Report breakdown by group
In amplican: Automated analysis of CRISPR experiments

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$Group)))

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

Group Summary

Read distribution

ggplot(data = config, aes(x = as.factor(Group), y = log10(Reads + 1), order = Group, fill = Group)) +
  geom_boxplot() +
  ylab('Number of reads + 1, log10 scaled')  +
  xlab('Group') + 
  theme(legend.position = 'none',
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14, face = 'bold')) +
  coord_flip()

Filtered reads

config$F_percentage <- (config$PRIMER_DIMER + config$Low_Score) * 100/config$Reads
config$F_percentage[is.nan(config$F_percentage)] <- 0

ggplot(data = config, aes(x = as.factor(Group), y = F_percentage, 
                          order = Group, fill = Group)) +
  geom_boxplot() +
  xlab('Group') + 
  ylab('Percentage of filtered reads')  +
  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

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(Group), y = edit_percentage, order = Group, fill = Group)) +
  geom_boxplot() +
  xlab('Group') + 
  ylab('Percentage of reads (not filtered) that have edits')  +
  theme(axis.text = element_text(size=12),
        axis.title = element_text(size=14, face = 'bold'),
        legend.position = 'None') +
  ylim(0,100) +
  coord_flip()

Frameshift

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(Group), y = frameshift_percentage, order = Group, fill = Group)) +
  geom_boxplot() +
  xlab('Group') + 
  ylab('Percentage of reads (not filtered) that have frameshift')  +
  theme(axis.text = element_text(size=12),
        axis.title = element_text(size=14,face = 'bold'),
        legend.position = 'None') +
  ylim(0, 100) +
  coord_flip()

Heterogeneity of reads

plot_heterogeneity(alignments, config, level = 'Group')

Alignments plots

alignments <- alignments[alignments$consensus & alignments$overlaps, ]
alignments$strand <- "+" # strand does not matter after consensus filtering
src = sapply(unique(config$Group), function(i) {
  knitr::knit_expand(text = c(
    "## Group {{i}}  \n", 
    "### Deletions  \n", 
    paste('```r}, echo = F, results = "asis", ',
          'message=F, warning=F}', collapse = ''), 
    'amplican::metaplot_deletions(alignments, config, "Group", "{{i}}")', 
    '```\n',
    "### Insertions", 
    paste('```r}, echo = F, results = "asis", ',
          'message=F, warning=F}', collapse = ''), 
    'amplican::metaplot_insertions(alignments, config, "Group", "{{i}}")',
    '```\n', 
    "### Mismatches", 
    paste('```r}, echo = F, results = "asis", ',
          'message=F, warning=F}', collapse = ''), 
    'amplican::plot_mismatches(alignments, config, "Group", "{{i}}")',
    '```\n'))
})
# knit the source
res = knitr::knit_child(text = src, quiet = TRUE)
cat(res, sep = '\n')