---
title: 'Report breakdown by barcode'
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')
  unassigned_folder: !r system.file('extdata', 'results',  'alignments', 'unassigned_reads.csv', package = 'amplican')
  top: 5
vignette: >
  %\VignetteIndexEntry{example barcode_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 <- amplican::plot_height(length(unique(config$Barcode)))
```

***

# 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  
**Top unassigned reads** - take a look at the alignment of most abundant 
forward and reverse complemented reverse reads for each barcode, 
if you find that there is many unassigned reads you can ivestigate here.  

***

# Barcode Summary  

***

## Groups IDs

```{r group ids, echo=FALSE, message=F, warning=FALSE}
library(knitr)
groupDF <- data.frame(group = unique(config$Barcode),
                      IDs = sapply(unique(config$Barcode),
                                   function(x) toString(config$ID[config$Barcode == x])))
kable(groupDF, row.names = FALSE)
```

## Read distribution  

```{r plot_total_reads, echo=FALSE, fig.height=height + height/4, fig.width=14, message=F, warning=FALSE}
p <- ggplot(data = config, 
            aes(x = as.factor(Barcode), y = log10(Reads + 1), 
                order = Barcode, fill = ID)) +
  geom_bar(position='stack', stat='identity') +
  ylab('Number of reads + 1, log10 scaled')  +
  xlab('Barcode')
if (length(groupDF$group) > 20) {
  p <- p + theme(legend.position = 'none',
                 legend.direction = 'horizontal',
                 axis.text = element_text(size = 12),
                 axis.title = element_text(size = 14, face = 'bold'))
} else {
  p <- p + theme(legend.position = 'top',
                 legend.direction = 'horizontal',
                 axis.text = element_text(size = 12),
                 axis.title = element_text(size = 14, face = 'bold'),
                 legend.title = element_blank())
}
p + coord_flip()
```

## Filtered Reads  

```{r plot_filter_per, echo=FALSE, fig.height=height, fig.width=14, message=F, warning=FALSE}
barcodeTable <- aggregate(cbind(Reads, Reads_Filtered, PRIMER_DIMER, 
                                Low_Score, Reads_Edited, Reads_Frameshifted) ~ Barcode, 
                          data = config, sum)
barcodeTable$F_percentage <- 
  (barcodeTable$PRIMER_DIMER + barcodeTable$Low_Score) * 100/barcodeTable$Reads
barcodeTable$F_percentage[is.nan(barcodeTable$F_percentage)] <- 0  

ggplot(data = barcodeTable, aes(x = as.factor(Barcode), y = F_percentage, 
                                order = Barcode, fill = Barcode)) +
  geom_bar(stat='identity') +
  ylab('Percentage of filtered reads')  +
  xlab('Barcode') +
  theme(axis.text = element_text(size=12),
        axis.title = element_text(size=14, face = 'bold'),
        legend.position = 'none') +
  ylim(0, 100) +
  coord_flip()
``` 

## Edit rates  

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

ggplot(data = barcodeTable, aes(x = as.factor(Barcode), y = edit_percentage, order = Barcode)) +
  geom_bar(stat = 'identity') +
  ylab('Percentage of reads (not filtered) that have edits')  +
  theme(axis.text = element_text(size=12),
        axis.title = element_text(size=14, face = 'bold')) +
  ylim(0,100) +
  xlab('Barcode') +
  coord_flip() +
  geom_text(aes(x = as.factor(Barcode), 
                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}
barcodeTable$frameshift_percentage <- 
  barcodeTable$Reads_Frameshifted * 100/barcodeTable$Reads_Filtered
barcodeTable$frameshift_percentage[is.nan(barcodeTable$frameshift_percentage)] <- 0  

ggplot(data = barcodeTable, aes(x = as.factor(Barcode), 
                                y = frameshift_percentage, order = Barcode)) +
  geom_bar(position = 'stack', stat = 'identity') +
  ylab('Percentage of reads (not filtered) that have frameshift')  +
  xlab('Barcode') +
  theme(axis.text = element_text(size=12),
        axis.title = element_text(size=14,face = 'bold')) +
  ylim(0, 100) +
  coord_flip() +
  geom_text(aes(x = as.factor(Barcode), 
                y = frameshift_percentage, label = Reads_Frameshifted), hjust = -1)
``` 

## Heterogeneity of reads  

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

***  

```{r plot read unassigned reads, results = "asis", echo=FALSE, message=F, warning=FALSE, comment = ''} 
if (!is.na(config$Forward_Reads_File) && !is.na(config$Reverse_Reads_File) &&
    file.exists(params$unassigned_folder)) {
  unassigned_reads <- data.table::fread(params$unassigned_folder)
  data.table::setDF(unassigned_reads)
  
  cat("# Top unassigned reads  \n\n***  \n\n")
  for (b in unique(unassigned_reads$Barcode)) {
    cat("## ", b, "  \n\n", sep = "")
    b_unassiged <- unassigned_reads[unassigned_reads$Barcode == b, ]
    b_unassiged <- b_unassiged[order(b_unassiged$BarcodeFrequency, decreasing = TRUE), ]
    if (dim(b_unassiged)[1] == 0) {
      cat(knitr::asis_output("No unassigned reads in this barcode. That\'s great!  \n\n"))
    } else {
      topN <- if (dim(b_unassiged)[1] < params$top) dim(b_unassiged)[1] else params$top
      cat('\n')
      print(knitr::kable(data.frame(Forward = paste0('P', 1:topN),
                                    Reverse = paste0('S', 1:topN),
                                    Counts = b_unassiged[1:topN, 'Total'],
                                    Frequency = b_unassiged[1:topN, 'BarcodeFrequency'])))
      cat("\n")
      cat("<PRE>", paste(amplican_print_reads(b_unassiged[1:topN, 'Forward'],
                                              b_unassiged[1:topN, 'Reverse']),
                         collapse = "\n"), "</PRE>")
      cat("\n\n")
    }
  }
}
```

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