---
title: "`r i` Quality Control Report"
author: "Rodrigo Arcoverde" 
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document: default
  pdf_document: default
  toc: yes
---

```{r logo_indiv, echo=FALSE}
htmltools::img(
  src = knitr::image_uri(file.path(R.home("doc"), "html", "logo.jpg")),
  alt = "logo",
  style = "position:absolute; top:0; right:0; padding:10px;"
)
```
---


```{r setup_indiv, include=FALSE, warning=FALSE, echo=FALSE, message=FALSE}

sf_summary <- sf$summaries %>% filter(ID == i)
sf_summary_filtered <- sf_filtered %>% filter(ID == i)
merged_summary_csv <- sf_filtered_csv %>% filter(ID == i)
```

## Overall quality of sequences post-filtering

Here is attached four plots to compare the filtered sequences by the proposed quality requirements, the graphs on the left are before the filtering and the graphs on the right are after filtering. The code was written to filter the best sequences. If they were repeated, it will select the best quality sequence after comparing both sequences quality scores.
The y-axis contain quality score similar to Phread Quality Score, which is logarithmically related to the base-calling error probabilities. Thus, a score of 10 represents a basecalling error probability of 1 in 10 (90% accuracy), a quality score of 20, 1 in 100 (99% accuracy) etc. 
The full script can be found on the Rmd file. 



```{r plot_indiv, echo=FALSE, warning=FALSE, fig.align='center'}
# I organized the sequences on different folders named with the identification I wanted. In this way, the folder.name gives me the ID that I can use to plot what I need. Here I wanted to compare the repeated sequences before and after filtering. You can change the axis depending on what you want to show here.

g1 <- sf_summary %>%
  ggplot(aes(x = folder.name, y = raw.mean.quality)) +
  geom_boxplot() +
  ylim(0, 60) +
  ggtitle("Before filtering") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
  labs(y = "Mean Quality Score", x = NULL) +
  theme_bw()

g2 <- sf_summary_filtered %>%
  ggplot(aes(x = folder.name, y = raw.mean.quality)) +
  geom_boxplot() +
  ylim(0, 60) +
  ggtitle("After filtering") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
  labs(y = NULL, x = NULL) +
  theme_bw()

gridExtra::grid.arrange(g1, g2, nrow = 1)
```

```{r plot_indiv_2, echo = FALSE, fig.align='center'}
# graph raw vs trimmed mean quality scores before and after filtering
g3 <- sf_summary %>%
  ggplot(aes(
    trimmed.mean.quality,
    raw.mean.quality
  )) +
  geom_point() +
  ylim(0, 60) +
  xlim(0, 60) +
  ggtitle("Not filtered") +
  theme_bw()
g4 <- sf_summary_filtered %>%
  ggplot(aes(
    trimmed.mean.quality,
    raw.mean.quality
  ), label = file.name) +
  geom_point() +
  ylim(0, 60) +
  xlim(0, 60) +
  ggtitle("Filtered") +
  theme_bw()
gridExtra::grid.arrange(g3, g4, nrow = 1)
```



## Overall quality score 

Here you can see a table containing the number of repeated sequences, total sequences, filtered sequences, unique sequences (total-repeated), and the percentage of the selected sequences from unique sequences.It also contain the quality score per plate and its standard deviation based on the Quality Score explained above. 


```{r quality_indiv, echo=FALSE, fig.align='center'}
# This code is to create a general table containing the quality report per plate

# Calculate the mean quality per plate number
plate_scores <- sf_summary_filtered %>%
  group_by(plate) %>%
  summarise(
    mean.quality = mean(raw.mean.quality),
    standard.deviation = sd(raw.mean.quality)
  ) %>%
  ungroup()

# Add the row containing the total, including the mean quality of all the sequences and its standard deviation
plate_scores <- plate_scores %>%
  add_row(
    plate = "Total",
    mean.quality = mean(sf_filtered$raw.mean.quality),
    standard.deviation = sd(sf_filtered$raw.mean.quality)
  )
options(digits = 4)


# Calculate the number of repeated sequences per plate
n.repeated.seq <- sf_summary %>%
  group_by(plate) %>%
  filter(str_detect(folder.name, "_R")) %>%
  summarise(n_repeated = n()) %>%
  data.frame()

# calculate the total number of sequences per plate
total.seq <- sf_summary %>%
  group_by(plate) %>%
  summarise(n_total = n()) %>%
  data.frame()

# calculate the number of sequences post filtering per plate
filtered.seq <- sf_summary_filtered %>%
  group_by(plate) %>%
  summarise(n_filtered = n()) %>%
  data.frame()

# Merge all the different datasets created above
x <- merge(n.repeated.seq, total.seq, by.default = plate, all = TRUE)
x <- merge(x, filtered.seq, by.default = plate, all = TRUE)
x[is.na(x)] <- 0

# Caculated the percentage of sequences filtered out of the total unique sequences and add the value in a new column (not repeated)
y <- x %>%
  mutate(n_unique = n_total - n_repeated) %>%
  mutate(used_percentage = (n_filtered / (n_unique)) * 100)

# Add row with the total number
plates_used_sequences <- y %>%
  add_row(
    plate = "Total",
    n_repeated = sum(y$n_repeat),
    n_total = sum(y$n_total),
    n_unique = sum(y$n_unique),
    n_filtered = sum(y$n_filtered),
    used_percentage = mean(y$used_percentage)
  )


# By now you have to tables containing different information per plate, here it just merging them into one single table to plot it
table.seq <- merge(plates_used_sequences, plate_scores, by.default = "plate")
kable(table.seq) %>%
  kable_styling(bootstrap_options = "striped", full_width = FALSE)
```
Obs.: After merging, sequences without a index sorting (specificity) were deleted. Those sequences were in general empty wells but also double negative. In total `r nrow(sf_summary_filtered)-nrow(merged_summary_csv)` sequences were subtracted from the total filtered sequences, thus the csv file contains `r nrow(merged_summary_csv)` of this animal.


## Histogram of secondary peaks (CDR3)

Below you can see a histogram based on the number of secondary peaks detected inside the CDR3 region.

```{r CDR3_indiv, echo=FALSE, fig.align='center'}
# plot number secondary peaks in CDR3
g5 <- sf_summary_filtered %>% ggplot(aes(sec.peak.CDR3 > 0)) +
  geom_bar() +
  xlab("Secondary peaks CDR3 > 0") +
  theme_bw()
g6 <- sf_summary_filtered %>% ggplot(aes(sec.peak.CDR3)) +
  geom_bar(aes(y = (..count..) / sum(..count..))) +
  scale_y_continuous(labels = scales::percent) +
  ylab("relative frequencies") +
  xlab("Number of secondary peaks CDR3") +
  theme_bw()

gridExtra::grid.arrange(g5, g6, nrow = 1)
```

## Fasta file 

A fasta file containing the filtered of this animal was created on the folder called __"fasta"__. 
```{r fasta_indiv, echo=FALSE}
# fasta file creation
df_to_fasta(
  sequence_name = merged_summary_csv$file_ID,
  sequence_strings = merged_summary_csv$sequence,
  file_name = paste0(i, ".fasta"),
  output_dir = output_dir
)
```













```{r session_indv}
sessionInfo()
```
