---
title: 'Report breakdown by ID'
author: 'ampliCan'
date: '`r format(Sys.time(), "%d %B %Y")`'
output:
  html_document:
    toc: true
    theme: paper
    toc_float: true
    number_sections: true
params:
    alignments: !r system.file('extdata', 'results', 'alignments','events_filtered_shifted_normalized.csv', package = 'amplican')
    config_summary: !r system.file('extdata', 'results', 'config_summary.csv', package = 'amplican')
    cut_buffer: 5
    xlab_spacing: 4
vignette: >
  %\VignetteIndexEntry{example id_report report}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r load data, message=F, warning=FALSE, include=FALSE}
library(amplican)
library(ggplot2)
alignments <- data.table::fread(params$alignments)
data.table::setDF(alignments)
config <- data.frame(data.table::fread(params$config_summary))
height <- plot_height(length(unique(config$ID)))
```

***

# Description  

***

**Read distribution plot** - plot shows number of reads assigned during read grouping  
**Filtered Reads** - plot shows percentage of assigned reads that have been recognized as PRIMER DIMERS or filtered based on low alignment score  
**Edit rates** - plot gives overview of percentage of reads (not filtered as PRIMER DIMER) that have edits  
**Frameshift** - plot shows what percentage of reads that have frameshift  
**Read heterogeneity plot** - shows what is the share of each of the unique reads in total count of all reads. The more yellow each row, the less heterogeneity in the reads, more black means reads don't repeat often and are unique  
**Deletions plot** - shows summary of deletions detected after alignments with distinction
for forward (top plot) and reverse (bottom) reads, blue dotted lines represent primers as black
dotted line represents cut site box, for deletions overlapping with cut site box there is distinction
in color  
**Mismatches plot** - shows summary of mismatches detected after alignments split by forward
(top plot) and reverse (bottom) reads, mismatches are colored in the same manner as amplicon  
**Insertions plot** - shows summary of insertions detected after alignments split by forward
(top plot) and reverse (bottom) reads, insertion is shown as right-angled triangle where size of
the insertion corresponds to the width of the triangle, size and transparency of triangle reflect on
the frequency of the insertion

***

# ID Summary  

***

## Read distribution  

```{r plot_total_reads, echo=FALSE, fig.height=height, fig.width=14, message=F, warning=FALSE}
ggplot(data = config, aes(x = as.factor(ID), y = log10(Reads + 1), order = ID)) +
  geom_bar(stat='identity') +
  ylab('Number of reads + 1, log10 scaled')  +
  xlab('ID') +
  theme(legend.position = 'none',
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14, face = 'bold')) +
  coord_flip() +
  geom_text(aes(x = as.factor(ID), y = log10(Reads + 1), label = Reads), hjust = -1)
```

## Filtered reads  

```{r plot_F_per, echo=FALSE, fig.height=height, fig.width=14, message=F, warning=FALSE}
config$PRIMER_DIMER <- config$PRIMER_DIMER * 100/config$Reads
config$PRIMER_DIMER[is.nan(config$PRIMER_DIMER)] <- 0  
config$Low_Score <- config$Low_Score * 100/config$Reads
config$Low_Score[is.nan(config$Low_Score)] <- 0  

config_melt <- data.table::melt(data.table::as.data.table(config), id.vars = "ID", 
                                measure.vars = c("PRIMER_DIMER", "Low_Score"))
ggplot(data = config_melt, 
       aes(x = as.factor(ID), y = value, fill = variable, order = ID)) +
  geom_bar(stat='identity') +
  ylab('Percentage of filtered reads')  +
  xlab('ID') +
  theme(legend.position = 'top',
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14, face = 'bold')) +
  coord_flip() +
  labs(fill = "")
```  

## Edit rates  

```{r plot mut percentage, echo=FALSE, fig.height=height, fig.width=14, message=F, warning=FALSE}
config$edit_percentage <- config$Reads_Edited * 100/config$Reads_Filtered
config$edit_percentage[is.nan(config$edit_percentage)] <- 0  

ggplot(data = config, aes(x = as.factor(ID), y = edit_percentage, order = ID)) +
  geom_bar(stat='identity') +
  ylab('Percentage of reads (not filtered) that have edits')  +
  xlab('ID') +
  theme(legend.position = 'none',
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14, face = 'bold')) +
  coord_flip() +
  geom_text(aes(x = as.factor(ID), y = edit_percentage, label = Reads_Edited), hjust = -1)
```  

## Frameshift  

```{r plot_frameshift_per, echo=FALSE, fig.height=height, fig.width=14, message=F, warning=FALSE}
config$frameshift_percentage <- config$Reads_Frameshifted * 100/config$Reads_Filtered
config$frameshift_percentage[is.nan(config$frameshift_percentage)] <- 0  

ggplot(data = config, aes(x = as.factor(ID), y = frameshift_percentage, order = ID)) +
  geom_bar(stat='identity') +
  ylab('Percentage of reads (not filtered) that have frameshift')  +
  xlab('ID') +
  theme(legend.position = 'none',
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14, face = 'bold')) +
  coord_flip() +
  geom_text(aes(x = as.factor(ID), y = frameshift_percentage, label = Reads_Frameshifted), hjust = -1)
```  

## Heterogeneity of reads  

```{r plot read domination, echo=FALSE, fig.height=height + 1, fig.width=14, message=F, warning=FALSE}
plot_heterogeneity(alignments, config)
```  

***

# Alignments plots  

***

```{r plot_alignments, results='asis', echo=F, message=F, warning=F}
alignments_cons <- alignments[alignments$consensus & alignments$overlaps, ]
src = sapply(config$ID, function(i) {
  knitr::knit_expand(text = c(
    "## {{i}}  \n", 
    "### Deletions  \n", 
    paste('```{r del-{{i}}, echo = F, results = "asis", ',
          'fig.width=25, message=F, warning=F}', collapse = ''), 
    paste('p <- amplican::plot_deletions(alignments, config, "{{i}}",',
          ' params$cut_buffer, params$xlab_spacing)', collapse = ''), 
    '```\n',
    "### Insertions  \n", 
    paste('```{r ins-{{i}}, echo = F, results = "asis", ',
          'fig.width=25, message=F, warning=F}', collapse = ''), 
    paste('p <- amplican::plot_insertions(alignments, config, "{{i}}",',
          ' params$cut_buffer, params$xlab_spacing)', collapse = ''), 
    '```\n', 
    "### Mismatches  \n", 
    paste('```{r mis-{{i}}, echo = F, results = "asis", ',
          'fig.width=25, message=F, warning=F}', collapse = ''), 
    paste('p <- amplican::plot_mismatches(alignments, config, "{{i}}",',
          ' params$cut_buffer, params$xlab_spacing)', collapse = ''), 
    '```\n', 
    "### Variants  \n", 
    paste('```{r var-{{i}}, echo = F, message=F, results = "asis", ',
          'message=F, warning=F}', collapse = ''),
    paste('p <- amplican::plot_variants(alignments_cons, config, "{{i}}", ',
          ' params$cut_buffer)', collapse = ''),
    '```\n'))
})
# knit the source
res = knitr::knit_child(text = src, quiet = TRUE)
cat(res, sep = '\n')
```

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1TJrnkDYNU+y1509ycLJqx2DWOcFmWpHUvCKo2wni/ZNc9IWXbXAJ9DzGdOa7Tp2GnCUjuWhNU1VcIsnivZNU9PWXbnAJ/Dszndm7yENUWPgoSlCEsRVis/AgMpLEVYEpaEVVDSHs6BntG6k8JKu9ZN9aZQwipLlbA0wjKzVek/4YYXVFgT23Fgd3+W/m8KRwBr6nGQsBRhFS+66lumHnb3Vwf4PF4AlmekGRvG7GoHaf3qJutxkLAkrGKxWcqygW6/Wjmg3ks53Z9meETCkrAkrOKTNv/i/A6dSyfbsRRhSVgSVhewfmS0UUVhrafHQcIqLGa2CjA+q/ZC0t5SZmE9XEFhLVCEJWF1G6uRPZb7ojZOeVUUYT0kYUlYEpaqg0WLKEemCKAHeLxDp/R8TvcoL2Gtp75YEpaEVazoqu8D+WiYyr1SEVYYfWNRn8XDUV8sCavAVKrTKMVqcO90lVDVQgmr6xgfkUYN7uUVVlpV+K16LCSsojIqIs0iCaujwpowwMdfXY+FhNXNwlpcoutdr2DCejkizegBFtZEPRYSVjcL69USXW9adeeJDp5PzI9BO4X1vIQlYXUTMQ9DmYSVNoLns506GXdfCmRNKza0TdN9qUooYSnCKiqhf9HqRRJWA/nbrihLwpKwJKyCMg4Y1rdK5u5LOnxei3O6T6oSSlgSFuVpdF+zgNGVIiwJSyjCSiWt/eqZApxXJxvenyf5uL03E8LkukLCkrAUYRVHWOGj9r797AYDb9GjIWFJWMWLsLpFWKPaeHy1Y0lYpRJWWdqwiviGMPYHYaD7Yk3QoyFhKcIqXpWw6m1YtY4/So+GhFUowmwsQzKSvVmiwfsmREYXVRPWqxKWhKXoqniMSVlWhA+7X83pXklYEpaEVaLrHd1kdFPFKuFoPR4SloRVvAjrlS4RliIsCavyjJSwuibCGiNhSVhVZ0hEmjdKXiUsQoQV8y3jCAlLwpKwsukpw4WG+Rf7RpTLI2XRbmLyeBUJS8KqOoNzepi6tjro7i5hSVgSVnewSk4PU7dWB4vSg39ZTveqWfSWUMKSsLogwnqlIOemCEvCEjk9BMsVYRVCWEPaePw0YY3U4yFhKcKSsIoYYS0b4ONJWKIpqtToPixl2VIJq2YULWFJWIqwCnatRbm2Tje69zT5YyYkLAmrTQxpUhRFibCGDPDxFWFJWF0prOUlvtZuEpaqhBKWhFXxCKso19ZpYalKKGF1BVVqdFeEpSqhhKUISxFWDixr8vwlLAlLwuqD2rDKH2GpDUvCUoSlCKurq4Rqw5KwJCxFWF0jLEVYEpaEpQgrFbVhSVgigpjpu14vcRkqRITl7ivI/q5xSRuP35MiLdfjIWEVjb/nlKYbeDBl2X0FOr+5La5vlev6/H+eHg8Jq2hckfHQ3hfSlIHfABf3+v/FYVlR+FadiG8Z8O02H/8Q4FqSD8KvDf8XTWLFGMm2hBlrNiVIae0+q54AZrv7PSW73qmhGnR3Ac9tH+AMYGyvxYuAT7j7b1VaJSyRPCjjgU8BO4ZFfwVOc/eFyp0BvxdjgT2BScBTwB/cfZFyRsISQoi2oDYsIYSEJYQQEpYQQsISQggJSwghJCxRBMzsKjPzGn9zlUMiT/Qhpuik7NYBDgVmknxjdyvwC3d/RrkjJCxRJFltB1zOP091vydwhJm9191vbWHfawN7A7OCDFcn6eW+DHgp/M0n+Y7wWne/U3ekS8qNOo6KVquEwHtqrL7O3Xeosd19wMY1trvD3Tdv4lx2AI4F3g1YA5veAvwIuMDdl+muFhe1YYlOSG6DOrICmGlmkxrY33gzu5Dk06f3NCgrgC2BXwE3hW9AhYQlxD8YnlMazGwacBvwrzmc10zgFjP7qG6RhCXESu4HXq6z/llgQaSsrgbWy1mmZ4URHoSEJapOaCc6uk6SL4fRQuvJahxwCTCxDac4GJhjZtvobhULvSUUnZLWL8zseeDLoSoGSbeG4939jxG7ODUisuoJEdh1wNPhB3oCsDmwC/88PlZfhgL/ZWZbZ8lTSFiiGtK6CLio0e3MbCdg34xklwGHufvjNfYxMsjyG9SeeuttJP3ETtHdUpVQiGb5Xsb6XwO715JVkOUSdz82CKkehyu7FWG1hJkNBWYDO5O8kl4fGBeu52XgReAOkgH/f+vuT7bhHEYCuwN7AdOBt5J0glxI0inxcuB0d3+uxvbjgY8Cu4btJwIrgBeAB4ArgXPyPvfQpeBgYDdgXWB0qC49DlwKnO/uCwp877ck6RBai0eAgz2yg6G7n2lmB5D03UpjupnNiO1cWpCyOQjYI0Sh25AM0z0olK2He5WtBV338Lt71/yFh+uYkPEe+bcMmAOsHbH/cXX20wMMCun2IxlmN+vYS4BD+xxjEPAV4NWI7V8D/p3QwbfOeY+qs4+FIc1gkk6VPRnHXE7SiXJE5D25qs6+5tbZbm6d7a6qs93PM87/Y02Uqw9m7PObBSibk+vsZ1GvdLOAuyKOvZRkAg7rKgd0kax2BB5toDD0/XsJ+GALwnKSTzxObeLYR4X9Dyd5s9Xo9qe1ICwHRgB/aPCY9wATCyisxzJ+IEY0+UO4vM5+LypA2ZycsY9VgP2BNxo89pkSVv6y2iP8IniLf8uA3VoQ1pVNHrcnVA9+18K579+CsH7f5DFvBkYXRVjAhlnX2UIZW1Bnvw8UoGxmCeugiOi54bJVtL/Ct2GZ2WahEXVYTm1255vZRu7+bBPb79zkcQeTTPk1oYVzP9HMLgizCTfKnk0ec0vghAI1PGf1i7qlhX0fAIyvsW55F5TNU+u87cziBDM7rxu6b3RDo/vJwMg665cDfyHpPe3A1uGvFqOB/wA+PcDXMaHF7d8aGsovHuDzPtTMTinIiAYbZ6y/t4W23Ou6vGwObSFf1ybpl3a5Gt1bqwrOyAhl7wampWy3b0Z4vBgY2kSVcOXf74D3hyrK2sA7SCbqjA3BHwOOALYIIpoBfDG0ZdTb7hdNVglX/v0p5M0UYB1gJ+CciO0uKEiVMOtcZ5W4bE6OvMeXhzfXG5PMwTiLZPbr1zK2+4nasFovFF/KyOTN62x7dsa2725SWJ+uc8zjI7a/A5hQY/utMxp/b2xBWF+tc96HZmz7OjCmAMLKemGxWYnLZoywjqxzzHeFbjO1tv1rNwir6B1H16+z7ll3v73O+p9n7HuTJs7nHHev1+v5B6FQ1Ksi7OfuL9aIdm8KVYg8zxngUnc/vk6UfWpoi6nF8PDqv9OMzFi/uMJl8wx3/1Gde3wNcG2d7Tfqhm5YhRaWux/u7lbjb82MzR/IWL96E6d0Y8b5vkDScbEWV7n7vS0cY3ToFNgoV0akOS5j/XYFKBJZQ870VLhs/iUizfV11o2TsDoru4UkfVJqMapNh36hzrqlLW5voWG2Hfl1T0aj9cwC3FZT2Wxb2RxmZsOLnndV/pawXYW/1V/55Rnrh7QxT+q9KZtWhGddZbOtZavwvQa69VvCTUm+wZsZHqSJJEOFjCm4hIseIdyfUR0d6u5vdvD8so49TGWz3HSNsMzMgAOBo0jGMxL582LG+jEZ1Yp283rG+lVVNiWsIshqXeB8YFvdsraypODCej5j/UiVzXJT+BDVzDYmaVspQ4EoepVwaMHP/6mM9eupbCrC6qSsVgHOI+lNXotlwDUk3QGeJunRu5LT0KiqjTAiY/0rHT6/+RnrN2yhrI2m9rd4K9z9FZVNCSuLT5F8vlKL/wUOcvdHaxTCUwp2jUWPsLLmAlzU4fO7PWP9Fi3KsNb130//zpxlK5sSVg4cWGfdIyTD4C7RbcyNer23F3f4DSEknzUtpPaoCu81s0GNjjpgZmNIvulsJLJT2ewAhW3DMrNR1O9dfYoKRO5sX2ddx0drCCK6ok6St5B0KWiUXTKi3/kqmxJWTPXEWqgeFNLDBf6B2AzYoE6SWwtyqudmrD8mdDNohCMy1l9WgbIpYbVI1rdN4zMewElqI/gHMVHHlzLWzy3ItVwGPFRn/Syyv4vsXU4+QzLEcS2epP9HwyqbElY/sjox7pNRnZxD/REYOxHtdCrCmm1mX6+TX/sAH6+z/SsM/MCBtaqFy4HvZCT7mpmdFtqmapYRM/sucFLGvk5PaRMrY9mUsFrkceoPF7KvmR1nZsN6FYbBZrYHyVjk78zY/4iK3evjzOxyM9vDzNYzs7FmtrmZ/Ri4IGPbC9399QJdy9nADRlpPgk8ZWbnm9lXzOwgM/u4mf2bmV1A0qfrqxnPwKMkQ0SrbBaEwoal7v6mmV0KfLhOsq8DnzOze8Ov0kZZ4Xgvxlbwfs8Of42wtJEq1gCVDTezA0nGcK93H0cCHwp/jbIC+EyaqFU2FWHV4rtkf2E+JrRbbNNAgYBkBMeqVAlb4YQiTrjp7g+RDDfcrq4Wn3H3P1aobEpYORTKO1v8da/XH2eamQ1WEajLlWS3F3WyfFwJfIB8RxpdBnw+Y2RZlU0Jq2bBOBb4SRObnhd+Bev9+g30KJqdirB+TjLxRSNcD+zt7ssKXj4uD1HMzTnsbj6wnbufVMGyKWHlWCi/QDI9/JMRyV8FjiaZZ+6XGb9kB1bkPj9A0ik05k1fD8lUU+9091e7pHzcS/IB8iE0N9XX7WHbzd39FpXN4mJhRo3uOFmzoSQNqLuFdoHVScb5fi48lJcAc9z9mUrdxORVeb1q0ZHu/uOQdgeSxuKdgLVIxpB6muSN2MXAr939qS7Pj61J+p5tR9LYvUa4zqUkU6m9FMR2HclsMXeobEpYooDCEkJVQiGEkLCEEELCEkJIWEIIIWEJIYSEJYSQsIQQQsISQkhYQghReNTTXQihCEsIISQsIYSEJYQQEpYQQkhYQggJSwghJCwhhJCwhBASlhBCSFhCCCFhCSEkLCGEkLCEEELCEkJIWEIIIWEJIYSEJYSQsIQQQsISQggJSwghYQkhhIQlhBASlhBCwhJCCAlLCCEkLCGEhCWEEBKWEELCUhYIISQsIYSQs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