diff fastqc_report.Rmd @ 17:ac5c618e4d97 draft

compare evaluation before and after trimming

--- a/fastqc_report.Rmd	Sat Oct 21 09:25:49 2017 -0400
+++ b/fastqc_report.Rmd	Mon Nov 06 16:53:14 2017 -0500
@@ -109,25 +109,27 @@
 
 ```{r}
 reads_1_summary = read.csv('REPORT_DIR/reads_1_summary.txt', header = FALSE, sep = '\t')[, 2:1]
-reads_2_summary = read.csv('REPORT_DIR/reads_1_summary.txt', header = FALSE, sep = '\t')[, 1]
+reads_2_summary = read.csv('REPORT_DIR/reads_2_summary.txt', header = FALSE, sep = '\t')[, 1]
 combined_summary = cbind(reads_1_summary, reads_2_summary)
 names(combined_summary) = c('MODULE', paste0(opt$name_1, '(before)'), paste0(opt$name_2, '(after)'))
+combined_summary[combined_summary == 'FAIL'] = 'FAIL (X)'
+combined_summary[combined_summary == 'WARN'] = 'WARN (!)'
 knitr::kable(combined_summary)
 ```
 
-## Visualization by module {.tabset}
+## Visualization by data module {.tabset}
 
 * Define a function to extract outputs for each module from fastqc output
 
 ```{r 'function definition'}
-extract_data_module = function(fastqc_data, module_name) {
+extract_data_module = function(fastqc_data, module_name, header = TRUE, comment.char = "") {
   f = readLines(fastqc_data)
   start_line = grep(module_name, f)
   end_module_lines = grep('END_MODULE', f)
   end_line = end_module_lines[which(end_module_lines > start_line)[1]]
   module_data = f[(start_line+1):(end_line-1)]
   writeLines(module_data, 'temp.txt')
-  read.csv('temp.txt', sep = '\t')
+  read.csv('temp.txt', sep = '\t', header = header, comment.char = comment.char)
 }
 ```
 
@@ -138,7 +140,7 @@
 pbsq_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base sequence quality')
 pbsq_1$id = 1:length(pbsq_1$X.Base)
 
-melt_pbsq_1 = filter(melt(pbsq_1, id=c('X.Base', 'id')), variable != 'X90th.Percentile' & variable != 'X10th.Percentile')
+melt_pbsq_1 = filter(melt(pbsq_1, id=c('X.Base', 'id')), variable == 'Mean')
 melt_pbsq_1$trim = 'before'
 
 
@@ -146,15 +148,17 @@
 pbsq_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base sequence quality')
 pbsq_2$id = 1:length(pbsq_2$X.Base)
 
-melt_pbsq_2 = filter(melt(pbsq_2, id=c('X.Base', 'id')), variable != 'X90th.Percentile' & variable != 'X10th.Percentile')
+melt_pbsq_2 = filter(melt(pbsq_2, id=c('X.Base', 'id')), variable == 'Mean')
 melt_pbsq_2$trim = 'after'
 
 comb_pbsq = rbind(melt_pbsq_1, melt_pbsq_2)
 comb_pbsq$trim = factor(levels = c('before', 'after'), comb_pbsq$trim)
+
 p = ggplot(data = comb_pbsq) +
   geom_line(mapping = aes(x = id, y = value, group = variable, color = variable)) +
   scale_x_continuous(breaks = pbsq_2$id, labels = pbsq_2$X.Base) + 
   facet_grid(. ~ trim) + 
+  ylim(0, max(comb_pbsq$value) + 5) +
   theme(axis.text.x = element_text(angle=45))
 ggplotly(p)
 
@@ -162,27 +166,249 @@
 
 ### Per tile sequence quality
 
-```{r 'per tile sequence quality'}
+```{r 'per tile sequence quality', fig.width=10}
+## check if 'per tile sequence quality' module exits or not
+check_ptsq = grep('Per tile sequence quality', readLines('REPORT_DIR/reads_1_fastqc_data.txt'))
+if (length(check_ptsq) > 0) {
+    ## reads 1
+  ptsq_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per tile sequence quality')
+  ptsq_1$trim = 'before'
+  
+  ## reads 2
+  ptsq_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per tile sequence quality')
+  ptsq_2$trim = 'after'
+  
+  comb_ptsq = rbind(ptsq_1, ptsq_2)
+  comb_ptsq$trim = factor(levels = c('before', 'after'), comb_ptsq$trim)
+  comb_ptsq$Base = factor(levels = unique(comb_ptsq$Base), comb_ptsq$Base)
+  
+  # convert integers to charaters
+  comb_ptsq$Tile = as.character(comb_ptsq$X.Tile)
+  
+  p = ggplot(data = comb_ptsq, aes(x = Base, y = Tile, fill = Mean)) +
+    geom_raster() + 
+    facet_grid(. ~ trim) + 
+    xlab('Position in read (bp)') + 
+    ylab('') +
+    theme(axis.text.x = element_text(angle=45))
+  ggplotly(p)
+} else {
+  print('No "per tile sequence quality" data')
+}
+
+
+```
+
+### Per sequence quality score
+
+```{r 'Per sequence quality score', fig.width=10}
 ## reads 1
-ptsq_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per tile sequence quality')
-ptsq_1$trim = 'before'
+psqs_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per sequence quality scores')
+psqs_1$trim = 'before'
 
 ## reads 2
-ptsq_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per tile sequence quality')
-ptsq_2$trim = 'after'
+psqs_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per sequence quality scores')
+psqs_2$trim = 'after'
+
+comb_psqs = rbind(psqs_1, psqs_2)
+comb_psqs$trim = factor(levels = c('before', 'after'), comb_psqs$trim)
+
+p = ggplot(data = comb_psqs, aes(x = X.Quality, y = Count)) + 
+  geom_line(color = 'red') + 
+  facet_grid(. ~ trim) + 
+  xlim(min(comb_psqs$X.Quality), max(comb_psqs$X.Quality)) + 
+  xlab('Mean Sequence Qaulity (Phred Score)') + 
+  ylab('')
+ggplotly(p)
+```
+
+
+### Per base sequence content
+
+```{r 'Per base sequence content', fig.width=10}
+## reads 1
+pbsc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base sequence content')
+pbsc_1$id = 1:length(pbsc_1$X.Base)
+
+melt_pbsc_1 = melt(pbsc_1, id=c('X.Base', 'id'))
+melt_pbsc_1$trim = 'before'
+
+
+## reads 2
+pbsc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base sequence content')
+pbsc_2$id = 1:length(pbsc_2$X.Base)
+
+melt_pbsc_2 = melt(pbsc_2, id=c('X.Base', 'id'))
+melt_pbsc_2$trim = 'after'
+
+comb_pbsc = rbind(melt_pbsc_1, melt_pbsc_2)
+comb_pbsc$trim = factor(levels = c('before', 'after'), comb_pbsc$trim)
+
+p = ggplot(data = comb_pbsc, aes(x = id, y = value, color = variable)) +
+  geom_line() +
+  facet_grid(. ~ trim) +
+  xlim(min(comb_pbsc$id), max(comb_pbsc$id)) + 
+  ylim(0, 100) +
+  xlab('Position in read (bp)') +
+  ylab('')
+ggplotly(p)
+```
 
-comb_ptsq = rbind(ptsq_1, ptsq_2)
-comb_ptsq$trim = factor(levels = c('before', 'after'), comb_ptsq$trim)
-comb_pbsq$Base = factor(levels = unique(comb_ptsq$Base), comb_ptsq$Base)
+### Per sequence GC content
+
+```{r 'Per sequence GC content', fig.width=10}
+## reads 1
+psGCc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per sequence GC content')
+psGCc_1$trim = 'before'
+
+## reads 2
+psGCc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per sequence GC content')
+psGCc_2$trim = 'after'
+
+comb_psGCc = rbind(psGCc_1, psGCc_2)
+comb_psGCc$trim = factor(levels = c('before', 'after'), comb_psGCc$trim)
+
+p = ggplot(data = comb_psGCc, aes(x = X.GC.Content, y = Count)) +
+  geom_line(color = 'red') +
+  facet_grid(. ~ trim) +
+  xlab('Mean Sequence Qaulity (Phred Score)') +
+  ylab('')
+ggplotly(p)
+```
+
 
-p = ggplot(data = comb_ptsq, aes(x = Base, y = X.Tile, fill = Mean)) +
-  geom_raster() + 
-  facet_grid(. ~ trim) + 
+### Per base N content
+
+```{r 'Per base N content', fig.width=10}
+## reads 1
+pbNc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Per base N content')
+pbNc_1$id = 1:length(pbNc_1$X.Base)
+pbNc_1$trim = 'before'
+
+## reads 2
+pbNc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Per base N content')
+pbNc_2$id = 1:length(pbNc_2$X.Base)
+pbNc_2$trim = 'after'
+
+comb_pbNc = rbind(pbNc_1, pbNc_2)
+comb_pbNc$trim = factor(levels = c('before', 'after'), comb_pbNc$trim)
+
+p = ggplot(data = comb_pbNc, aes(x = id, y = N.Count)) +
+  geom_line(color = 'red') +
+  scale_x_continuous(breaks = pbNc_2$id, labels = pbNc_2$X.Base) + 
+  facet_grid(. ~ trim) +
+  ylim(0, 1) + 
+  xlab('N-Count') +
+  ylab('') + 
   theme(axis.text.x = element_text(angle=45))
 ggplotly(p)
 ```
 
 
+### Sequence Length Distribution
+
+```{r 'Sequence Length Distribution', fig.width=10}
+## reads 1
+sld_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Sequence Length Distribution')
+sld_1$id = 1:length(sld_1$X.Length)
+sld_1$trim = 'before'
+
+## reads 2
+sld_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Sequence Length Distribution')
+sld_2$id = 1:length(sld_2$X.Length)
+sld_2$trim = 'after'
+
+comb_sld = rbind(sld_1, sld_2)
+comb_sld$trim = factor(levels = c('before', 'after'), comb_sld$trim)
+
+p = ggplot(data = comb_sld, aes(x = id, y = Count)) +
+  geom_line(color = 'red') +
+  scale_x_continuous(breaks = sld_2$id, labels = sld_2$X.Length) + 
+  facet_grid(. ~ trim) +
+  xlab('Sequence Length (bp)') +
+  ylab('') + 
+  theme(axis.text.x = element_text(angle=45))
+ggplotly(p)
+```
+
+### Sequence Duplication Levels
+
+```{r 'Sequence Duplication Levels', fig.width=10}
+## reads 1
+sdl_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
+names(sdl_1) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
+sdl_1$id = 1:length(sdl_1$Duplication_Level)
+
+melt_sdl_1 = melt(sdl_1, id=c('Duplication_Level', 'id'))
+melt_sdl_1$trim = 'before'
+
+
+## reads 2
+sdl_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
+names(sdl_2) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
+sdl_2$id = 1:length(sdl_2$Duplication_Level)
+
+melt_sdl_2 = melt(sdl_2, id=c('Duplication_Level', 'id'))
+melt_sdl_2$trim = 'after'
+
+comb_sdl = rbind(melt_sdl_1, melt_sdl_2)
+comb_sdl$trim = factor(levels = c('before', 'after'), comb_sdl$trim)
+
+p = ggplot(data = comb_sdl, aes(x = id, y = value, color = variable)) +
+  geom_line() +
+  scale_x_continuous(breaks = sdl_2$id, labels = sdl_2$Duplication_Level) +
+  facet_grid(. ~ trim) +
+  xlab('Sequence Duplication Level') +
+  ylab('') + 
+  theme(axis.text.x = element_text(angle=45))
+ggplotly(p)
+```
+
+### Adapter Content
+
+```{r 'Adapter Content', fig.width=10}
+## reads 1
+ac_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Adapter Content')
+ac_1$id = 1:length(ac_1$X.Position)
+
+melt_ac_1 = melt(ac_1, id=c('X.Position', 'id'))
+melt_ac_1$trim = 'before'
+
+## reads 2
+ac_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Adapter Content')
+ac_2$id = 1:length(ac_2$X.Position)
+
+melt_ac_2 = melt(ac_2, id=c('X.Position', 'id'))
+melt_ac_2$trim = 'after'
+
+comb_ac = rbind(melt_ac_1, melt_ac_2)
+comb_ac$trim = factor(levels = c('before', 'after'), comb_ac$trim)
+
+p = ggplot(data = comb_ac, aes(x = id, y = value, color = variable)) +
+  geom_line() +
+  facet_grid(. ~ trim) +
+  xlim(min(comb_ac$id), max(comb_ac$id)) + 
+  ylim(0, 1) +
+  xlab('Position in read (bp)') +
+  ylab('')
+ggplotly(p)
+```
+
+### Kmer Content {.tabset}
+
+#### Before
+
+```{r 'Kmer Content (before)', fig.width=10}
+kc_1 = extract_data_module('REPORT_DIR/reads_1_fastqc_data.txt', 'Kmer Content')
+knitr::kable(kc_1)
+```
+
+#### After
+```{r 'Kmer Content (after)', fig.width=10}
+kc_2 = extract_data_module('REPORT_DIR/reads_2_fastqc_data.txt', 'Kmer Content')
+knitr::kable(kc_2)
+```
+
 
 # Session Info