annotate fastqc_report.Rmd @ 7:5a0ade3de4d0 draft

planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_fastqc_report commit 9285c2b8ad41a486dde2a87600a6b8267841c8b5-dirty

---
title: "Fastqc report: short reads quality evaluation"
author: "Ming Chen"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=ECHO, warning=FALSE, message=FALSE)
library(plyr)
library(stringr)
library(dplyr)
library(highcharter)
library(DT)
library(reshape2)
library(plotly)
library(formattable)
library(htmltools)
```


```{bash 'create output directory', echo=FALSE}
# create extra files directory. very important!
mkdir REPORT_OUTPUT_DIR
```

# Fastqc analysis
```{bash 'copy data to working directory', echo=FALSE}
# Copy uploaded data to the working directory
for f in $(echo READS | sed "s/,/ /g")
do
    cp $f ./
done
```


```{bash 'run fastqc', echo=FALSE}
for r in $(ls *.dat)
do
    fastqc -o REPORT_OUTPUT_DIR $r > /dev/null 2>&1
done
```

## Fastqc html reports

Below are links to ***Fastqc*** original html reports.
```{r 'html report links'}
html_report_list = list()
html_files = list.files('REPORT_OUTPUT_DIR', pattern = '.*html')
for (i in html_files) {
  html_report_list[[i]] = tags$li(tags$a(href=i, i))
}
tags$ul(html_report_list)
```


## Parsing fastqc data

```{bash echo=FALSE}
##==== copy fastqc generated zip files from report output directory to job work directory ==
cp -r REPORT_OUTPUT_DIR/*zip ./

# create a file to store data file paths
echo "sample_id,file_path" > PWF_file_paths.txt # Pass, Warning, Fail
echo "sample_id,file_path" > PBQS_file_paths.txt # Per Base Quality Score
echo "sample_id,file_path" > PSQS_file_paths.txt # Per Sequence Quality Score
echo "sample_id,file_path" > PSGC_file_paths.txt # Per Sequence GC Content
echo "sample_id,file_path" > PBSC_file_paths.txt # Per Base Sequence Content
echo "sample_id,file_path" > PBNC_file_paths.txt # Per Base N Content
echo "sample_id,file_path" > SDL_file_paths.txt # Sequence Duplication Level
echo "sample_id,file_path" > SLD_file_paths.txt # Sequence Length Distribution
echo "sample_id,file_path" > KMC_file_paths.txt # Kmer Content

for i in $(ls *.zip)
do
    BASE=$(echo $i | sed 's/\(.*\)\.zip/\1/g')
    echo $BASE
    unzip ${BASE}.zip > /dev/null 2>&1
    
    ##====== pass,warning,fail (WSF) =============
    awk '/^>>/ {print}' "$BASE"/fastqc_data.txt | grep -v 'END_MODULE' | sed 's/>>//' > "$BASE"-PWF.txt
    echo "${BASE},${BASE}-PWF.txt" >> PWF_file_paths.txt

    ##====== per base quality scores (PBQS) ======
    awk '/^>>Per base sequence quality/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-PBQS.txt
    echo "${BASE},${BASE}-PBQS.txt" >> PBQS_file_paths.txt

    ##====== per sequence quality scores (PSQS)
    awk '/^>>Per sequence quality scores/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-PSQS.txt
    echo "${BASE},${BASE}-PSQS.txt" >> PSQS_file_paths.txt

    ##====== Per sequence GC content (PSGC)
    awk '/^>>Per sequence GC content/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-PSGC.txt
    echo "${BASE},${BASE}-PSGC.txt" >> PSGC_file_paths.txt
    
    ##====== Per Base Sequence Content (PBSC)
    awk '/^>>Per base sequence content/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-PBSC.txt
    echo "${BASE},${BASE}-PBSC.txt" >> PBSC_file_paths.txt
    
    ##====== Per Base N Content (PBNC)
    awk '/^>>Per base N content/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-PBNC.txt
    echo "${BASE},${BASE}-PBNC.txt" >> PBNC_file_paths.txt
    
    ##====== Sequence Duplication Level (SDL)
    awk '/^>>Sequence Duplication Levels/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-SDL.txt
    echo "${BASE},${BASE}-SDL.txt" >> SDL_file_paths.txt
    
    ##====== Sequence Length Distribution (SLD)
    awk '/^>>Sequence Length Distribution/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-SLD.txt
    echo "${BASE},${BASE}-SLD.txt" >> SLD_file_paths.txt
    
    ##====== Kmer Content ============
    awk '/^>>Kmer Content/ {flag=1; next} /END_MODULE/ {flag=0} flag' "$BASE"/fastqc_data.txt >"$BASE"-KMC.txt
    echo "${BASE},${BASE}-KMC.txt" >> KMC_file_paths.txt
    
done
```


## Evaluation Overview

```{r 'overview'}
PWF_file_paths = read.csv('PWF_file_paths.txt',
                           header = TRUE, stringsAsFactors = FALSE)
rm('PWF_df')
for(i in 1:nrow(PWF_file_paths)) {
  file_path = PWF_file_paths[i,2]
  pwf_df = read.csv(file_path,
                     sep='\t', header=FALSE, stringsAsFactors = FALSE)
  colnames(pwf_df) = c('item', PWF_file_paths[i,1])
  if (!exists('PWF_df')) {
    PWF_df = pwf_df
  } else {
    PWF_df = cbind(PWF_df, pwf_df[,2,drop=FALSE])
  }
}
```

```{r}
my_icon = c('ok', 'remove', 'star')
names(my_icon) = c('pass', 'fail', 'warn')
evaluate_list = list()
for (i in colnames(PWF_df)[-1]) {
  evaluate_list[[i]] = formatter(
      "span", 
      style = x ~ style("background-color" = ifelse(x =='pass', '#9CD027', ifelse(x == 'fail', '#CC0000', '#FF4E00')), 
                        "color" = "white",
                        "width" = "50px",
                        "float" = "left",
                        "padding-right" = "5px")
    )
}

formattable(PWF_df, evaluate_list)
```


## Per Base Quality Scores

```{r}
PBQS_df = data.frame()
PBQS_file_paths = read.csv('PBQS_file_paths.txt',
                           header = TRUE, stringsAsFactors = FALSE)
for(i in 1:nrow(PBQS_file_paths)) {
  # file_path = paste0('REPORT_OUTPUT_DIR/', PBQS_file_paths[i,2])
  file_path = PBQS_file_paths[i,2]
  pbqs_df = read.csv(file_path,
                     sep='\t', header=TRUE, stringsAsFactors = FALSE) %>%
    mutate(Base1=as.numeric(str_split_fixed(X.Base, '-', 2)[,1]),
           Base2=as.numeric(str_split_fixed(X.Base, '-', 2)[,2])) %>%
  (function (df) {
    df1 = select(df, -Base2)
    df2 = select(df, -Base1) %>% filter(Base2 != '')
    colnames(df1) = c(colnames(df1)[1:7], 'Base')
    colnames(df2) = c(colnames(df2)[1:7], 'Base')
    res = rbind(df1, df2) %>% arrange(Base)
    return(res)
  })
  pbqs_df$sample_id = rep(PBQS_file_paths[i,1], nrow(pbqs_df))
  PBQS_df = rbind(PBQS_df, pbqs_df)
}
```


```{r}
# datatable(PBQS_df)
max_phred = max(PBQS_df$Mean) + 10
hchart(PBQS_df, "line", hcaes(x = Base, y = Mean, group = sample_id)) %>%
  hc_title(
    text = "Per Base Quality Score"
  ) %>%
  hc_yAxis(
    title = list(text = "Mean Base Quality Score"),
    min = 0,
    max = max_phred,
    plotLines = list(
      list(label = list(text = "Phred Score = 27"),
           width = 2,
           dashStyle = "dash",
           color = "green",
           value = 27),
      list(label = list(text = "Phred Score = 20"),
           width = 2,
           color = "red",
           value = 20)
    )
  ) %>% 
  hc_exporting(enabled = TRUE)
```


## Per Base N Content

```{r}
PBNC_df = data.frame()
PBNC_file_paths = read.csv('PBNC_file_paths.txt',
                           header = TRUE, stringsAsFactors = FALSE)
for(i in 1:nrow(PBNC_file_paths)) {
  # file_path = paste0('REPORT_OUTPUT_DIR/', PBNC_file_paths[i,2])
  file_path = PBNC_file_paths[i,2]
  pbnc_df = read.csv(file_path,
                     sep='\t', header=TRUE, stringsAsFactors = FALSE) %>%
    mutate(Base1=as.numeric(str_split_fixed(X.Base, '-', 2)[,1]),
           Base2=as.numeric(str_split_fixed(X.Base, '-', 2)[,2])) %>%
  (function (df) {
    df1 = select(df, -Base2)
    df2 = select(df, -Base1) %>% filter(Base2 != '')
    colnames(df1) = c(colnames(df1)[1:2], 'Base')
    colnames(df2) = c(colnames(df2)[1:2], 'Base')
    res = rbind(df1, df2) %>% arrange(Base)
    return(res)
  })
  pbnc_df$sample_id = rep(PBNC_file_paths[i,1], nrow(pbnc_df))
  PBNC_df = rbind(PBNC_df, pbnc_df)
}
```


```{r}
PBNC_df$N.Count = PBNC_df$N.Count * 100
max_phred = max(PBNC_df$N.Count) + 5
hchart(PBNC_df, "line", hcaes(x = as.character(Base), y = N.Count, group = sample_id)) %>%
  hc_title(
    text = "Per Base N Content"
  ) %>%
  hc_xAxis(
    title = list(text = "Base Position")
  ) %>%
  hc_yAxis(
    title = list(text = "N %"),
    plotLines = list(
      list(label = list(text = "N = 5%"),
           width = 2,
           dashStyle = "dash",
           color = "red",
           value = 5)
    )
  ) %>% 
  hc_exporting(enabled = TRUE)
```




## Per Sequence Quality Scores

```{r}
PSQS_df = data.frame()
PSQS_file_paths = read.csv('PSQS_file_paths.txt', 
                           header = TRUE, stringsAsFactors = FALSE)
for(i in 1:nrow(PSQS_file_paths)) {
  # file_path = paste0('REPORT_OUTPUT_DIR/', PSQS_file_paths[i,2])
  file_path = PSQS_file_paths[i,2]
  psqs_df = read.csv(file_path,
                     sep='\t', header=TRUE, stringsAsFactors = FALSE) 
  psqs_df$sample_id = rep(PSQS_file_paths[i,1], nrow(psqs_df))
  PSQS_df = rbind(PSQS_df, psqs_df)
}
```


```{r}
max_phred = max(PSQS_df$X.Quality) + 5
hchart(PSQS_df, "line", hcaes(x = X.Quality, y = Count, group = sample_id)) %>%
  hc_title(
    text = "Per Sequence Quality Score"
  ) %>%
  hc_xAxis(
    title = list(text = "Mean Sequence Quality Score"),
    min = 0,
    max = max_phred,
    plotLines = list(
      list(label = list(text = "Phred Score = 27"),
           width = 2,
           dashStyle = "dash",
           color = "green",
           value = 27),
      list(label = list(text = "Phred Score = 20"),
           width = 2,
           color = "red",
           value = 20)
    )
  ) %>% 
  hc_exporting(enabled = TRUE)
```


## Per Sequence GC Content


```{r}
PSGC_df = data.frame()
PSGC_file_paths = read.csv('PSGC_file_paths.txt', 
                           header = TRUE, stringsAsFactors = FALSE)
for(i in 1:nrow(PSGC_file_paths)) {
  # file_path = paste0('REPORT_OUTPUT_DIR/', PSGC_file_paths[i,2])
  file_path = PSGC_file_paths[i,2]
  psgc_df = read.csv(file_path,
                     sep='\t', header=TRUE, stringsAsFactors = FALSE) 
  psgc_df$sample_id = rep(PSGC_file_paths[i,1], nrow(psgc_df))
  PSGC_df = rbind(PSGC_df, psgc_df)
}
```


```{r}
max_phred = max(PSGC_df$Count) + 5
hchart(PSGC_df, "line", hcaes(x = X.GC.Content, y = Count, group = sample_id)) %>%
  hc_title(
    text = "Per Sequence GC Content"
  ) %>%
  hc_xAxis(
    title = list(text = "% GC")
  ) %>%
  hc_exporting(enabled = TRUE)
```


## Per Base Sequence Content

```{r}
PBSC_df = data.frame()
PBSC_file_paths = read.csv('PBSC_file_paths.txt',
                           header = TRUE, stringsAsFactors = FALSE)
for(i in 1:nrow(PBSC_file_paths)) {
  # file_path = paste0('REPORT_OUTPUT_DIR/', PBSC_file_paths[i,2])
  file_path = PBSC_file_paths[i,2]
  pbsc_df = read.csv(file_path,
                     sep='\t', header=TRUE, stringsAsFactors = FALSE) %>%
    mutate(Base1=as.numeric(str_split_fixed(X.Base, '-', 2)[,1]),
           Base2=as.numeric(str_split_fixed(X.Base, '-', 2)[,2])) %>%
  (function (df) {
    df1 = select(df, -Base2)
    df2 = select(df, -Base1) %>% filter(Base2 != '')
    colnames(df1) = c(colnames(df1)[1:5], 'Base')
    colnames(df2) = c(colnames(df2)[1:5], 'Base')
    res = rbind(df1, df2) %>% arrange(Base)
    return(res)
  })
  pbsc_df$sample_id = rep(PBSC_file_paths[i,1], nrow(pbsc_df))
  PBSC_df = rbind(PBSC_df, pbsc_df)
}
```


```{r out.width="100%"}
PBSC_df_2 = select(PBSC_df, -X.Base) %>%
  melt(id = c('Base', 'sample_id'), value.name = 'base_percentage')
p = ggplot(data = PBSC_df_2, aes(x = Base, y = base_percentage, group = variable, color = variable)) +
  geom_line() +
  facet_wrap(~ sample_id)
ggplotly(p)
```


## References

* Andrews, Simon. "FastQC: a quality control tool for high throughput sequence data." (2010): 175-176.
* Goecks, Jeremy, Anton Nekrutenko, and James Taylor. "Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences." Genome biology 11.8 (2010): R86.
* Afgan, Enis, et al. "The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update." Nucleic acids research (2016): gkw343.
* Highcharts. https://www.highcharts.com/. (access by May 26, 2017).
* R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
* Joshua Kunst (2017). highcharter: A Wrapper for the 'Highcharts' Library. R package version 0.5.0. https://CRAN.R-project.org/package=highcharter
* Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec and Pedro Despouy (2017). plotly: Create Interactive Web Graphics via 'plotly.js'. R package version 4.6.0. https://CRAN.R-project.org/package=plotly