02.Kronos RH score analysis.ipynb

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   "source": [
    "# Identify SNPs in RH regions\n",
    "\n",
    "A region is defined as a group of SNPs that have a gap between any two SNPs in the group less than 60bp. \n",
    "\n",
    "**Criteria to identify RH regions.** Four characteristics were used to differentiate the RH regions from regions carrying real EMS-induced mutations:\n",
    "\n",
    " 1. The RH-SNPs are more likely to be present in multiple individuals in the population, since different inbred plants are pooled and then self-pollinated to generate the commercial seed. Among the identified RH-SNPs, the mode of the distributions of SNP shared by different numbers of individuals was 18 lines in Kronos and 10 in Cadenza (Figs. 3A-B). By contrast, the mode for the non-RH region was 1 line (99% of the mutations were found in a single individual). \n",
    " 2. The RH-SNPs are expected to show a higher percent of non-EMS-type mutations than the regions containing only EMS-induced mutations. Among the identified RH-SNPs, the percentage of non-EMS-type mutations (76.4% in Kronos and 84.4% in Cadenza) was more than 75-fold higher than the percentage detected in the non-RH regions (<1% in both populations, Table 1). \n",
    " 3. SNPs from RH regions are expected to have a higher proportion of homozygous mutations due to the multiple generations of self-pollination during seed increases. The SNPs in the identified RH regions showed a six- to seven-fold lower ratio of heterozygous to homozygous mutations (Kronos = 0.33 and Cadenza = 0.30) than those in the non-RH regions (Kronos = 1.87 and Cadenza = 2.21).\n",
    " 4. RH regions are expected to have a higher average SNP density than the regions including only induced mutations. Among the identified RH-SNPs, SNP densities per individual line were 25.7-fold higher than in the non-RH regions in Kronos and 13.4-fold higher in Cadenza (SI Appendix, Fig. S6).\n",
    " \n",
    "**Bioinformatics pipeline to identify RH regions.** To identify these RH regions, we developed a custom pipeline (https://github.com/DubcovskyLab/wheat_tilling_pub), which uses the output files generated from the MAPS pipeline. The first step of the pipeline breaks large scaffolds in the reference into 10 kb bins to avoid calling all mutations on a large scaffold as RH if it has a small RH region. This was particularly important for the large 3B pseudomolecule. Next, for each bin in each individual the pipeline calculates a score based on the criteria described in the previous section and in SI Appendix, Table S11. Intervals with a score of 12.5 or higher are tagged as RH regions in the database and users are warned in the JBrowse viewer if they are in a RH region. Using this bioinformatics pipeline, we identified 69,651 SNPs in RH regions in the tetraploid population (1.7%), and 38,626 SNPs in RH regions in the hexaploid population (0.6%) at HetMC5/HomMC3 (Table 1).\n",
    "\n",
    "**EMS-type and reciprocal transitions in RH Regions.** Within the identified RH regions of tetraploid wheat, we identified similar numbers of EMS-type G>A and C>T mutations (16,412) and reciprocal A>G and T>C transitions (20,358) at HetMC5/HomMC3. At the same stringency, we also detected similar numbers in Cadenza (EMS-type 6,023 and reciprocal transitions 8,669). We took advantage of this similarity to use the number of A>G and T>C transitions within the non-EMS SNPs as an estimate of the maximum number of non EMS-induced G>A and C>T SNPs that could have been incorrectly included as EMS-type mutations in the non-RH regions.\n",
    "\n",
    "**Real EMS-type induced mutations within RH regions.** Real EMS-type induced mutations are also present within the RH regions and could be tentatively identified by their presence in single lines (see blue arrows in SI Appendix, Fig. S6). However, the relatively high SNP density in the RH regions increases the probability that a linked SNP rather than the induced mutation caused the distinctive phenotype found in the mutant line. Two different strategies can be used to avoid this problem depending on the status of the mutation. For homozygous mutations, the phenotype of the line with the putative EMS-type mutation can be compared the phenotypes of other lines carrying the same RH region(s). If only the plants carrying the putative EMS-induced mutation show the phenotype, this would suggest that the phenotype is not caused by the SNPs present in the linked RH region. For heterozygous mutations, sibling lines with and without the EMS- induced mutation can be compared.\n",
    "\n",
    "# Input\n",
    "\n",
    "The output from the script ```01. Kronos snp windows```\n",
    "\n",
    "# Output\n",
    "\n",
    "A table with the regions on each line containing residual heterogeneity. \n",
    "\n",
    "| Column | Description |\n",
    "|--------|-------------|\n",
    "| LINE   | Mutant line  |\n",
    "| mut_no | Number of mutations in the block|\n",
    "| ems_no | Number of EMS mutations in the block |\n",
    "| hom_sum | $HOM \\subset SNP_{ems\\_gt=\\text{Hom}}, \\#HOM $ |\n",
    "| avg_mut_per_pos | Average mutations per position (across lines in the region). Ideally it shoyld be 1 |\n",
    "| mut_density | Mutation density of the line in the current region.   $ mut\\_density= \\frac{100 * mut\\_no}{region\\_length} $  |\n",
    "| region_id | Unique identifierd of the region  |\n",
    "| CHROM |  Chromosome where the region starts|\n",
    "| region_start | Position of the start of the region |\n",
    "| region_end | Position of the end of the region |\n",
    "| region_length | Length of the region|\n",
    "| window_size | Name of the region/window size.  |\n",
    "| ems_per | Percentage of EMS mutations in region |\n",
    "| hom_per | Percentage of Homozyglous mutations|\n",
    "| no_mut_cat | Number of mutations.  $ mut\\_no = 1 \\implies  \\text{\"1 mut\"} $,  $ mut\\_no \\neq 1 \\implies  \\text{\">=2 mut\"} $ |\n",
    "| no_mut_cat_score | Mutation score. $ mut\\_no = 1 \\implies  0 $,  $ mut\\_no \\neq 1 \\implies  0 $|\n",
    "| per_ems_cat | Category of EMS percentage. $ems\\_per = 0 \\implies \\text{\"0%\"}$,  $ems\\_per \\gt 0 \\land ems\\_per \\lt 50 \\implies \\text{\"<50% and >0%\"}$,   $ems\\_per \\geq 50 \\land ems\\_per \\lt 100 \\implies \\text{\">=50% and <100%\"}$, $ems\\_per = 100 \\implies \\text{\"100%\"}$     |\n",
    "| per_ems_cat_score | Score for EMS percentage.  $ems\\_per = 0 \\implies 6$,  $ems\\_per \\gt 0 \\land ems\\_per \\lt 50 \\implies 3.5$,   $ems\\_per \\geq 50 \\land ems\\_per \\lt 100 \\implies 2.5$, $ems\\_per = 100 \\implies 0$  |\n",
    "| avg_mut_cat | Category of the average mutations. $no\\_mut\\_cat = 1 \\implies \\text{\"1 MI\"}$, $no\\_mut\\_cat \\gt 1 \\land no\\_mut\\_cat \\lt 5 \\implies \\text{\">1 and <= 4 MI\"}$,   $no\\_mut\\_cat \\geq 5 \\land no\\_mut\\_cat \\lt 7 \\implies \\text{\">4 and <= 6 MI\"}$,  $no\\_mut\\_cat \\gt 7 \\implies \\text{\"> 6 MI\"}$  |\n",
    "| avg_mut_cat_score | Score for the mutation categories. $no\\_mut\\_cat = 1 \\implies 0$, $no\\_mut\\_cat \\gt 1 \\land no\\_mut\\_cat \\lt 5 \\implies 1.5$,   $no\\_mut\\_cat \\geq 5 \\land no\\_mut\\_cat \\lt 7 \\implies 2$,  $no\\_mut\\_cat \\gt 7 \\implies 3$    |\n",
    "| mut_dens_cat |Category of the mutation density. $mut\\_density \\leq 0.2 \\implies \\text{\"<= 0.2 density\"} $, $mut\\_density \\gt 0.2 \\land mut\\_density \\lt 0.4 \\implies \\text{\">0.2 and <0.4 density\"} $,   $mut\\_density \\geq 0.4 \\land mut\\_density \\lt 0.7 \\implies \\text{\">0.4 and <0.7 density\"} $,  $mut\\_density \\geq 0.7 \\implies \\text{\">= 0.7 density\"} $   |\n",
    "| mut_dens_cat_score | Score for the mutation density. $mut\\_density \\leq 0.2 \\implies 0$, $mut\\_density \\gt 0.2 \\land mut\\_density \\lt 0.4 \\implies 1.5$,   $mut\\_density \\geq 0.4 \\land mut\\_density \\lt 0.7 \\implies 2$,  $mut\\_density \\geq 0.7 \\implies 3$   |\n",
    "| combined_score |  $hom\\_per < 25 \\lor (hom\\_per > 25 \\land ems\\_per > 75 \\land avg\\_mut\\_per\\_pos < 4) \\implies 0 $, $ (hom\\_per > 25 \\land ems\\_per \\leq 75) \\lor (hom\\_per > 25 \\land ems\\_per > 75  \\land avg\\_mut\\_per\\_pos \\geq 4) \\implies 12.5$   |\n",
    "| score | Total RH score. $no\\_mut\\_cat\\_score + per\\_ems\\_cat\\_score + avg\\_mut\\_cat\\_score + mut\\_dens\\_cat\\_score + combined\\_score$. |\n",
    "\n",
    "The regions with $score > 12$ are selected as regions with RH. \n",
    "\n",
    "A simplified version of the table, only with the regions and excluding the details is saved, including only the columns: ```CHROM```, ```region_start```, ```region_end```, ```LINE```, ```region_id```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: gsubfn\n",
      "Loading required package: proto\n",
      "Loading required package: RSQLite\n"
     ]
    }
   ],
   "source": [
    "library(sqldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "snps<-read.csv(\n",
    "    gzfile('./out/20190709_Kronos_snps_with_density_and_het_gap_60.tsv.gz','rt'),\n",
    "    header=T, sep=\"\\t\")\n",
    "snps$window_size <- \"gap_60\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions <- sqldf(\"\n",
    "SELECT \n",
    "  LINE, \n",
    "  mut_no,\n",
    "  ems_per, \n",
    "  SUM(CASE WHEN EMS_GT = 'Hom' THEN \n",
    "        1 \n",
    "     ELSE \n",
    "        0 \n",
    "     END) as 'hom_sum',\n",
    "  avg_mut_per_pos, \n",
    "  mut_density, \n",
    "  region_id, \n",
    "  CHROM,\n",
    "  region_start,\n",
    "  region_end,\n",
    "  region_length, \n",
    "  window_size\n",
    "FROM snps \n",
    "GROUP BY region_id, LINE, window_size \")\n",
    "\n",
    "colnames(regions) <- c(\"LINE\", \"mut_no\", \"ems_no\",\"hom_sum\" ,\"avg_mut_per_pos\", \"mut_density\", \"region_id\",\"CHROM\", \"region_start\" , \"region_end\", \"region_length\", \"window_size\")\n",
    "regions$ems_per <- 100.0 *  regions$ems_no / regions$mut_no\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$hom_per <- 100.0 * regions$hom_sum / regions$mut_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$no_mut_cat  <- ifelse(regions$mut_no == 1, \"1 mut\", \">=2 mut\" )\n",
    "regions$no_mut_cat_score  <- ifelse(regions$mut_no == 1, 0, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$per_ems_cat <- ifelse(regions$ems_per==0, \"0%\",\n",
    "                             ifelse(regions$ems_per < 50, \"<50% and >0%\",\n",
    "                                   ifelse(regions$ems_per < 100, \">=50% and <100%\", \n",
    "                                         \"100%\")))\n",
    "regions$per_ems_cat_score <- ifelse(regions$ems_per==0, 6,\n",
    "                             ifelse(regions$ems_per < 50, 3.5,\n",
    "                                   ifelse(regions$ems_per < 100, 2.5, \n",
    "                                         0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$avg_mut_cat<-ifelse(regions$avg_mut_per_pos==1, \"1 MI\",\n",
    "                             ifelse(regions$avg_mut_per_pos < 5, \">1 and <= 4 MI\",\n",
    "                                   ifelse(regions$avg_mut_per_pos < 7, \">4 and <= 6 MI\", \n",
    "                                         \"> 6 MI\")))\n",
    "regions$avg_mut_cat_score<-ifelse(regions$avg_mut_per_pos==1, 0,\n",
    "                             ifelse(regions$avg_mut_per_pos < 5, 1.5,\n",
    "                                   ifelse(regions$avg_mut_per_pos < 7, 2, \n",
    "                                         3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$mut_dens_cat<-ifelse(regions$mut_density <= 0.2, \"<= 0.2 density\",\n",
    "                             ifelse(regions$mut_density < 0.4, \">0.2 and <0.4 density\",\n",
    "                                   ifelse(regions$mut_density < 0.7, \">0.4 and <0.7 density\", \n",
    "                                         \">= 0.7 density\")))\n",
    "regions$mut_dens_cat_score<-ifelse(regions$mut_density <= 0.2, 0,\n",
    "                             ifelse(regions$mut_density < 0.4, 1.5,\n",
    "                                   ifelse(regions$mut_density < 0.7, 2, \n",
    "                                         3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$combined_score<-ifelse(regions$hom_per >= 25 ,\n",
    "                               ifelse(regions$ems_per <= 75, 12.5,\n",
    "                                     ifelse(regions$avg_mut_per_pos >= 4, 12.5,\n",
    "                                            0)\n",
    "                                     )\n",
    "                        ,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions$score <- regions$no_mut_cat_score + regions$per_ems_cat_score + regions$avg_mut_cat_score + regions$mut_dens_cat_score + regions$combined_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_ems_regions <-  regions[regions$score > 0 & regions$region_length > 1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_regions <- unique(regions[,c(\"region_id\",\"region_length\", \"window_size\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Using alpha for a discrete variable is not advised.”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wDXvznmtb+JLbeh6dFFSpZ03DCl955RU3LE7DBm+88UZ3qLZnV28F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\nAggggAACCCCQg8CSJUusXbt29uyzz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SYEEEAAAQQQCLUAAVKom5/Kl7PA9Fb7eBVvU69cZEIAAQQQQAABBBDw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\nRh9HEPSfcdVjyRK/HopybSN9yKjft/qgMchJP+N6vEW69wlBqZf+9rVv396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GG+2dd96xPn362HPPPWcLFy60c889N+kS77//vivX7bff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\na4lMZZ8zhrwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <- ggplot(non_ems_regions, aes(x=mut_density, fill=per_ems_cat, alpha=avg_mut_cat )) + \n",
    "geom_histogram(binwidth = 0.1) + coord_cartesian(xlim = c(0,5))+\n",
    "facet_wrap(~window_size) \n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Using alpha for a discrete variable is not advised.”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CCCCAAAIIpJoAAVKq3XHq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sWdKdg6VKlUID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\ngECABf4/LR+9iwf7PsIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <- ggplot(non_ems_regions, aes(x=score, fill=per_ems_cat, alpha=avg_mut_cat )) + \n",
    "geom_histogram(binwidth = 1) +\n",
    "facet_wrap(~window_size) \n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Using alpha for a discrete variable is not advised.”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Add1117meKc1XUpBw2WWXWfXq1e3QoUNuHpDX2xJS0OEN9bpojtO/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5j02aNMlWrlxZ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\nrrcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <- ggplot(non_ems_regions, aes(x=score, fill=per_ems_cat, alpha=avg_mut_cat )) + \n",
    "geom_histogram(binwidth = 1) + coord_cartesian(ylim = c(0,100000)) +\n",
    "facet_wrap(~window_size) \n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_regions_over_1 <- unique_regions[unique_regions$region_length > 1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$gap_60\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "      2      38      85     134     178    3332 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(unique_regions_over_1$region_length, unique_regions_over_1$window_size, summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>LINE</th><th scope=col>mut_no</th><th scope=col>ems_no</th><th scope=col>hom_sum</th><th scope=col>avg_mut_per_pos</th><th scope=col>mut_density</th><th scope=col>region_id</th><th scope=col>CHROM</th><th scope=col>region_start</th><th scope=col>region_end</th><th scope=col>⋯</th><th scope=col>no_mut_cat</th><th scope=col>no_mut_cat_score</th><th scope=col>per_ems_cat</th><th scope=col>per_ems_cat_score</th><th scope=col>avg_mut_cat</th><th scope=col>avg_mut_cat_score</th><th scope=col>mut_dens_cat</th><th scope=col>mut_dens_cat_score</th><th scope=col>combined_score</th><th scope=col>score</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td>Kronos4557                                                      </td><td>1                                                               </td><td>0                                                               </td><td>1                                                               </td><td>1.000000                                                        </td><td><span style=white-space:pre-wrap>  1.8518519</span>             </td><td> 1                                                              </td><td>chr1A                                                           </td><td> 227237                                                         </td><td> 227291                                                         </td><td>⋯                                                               </td><td><span style=white-space:pre-wrap>1 mut  </span>                 </td><td>0                                                               </td><td><span style=white-space:pre-wrap>0%          </span>            </td><td>6.0                                                             </td><td><span style=white-space:pre-wrap>1 MI          </span>          </td><td>0.0                                                             </td><td><span style=white-space:pre-wrap>&gt;= 0.7 density       </span></td><td>3.0                                                             </td><td>12.5                                                            </td><td>21.5                                                            </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Kronos2317                                                      </td><td>1                                                               </td><td>0                                                               </td><td>1                                                               </td><td>1.000000                                                        </td><td>100.0000000                                                     </td><td> 4                                                              </td><td>chr1A                                                           </td><td> 805669                                                         </td><td> 805670                                                         </td><td>⋯                                                               </td><td><span style=white-space:pre-wrap>1 mut  </span>                 </td><td>0                                                               </td><td><span style=white-space:pre-wrap>0%          </span>            </td><td>6.0                                                             </td><td><span style=white-space:pre-wrap>1 MI          </span>          </td><td>0.0                                                             </td><td><span style=white-space:pre-wrap>&gt;= 0.7 density       </span></td><td>3.0                                                             </td><td>12.5                                                            </td><td>21.5                                                            </td></tr>\n",
       "\t<tr><th scope=row>101</th><td>Kronos1108                                            </td><td>1                                                     </td><td>0                                                     </td><td>1                                                     </td><td>1.000000                                              </td><td><span style=white-space:pre-wrap>  0.3831418</span>   </td><td>24                                                    </td><td>chr1A                                                 </td><td>1160657                                               </td><td>1160918                                               </td><td>⋯                                                     </td><td><span style=white-space:pre-wrap>1 mut  </span>       </td><td>0                                                     </td><td><span style=white-space:pre-wrap>0%          </span>  </td><td>6.0                                                   </td><td><span style=white-space:pre-wrap>1 MI          </span></td><td>0.0                                                   </td><td>&gt;0.2 and &lt;0.4 density                           </td><td>1.5                                                   </td><td>12.5                                                  </td><td>20.0                                                  </td></tr>\n",
       "\t<tr><th scope=row>155</th><td>Kronos376                                                       </td><td>9                                                               </td><td>2                                                               </td><td>0                                                               </td><td>1.666667                                                        </td><td><span style=white-space:pre-wrap>  6.8702290</span>             </td><td>29                                                              </td><td>chr1A                                                           </td><td>1162385                                                         </td><td>1162516                                                         </td><td>⋯                                                               </td><td>&gt;=2 mut                                                      </td><td>7                                                               </td><td>&lt;50% and &gt;0%                                              </td><td>3.5                                                             </td><td>&gt;1 and &lt;= 4 MI                                            </td><td>1.5                                                             </td><td><span style=white-space:pre-wrap>&gt;= 0.7 density       </span></td><td>3.0                                                             </td><td> 0.0                                                            </td><td>15.0                                                            </td></tr>\n",
       "\t<tr><th scope=row>265</th><td>Kronos1434                                                      </td><td>2                                                               </td><td>0                                                               </td><td>0                                                               </td><td>2.500000                                                        </td><td><span style=white-space:pre-wrap>  0.7352941</span>             </td><td>41                                                              </td><td>chr1A                                                           </td><td>1170589                                                         </td><td>1170861                                                         </td><td>⋯                                                               </td><td>&gt;=2 mut                                                      </td><td>7                                                               </td><td><span style=white-space:pre-wrap>0%          </span>            </td><td>6.0                                                             </td><td>&gt;1 and &lt;= 4 MI                                            </td><td>1.5                                                             </td><td><span style=white-space:pre-wrap>&gt;= 0.7 density       </span></td><td>3.0                                                             </td><td> 0.0                                                            </td><td>17.5                                                            </td></tr>\n",
       "\t<tr><th scope=row>266</th><td>Kronos2303                                                      </td><td>4                                                               </td><td>0                                                               </td><td>0                                                               </td><td>2.500000                                                        </td><td><span style=white-space:pre-wrap>  1.4705882</span>             </td><td>41                                                              </td><td>chr1A                                                           </td><td>1170589                                                         </td><td>1170861                                                         </td><td>⋯                                                               </td><td>&gt;=2 mut                                                      </td><td>7                                                               </td><td><span style=white-space:pre-wrap>0%          </span>            </td><td>6.0                                                             </td><td>&gt;1 and &lt;= 4 MI                                            </td><td>1.5                                                             </td><td><span style=white-space:pre-wrap>&gt;= 0.7 density       </span></td><td>3.0                                                             </td><td> 0.0                                                            </td><td>17.5                                                            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllll}\n",
       "  & LINE & mut\\_no & ems\\_no & hom\\_sum & avg\\_mut\\_per\\_pos & mut\\_density & region\\_id & CHROM & region\\_start & region\\_end & ⋯ & no\\_mut\\_cat & no\\_mut\\_cat\\_score & per\\_ems\\_cat & per\\_ems\\_cat\\_score & avg\\_mut\\_cat & avg\\_mut\\_cat\\_score & mut\\_dens\\_cat & mut\\_dens\\_cat\\_score & combined\\_score & score\\\\\n",
       "\\hline\n",
       "\t2 & Kronos4557            & 1                     & 0                     & 1                     & 1.000000              &   1.8518519           &  1                    & chr1A                 &  227237               &  227291               & ⋯                     & 1 mut                 & 0                     & 0\\%                  & 6.0                   & 1 MI                  & 0.0                   & >= 0.7 density        & 3.0                   & 12.5                  & 21.5                 \\\\\n",
       "\t6 & Kronos2317            & 1                     & 0                     & 1                     & 1.000000              & 100.0000000           &  4                    & chr1A                 &  805669               &  805670               & ⋯                     & 1 mut                 & 0                     & 0\\%                  & 6.0                   & 1 MI                  & 0.0                   & >= 0.7 density        & 3.0                   & 12.5                  & 21.5                 \\\\\n",
       "\t101 & Kronos1108            & 1                     & 0                     & 1                     & 1.000000              &   0.3831418           & 24                    & chr1A                 & 1160657               & 1160918               & ⋯                     & 1 mut                 & 0                     & 0\\%                  & 6.0                   & 1 MI                  & 0.0                   & >0.2 and <0.4 density & 1.5                   & 12.5                  & 20.0                 \\\\\n",
       "\t155 & Kronos376             & 9                     & 2                     & 0                     & 1.666667              &   6.8702290           & 29                    & chr1A                 & 1162385               & 1162516               & ⋯                     & >=2 mut               & 7                     & <50\\% and >0\\%      & 3.5                   & >1 and <= 4 MI        & 1.5                   & >= 0.7 density        & 3.0                   &  0.0                  & 15.0                 \\\\\n",
       "\t265 & Kronos1434            & 2                     & 0                     & 0                     & 2.500000              &   0.7352941           & 41                    & chr1A                 & 1170589               & 1170861               & ⋯                     & >=2 mut               & 7                     & 0\\%                  & 6.0                   & >1 and <= 4 MI        & 1.5                   & >= 0.7 density        & 3.0                   &  0.0                  & 17.5                 \\\\\n",
       "\t266 & Kronos2303            & 4                     & 0                     & 0                     & 2.500000              &   1.4705882           & 41                    & chr1A                 & 1170589               & 1170861               & ⋯                     & >=2 mut               & 7                     & 0\\%                  & 6.0                   & >1 and <= 4 MI        & 1.5                   & >= 0.7 density        & 3.0                   &  0.0                  & 17.5                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | LINE | mut_no | ems_no | hom_sum | avg_mut_per_pos | mut_density | region_id | CHROM | region_start | region_end | ⋯ | no_mut_cat | no_mut_cat_score | per_ems_cat | per_ems_cat_score | avg_mut_cat | avg_mut_cat_score | mut_dens_cat | mut_dens_cat_score | combined_score | score | \n",
       "|---|---|---|---|---|---|\n",
       "| 2 | Kronos4557            | 1                     | 0                     | 1                     | 1.000000              |   1.8518519           |  1                    | chr1A                 |  227237               |  227291               | ⋯                     | 1 mut                 | 0                     | 0%                    | 6.0                   | 1 MI                  | 0.0                   | >= 0.7 density        | 3.0                   | 12.5                  | 21.5                  | \n",
       "| 6 | Kronos2317            | 1                     | 0                     | 1                     | 1.000000              | 100.0000000           |  4                    | chr1A                 |  805669               |  805670               | ⋯                     | 1 mut                 | 0                     | 0%                    | 6.0                   | 1 MI                  | 0.0                   | >= 0.7 density        | 3.0                   | 12.5                  | 21.5                  | \n",
       "| 101 | Kronos1108            | 1                     | 0                     | 1                     | 1.000000              |   0.3831418           | 24                    | chr1A                 | 1160657               | 1160918               | ⋯                     | 1 mut                 | 0                     | 0%                    | 6.0                   | 1 MI                  | 0.0                   | >0.2 and <0.4 density | 1.5                   | 12.5                  | 20.0                  | \n",
       "| 155 | Kronos376             | 9                     | 2                     | 0                     | 1.666667              |   6.8702290           | 29                    | chr1A                 | 1162385               | 1162516               | ⋯                     | >=2 mut               | 7                     | <50% and >0%          | 3.5                   | >1 and <= 4 MI        | 1.5                   | >= 0.7 density        | 3.0                   |  0.0                  | 15.0                  | \n",
       "| 265 | Kronos1434            | 2                     | 0                     | 0                     | 2.500000              |   0.7352941           | 41                    | chr1A                 | 1170589               | 1170861               | ⋯                     | >=2 mut               | 7                     | 0%                    | 6.0                   | >1 and <= 4 MI        | 1.5                   | >= 0.7 density        | 3.0                   |  0.0                  | 17.5                  | \n",
       "| 266 | Kronos2303            | 4                     | 0                     | 0                     | 2.500000              |   1.4705882           | 41                    | chr1A                 | 1170589               | 1170861               | ⋯                     | >=2 mut               | 7                     | 0%                    | 6.0                   | >1 and <= 4 MI        | 1.5                   | >= 0.7 density        | 3.0                   |  0.0                  | 17.5                  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "    LINE       mut_no ems_no hom_sum avg_mut_per_pos mut_density region_id\n",
       "2   Kronos4557 1      0      1       1.000000          1.8518519  1       \n",
       "6   Kronos2317 1      0      1       1.000000        100.0000000  4       \n",
       "101 Kronos1108 1      0      1       1.000000          0.3831418 24       \n",
       "155 Kronos376  9      2      0       1.666667          6.8702290 29       \n",
       "265 Kronos1434 2      0      0       2.500000          0.7352941 41       \n",
       "266 Kronos2303 4      0      0       2.500000          1.4705882 41       \n",
       "    CHROM region_start region_end ⋯ no_mut_cat no_mut_cat_score per_ems_cat \n",
       "2   chr1A  227237       227291    ⋯ 1 mut      0                0%          \n",
       "6   chr1A  805669       805670    ⋯ 1 mut      0                0%          \n",
       "101 chr1A 1160657      1160918    ⋯ 1 mut      0                0%          \n",
       "155 chr1A 1162385      1162516    ⋯ >=2 mut    7                <50% and >0%\n",
       "265 chr1A 1170589      1170861    ⋯ >=2 mut    7                0%          \n",
       "266 chr1A 1170589      1170861    ⋯ >=2 mut    7                0%          \n",
       "    per_ems_cat_score avg_mut_cat    avg_mut_cat_score mut_dens_cat         \n",
       "2   6.0               1 MI           0.0               >= 0.7 density       \n",
       "6   6.0               1 MI           0.0               >= 0.7 density       \n",
       "101 6.0               1 MI           0.0               >0.2 and <0.4 density\n",
       "155 3.5               >1 and <= 4 MI 1.5               >= 0.7 density       \n",
       "265 6.0               >1 and <= 4 MI 1.5               >= 0.7 density       \n",
       "266 6.0               >1 and <= 4 MI 1.5               >= 0.7 density       \n",
       "    mut_dens_cat_score combined_score score\n",
       "2   3.0                12.5           21.5 \n",
       "6   3.0                12.5           21.5 \n",
       "101 1.5                12.5           20.0 \n",
       "155 3.0                 0.0           15.0 \n",
       "265 3.0                 0.0           17.5 \n",
       "266 3.0                 0.0           17.5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rh_regions <- regions[regions$score > 12,]\n",
    "head(rh_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "280224"
      ],
      "text/latex": [
       "280224"
      ],
      "text/markdown": [
       "280224"
      ],
      "text/plain": [
       "[1] 280224"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(rh_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for_bed<-rh_regions[,c(\"CHROM\", \"region_start\", \"region_end\", \"LINE\", \"region_id\")]\n",
    "write.csv(for_bed, \"./out/RH_Kronos_for_bed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gz1 <- gzfile(\"./out/RH_Kronos_regions.csv.gz\", \"w\")\n",
    "write.csv(rh_regions, gz1)\n",
    "close(gz1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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