\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "[1] 32 19 23  6  7 10"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "haplotype_size_numeric <- as.numeric(unlist(haplotype_sizes))\n",
        "head(haplotype_size_numeric)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "id": "rETR6JMZSV3z",
        "outputId": "b17fcbd3-9c71-44a3-f681-88af0023f7dc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "Plot with title “Histogram of haplotype_size_numeric”"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<strong>quartz_off_screen:</strong> 3"
            ],
            "text/latex": [
              "\\textbf{quartz\\textbackslash{}\\_off\\textbackslash{}\\_screen:} 3"
            ],
            "text/markdown": [
              "**quartz_off_screen:** 3"
            ],
            "text/plain": [
              "quartz_off_screen \n",
              "                3 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<strong>pdf:</strong> 2"
            ],
            "text/latex": [
              "\\textbf{pdf:} 2"
            ],
            "text/markdown": [
              "**pdf:** 2"
            ],
            "text/plain": [
              "pdf \n",
              "  2 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "Plot with title “Histogram for Haplotype size distribution”"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Let's plot a histogram of the haplotype size distribution \n",
        "hisHap <- hist(haplotype_size_numeric)  # general histogram metrics for the data \n",
        "filename <-\"data/rplots/Histogram_ofHaplotypeSizeDistribution_SetA02.png\"\n",
        "hist(haplotype_size_numeric, \n",
        "     main=\"Histogram for Haplotype size distribution\", \n",
        "     xlab=\"haplotype size\", \n",
        "     border=\"blue\", \n",
        "     col=\"green\",\n",
        "     xlim=c(0,200),\n",
        "     ylim = c(0,320),\n",
        "     las=1, \n",
        "     breaks=20)\n",
        "# add the size of the frequency to the histogram plot \n",
        "text(hisHap$mids,hisHap$counts,labels=hisHap$counts, adj=c(0.5, -0.5))\n",
        "dev.copy(device = png, filename = filename, width = 1600, height = 600)\n",
        "dev.off()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "aYU7Cug2SlSD",
        "outputId": "f4e63d4c-571c-4dea-8508-29be123755c8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<strong>quartz_off_screen:</strong> 3"
            ],
            "text/latex": [
              "\\textbf{quartz\\textbackslash{}\\_off\\textbackslash{}\\_screen:} 3"
            ],
            "text/markdown": [
              "**quartz_off_screen:** 3"
            ],
            "text/plain": [
              "quartz_off_screen \n",
              "                3 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<strong>pdf:</strong> 2"
            ],
            "text/latex": [
              "\\textbf{pdf:} 2"
            ],
            "text/markdown": [
              "**pdf:** 2"
            ],
            "text/plain": [
              "pdf \n",
              "  2 "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "Plot with title “Density plot of the haplotype size distribution”"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Density plot\n",
        "densHap <- density(haplotype_size_numeric)\n",
        "filename <-\"data/rplots/DensityPlot_ofHaplotypeSizeDistribution_SetA02.png\"\n",
        "plot(densHap, main = \"Density plot of the haplotype size distribution\")\n",
        "polygon(densHap, col = 'red', border = 'blue')\n",
        "dev.copy(device = png, filename = filename, width = 1600, height = 600)\n",
        "\n",
        "dev.off()\n",
        "\n",
        "# print(\"Completed the switch error analyses on first round of phaseExtension on Set-A data.\")"
      ]
    },
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