An Introduction To ngsReports
18 August 2019
Package
ngsReports 1.0.1
Contents
1 Introduction
The package ngsReports is designed to bolt into data processing pipelines and
produce combined plots for multiple FastQC reports generated across an entire
set of libraries or samples.
The primary functionality of the package is parsing FastQC reports, with import
methods also implemented for log files produced by tools as as STAR, Hisat2
and others.
In addition to parsing files, default plotting methods are implemented.
Plots applied to a single file will replicate the default plots from
FastQC1 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/,
whilst methods applied to multiple FastQC reports summarise these and produce
a series of custom plots.
Plots are produced as standard ggplot2 objects, with an interactive option available using plotly. As well as custom summary plots, tables of read counts and the like can also be easily generated.
2 Basic Usage
2.1 Using the Shiny App
In addition to the usage demonstrated below, a shiny app has been developed
for interactive viewing of FastQC reports.
This can be installed using:
remotes::install_github("UofABioinformaticsHub/fastqcRshiny")A vignette for this app will be installed with the fastqcRshiny package.
2.2 The default report
In it’s simplest form, a default summary report can be generated simply by
specifying a directory containing the output from FastQC and calling the
function writeHtmlReport().
library(ngsReports)fileDir <- file.path("path", "to", "your", "FastQC", "Reports")
writeHtmlReport(fileDir)This function will transfer the default template to the provided directory and
produce a single .html file containing interactive summary plots of any
FastQC output found in the directory.
FastQC output can be *fastqc.zip files or the same files extracted as
individual directories.
The default template is provided as ngsReports_Fastqc.Rmd in the package
directory .
This template can be easily modified and supplied as an alternate template to
the above function using your modified file as the template RMarkdown file.
altTemplate <- file.path("path", "to", "your", "new", "template.Rmd")
writeHtmlReport(fileDir, template = altTemplate)3 Advanced Usage
3.1 Classes Defined in the Package
The package ngsReports introduces two main S4 classes:
FastqcData&FastqcDataList
FastqcData objects hold the parsed data from a single report as
generated by the stand-alone tool FastQC.
These are then extended into lists for more than one file as a
FastqcDataList.
For most users, the primary class of interest will be the FastqcDataList.
3.2 Loading FastQC Data Into R
To load a set of FastQC reports into R as a FastqcDataList, specify the
vector of file paths, then call the function FastqcDataList().
In the rare case you’d like an individual file, this can be performed by
calling FastqcData() on an individual file, or subsetting the output from
FastqcDataList() using the [[]] operator as with any list object.
fileDir <- system.file("extdata", package = "ngsReports")
files <- list.files(fileDir, pattern = "fastqc.zip$", full.names = TRUE)
fdl <- FastqcDataList(files)From here, all FastQC modules can be obtained as a tibble (i.e. data.frame)
using the function getModule() and choosing one of the following modules:
Summary(The PASS/WARN/FAIL status for each module)Basic_StatisticsPer_base_sequence_qualityPer_sequence_quality_scoresPer_base_sequence_contentPer_sequence_GC_contentPer_base_N_contentSequence_Length_DistributionSequence_Duplication_LevelsOverrepresented_sequencesAdapter_ContentKmer_ContentPer_tile_sequence_quality
getModule(fdl[[1]], "Summary")## # A tibble: 12 x 3
## Filename Status Category
## <chr> <chr> <chr>
## 1 ATTG_R1.fastq PASS Basic Statistics
## 2 ATTG_R1.fastq FAIL Per base sequence quality
## 3 ATTG_R1.fastq WARN Per tile sequence quality
## 4 ATTG_R1.fastq PASS Per sequence quality scores
## 5 ATTG_R1.fastq FAIL Per base sequence content
## 6 ATTG_R1.fastq FAIL Per sequence GC content
## 7 ATTG_R1.fastq PASS Per base N content
## 8 ATTG_R1.fastq PASS Sequence Length Distribution
## 9 ATTG_R1.fastq FAIL Sequence Duplication Levels
## 10 ATTG_R1.fastq FAIL Overrepresented sequences
## 11 ATTG_R1.fastq FAIL Adapter Content
## 12 ATTG_R1.fastq FAIL Kmer ContentCapitalisation and spelling of these module names follows the default patterns from FastQC reports with spaces replaced by underscores. One additional module is available and taken directly from the text within the supplied reports
Total_Duplicated_Percentage
In addition, the read totals for each file in the library can be obtained
using readTotals(), which can be easily used to make a table of read totals.
This essentially just returns the first two columns from
getModule(x, "Basic_Statistics").
reads <- readTotals(fdl)The packages dplyr and pander can also be extremely useful for manipulating
and displaying imported data.
To show only the R1 read totals, you could do the following
library(dplyr)
library(pander)
reads %>%
dplyr::filter(grepl("R1", Filename)) %>%
pander(
big.mark = ",",
caption = "Read totals from R1 libraries",
justify = "lr"
)| Filename | Total_Sequences |
|---|---|
| ATTG_R1.fastq | 24,978 |
| CCGC_R1.fastq | 22,269 |
| GACC_R1.fastq | 10,287 |
3.3 Generating Plots For One or More Fastqc Files
Plots created from a single FastqcData object will resemble those generated
by the FastQC tool, whilst those created from a FastqcDataList will be
combined summaries across a library of files.
In addition, all plots are able to be generated as interactive plots using the
argument usePlotly = TRUE.
All FastQC modules have been enabled for plotting using default S4 dispatch,
with the exception of Per_tile_sequence_quality.
3.3.1 Inspecting the PASS/WARN/FAIL Status of each module
The simplest of the plots is to summarise the PASS/WARN/FAIL flags as
produced by FastQC for each module.
This plot can be simply generated using plotSummary()
plotSummary(fdl)
Figure 1: Default summary of FastQC flags
3.3.2 Visualising Read Totals
The next most informative plot may be to summarise the total numbers of reads
in each associated Fastq file.
By default, the number of duplicated sequences from the
Total_Duplicated_Percentage module are shown, but this can be disabled by
setting duplicated = FALSE.
plotReadTotals(fdl)
As these are ggplot2 objects, the output can be modified easily using
conventional ggplot2 syntax.
Here we’ll move the legend to the top right as an example.
plotReadTotals(fdl) +
theme(
legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.background = element_rect(colour = "black")
)
3.3.3 Per Base Sequence Qualities
Turning to the Per base sequence quality scores is the next most common step
for most researchers, and these can be obtained for an individual file by
selecting this as an element (i.e. FastqcData object ) of the main
FastqcDataList object.
This plot replicates the default plots from a FastQC report.
plotBaseQuals(fdl[[1]])
Figure 2: Example showing the Per_base_sequence_quality plot for a single FastqcData object
When working with multiple FastQC reports, these are summarised as a heatmap using the mean quality score at each position.
plotBaseQuals(fdl)
Figure 3: Example showing the Mean Per Base Squence Qualities for a set of FastQC reports
Boxplots of any combinations can also be drawn from a FastqcDataList by
setting the argument plotType = "boxplot".
However, this may be not suitable for datasets with a large number of libraries.
plotBaseQuals(fdl[1:4], plotType = "boxplot")
3.3.4 Mean Sequence Quality Per Read
Similarly, the Mean Sequence Quality Per Read plot can be generated to
replicate plots from a FastQC report by selecting the individual file from the
FastqcDataList object.
plotSeqQuals(fdl[[1]])
Figure 4: Example plot showing Per_sequence_quality_scores for an individual file
A heatmap of mean sequence qualities can be generated when inspecting multiple reports.
plotSeqQuals(fdl)
Figure 5: Example heatmaps showing Per_sequence_quality_scores for a set of files
An alternative view may be to plot these as overlaid lines, which can be simply
done by setting plotType = "line".
Again, discretion should be shown when choosing this option for many samples.
r2 <- grepl("R2", names(fdl))
plotSeqQuals(fdl[r2], plotType = "line")
3.3.5 Per Base Sequence Content
The Per_base_sequence_content module can also be plotted for an individual
report with the layout being identical to that from FastQC.
plotSeqContent(fdl[[1]])
Figure 6: Individual Per_base_sequence_content plot
These are then combined across multiple files as a heatmap showing a composite
colour for each position.
Colours are combined using rgb() with A,C, G and T being represented
by green, blue, black and red respectively.
plotSeqContent(fdl)
Figure 7: Combined Per_base_sequence_content plot
NB These plots can be very informative setting the argument
usePlotly = TRUE, however they can be slow to render given the nature of how
the package plotly renders graphics.
Again, supplying multiple files and setting plotType = "line" will replicate
multiple individual plots from the original FastQC reports.
The argument nc is passed to facet_wrap() from the package ggplot2 to
determine the number of columns in the final plot.
plotSeqContent(fdl[1:2], plotType = "line", nc = 1)
3.3.6 Adapter Content
Adapter content as identified by FastQC is also able to be plotted for an individual file.
plotAdapterContent(fdl[[1]]) 
Figure 8: Adapter Content plot for a single FastQC report
When producing a heatmap across a set of FastQC reports, this will default to Total Adapter Content, instead of showing the individual adapter types.
plotAdapterContent(fdl)
Figure 9: Heatmap showing Total Adapter Content by position across a set of FastQC reports
3.3.7 Sequence Duplication Levels
As with all modules, the Sequence Duplication Levels plot is able to be replicated for an individual file.
plotDupLevels(fdl[[1]])
Figure 10: Example Sequence Duplication Levels plot for an individual file
When plotting across multiple FastQC reports, duplication levels are shown as a
heatmap based on each default bin included in the initial FastQC reports.
By default, the plotted values are the “Pre” de-duplication values.
Note that values are converted to percentages instead of read numbers to ensure
comparability across files.
In the plot below, CCGC_R2 shows very low duplication levels, whilst ATTG_R1
shows high levels of duplication.
The commonly observed ‘spikes’ around >10 are also evident as the larger red
blocks.
plotDupLevels(fdl)
Figure 11: Sequence Duplication Levels for multiple files
3.4 GC content
3.4.1 The class TheoreticalGC
A selection of Theoretical GC content is supplied with the package in the
object gcTheoretical, which has been defined with the additional S4 class
TheoreticalGC.
GC content was calculated using scripts obtained from
https://github.com/mikelove/fastqcTheoreticalGC.
Available genomes and transcriptomes can be obtained using the function
gcAvail() on the object gcTheoretical and specifying the type.
gcAvail(gcTheoretical, "Genome")## # A tibble: 24 x 4
## Name Group Source Version
## <chr> <chr> <chr> <chr>
## 1 Alyrata Plants JGI V1.0
## 2 Amellifera Animals UCSC apiMel2
## 3 Athaliana Plants TAIR TAIR9
## 4 Btaurus Animals UCSC bosTau8
## 5 Celegans Animals UCSC ce11
## 6 Cfamiliaris Animals UCSC canFam3
## 7 Dmelanogaster Animals UCSC dm6
## 8 Drerio Animals UCSC danRer10
## 9 Ecoli Bacteria NCBI 2008/08/05
## 10 Gaculeatus Other UCSC gasAcu1
## # … with 14 more rows3.4.2 Inspecting GC Content
As with all modules, data for an individual file replicates the default plot
from a FastQC report, but with one key difference.
This is that the Theoretical GC content has been provided in the object
gcTheoretical based on 100bp reads.
This empirically determined content is shown as the Theoretical GC content
line.
plotGcContent(fdl[[1]], species = "Hsapiens", gcType = "Transcriptome")
Figure 12: Example GC Content plot using the Hsapiens Transcriptome for the theoretical distribution
Again, data is summarised as a heatmap when plotting across multiple reports, with the default value being the difference between the observed and the theoretical GC content.
plotGcContent(fdl)
Figure 13: Example GC content showing the difference between observed and theoretical GC content across multiple files
Line plots can also be produce an alternative viewpoint, with read totals displayed as percentages instead of raw counts.
plotGcContent(fdl, plotType = "line", gcType = "Transcriptome")
Figure 14: Example GC content plot represented as a line plot instead of a heatmap
Customized theoretical GC content can be generated using input DNA sequences from a supplied fasta file.
faFile <- system.file(
"extdata", "Athaliana.TAIR10.tRNA.fasta",
package = "ngsReports")
plotGcContent(fdl, Fastafile = faFile, n = 1000)
3.5 Overrepresented Sequences
When inspecting the Overrepresented Sequence module, the top ncan be plotted
for an individual file, again broken down by their possible source, and
coloured based on their WARN/FAIL status.
plotOverrep(fdl[[1]])
When applying this across multiple files, instead of identifying common sequences across a set of libraries, overrepresented sequences are summarised by their possible source as defined by FastQC.
plotOverrep(fdl)
In addition to the above, the most abundant n overrepresented sequences can
be exported as a FASTA file for easy submission to blast.
overRep2Fasta(fdl, n = 10)4 Importing Log Files
A selection of log files as produced by tools such as STAR, hisat2,
bowtie, bowtie2 and picard duplicationMetrics, can be easily imported.
Files produced by hisat2 and bowtie2 have the identical format, and produce
identical output
4.1 Bowtie2
fl <- c("bowtie2PE.txt", "bowtie2SE.txt")
bowtie2Logs <- system.file("extdata", fl, package = "ngsReports")
df <- importNgsLogs(bowtie2Logs, type = "bowtie2")df %>%
dplyr::select("Filename", ends_with("Reads"), contains("Unique")) %>%
pander(
split.tables = Inf,
style = "rmarkdown",
big.mark = ",",
caption = "Select columns as an example of output from bowtie 2."
)| Filename | Total_Reads | Paired_Reads | Unique_Unpaired | Unique_In_Pairs | Unique_Discordant_Pairs |
|---|---|---|---|---|---|
| bowtie2PE.txt | 10,000 | 10,000 | 571 | 8,823 | 34 |
| bowtie2SE.txt | 20,000 | 0 | 18,739 | 0 | 0 |
4.2 Bowtie
Bowtie provides different output to both hisat2 and bowtie2
fls <- c("bowtiePE.txt", "bowtieSE.txt")
bowtieLogs <- system.file("extdata", fls, package = "ngsReports")
df <- importNgsLogs(bowtieLogs, type = "bowtie")df %>%
dplyr::select("Filename", starts_with("Reads")) %>%
pander(
split.tables = Inf,
style = "rmarkdown",
big.mark = ",",
caption = "Select columns as an example of output from bowtie."
)| Filename | Reads_Processed | Reads_With_At_Least_One_Reported_Alignment | Reads_That_Failed_To_Align | Reads_With_Alignments_Suppressed_Due_To_-m |
|---|---|---|---|---|
| bowtiePE.txt | 42,867,915 | 21,891,058 | 20,748,783 | 228,074 |
| bowtieSE.txt | 440,372 | 193,962 | 246,410 | NA |
4.3 STAR
Different information again is contained in STAR alignment logs.
starLog <- system.file("extdata", "log.final.out", package = "ngsReports")
df <- importNgsLogs(starLog, type = "star")| Filename | Uniquely_Mapped_Reads_Number | Uniquely_Mapped_Reads_Percent |
|---|---|---|
| log.final.out | 68,471,490 | 85.84 |
4.4 Duplication Metrics (Picard)
In addition to the files produced by the above alignment tools, the output
from Duplication Metrics (picard) can also be imported.
This is imported as a list with a tibble containing the detailed output in
the list element $metrics and the histogram data included as the second
elements.
sysDir <- system.file("extdata", package = "ngsReports")
fl <- list.files(sysDir, "Dedup_metrics.txt", full.names = TRUE)
dupMetrics <- importNgsLogs(fl, type = "duplicationMetrics", which = "metrics")
str(dupMetrics)## Classes 'tbl_df', 'tbl' and 'data.frame': 1 obs. of 10 variables:
## $ LIBRARY : chr "Sample1.bam"
## $ UNPAIRED_READS_EXAMINED : int 93
## $ READ_PAIRS_EXAMINED : int 43490914
## $ SECONDARY_OR_SUPPLEMENTARY_RDS: int 5366030
## $ UNMAPPED_READS : int 0
## $ UNPAIRED_READ_DUPLICATES : int 79
## $ READ_PAIR_DUPLICATES : int 11494386
## $ READ_PAIR_OPTICAL_DUPLICATES : int 642648
## $ PERCENT_DUPLICATION : num 0.264
## $ ESTIMATED_LIBRARY_SIZE : int 69609522Session info
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] pander_0.6.3 dplyr_0.8.3 ngsReports_1.0.1
## [4] tibble_2.1.3 ggplot2_3.2.1 BiocGenerics_0.30.0
## [7] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Biobase_2.44.0 ggdendro_0.1-20
## [3] httr_1.4.1 tidyr_0.8.3
## [5] jsonlite_1.6 viridisLite_0.3.0
## [7] assertthat_0.2.1 highr_0.8
## [9] BiocManager_1.30.4 stats4_3.6.1
## [11] latticeExtra_0.6-28 GenomeInfoDbData_1.2.1
## [13] Rsamtools_2.0.0 yaml_2.2.0
## [15] pillar_1.4.2 backports_1.1.4
## [17] lattice_0.20-38 glue_1.3.1
## [19] digest_0.6.20 RColorBrewer_1.1-2
## [21] GenomicRanges_1.36.0 XVector_0.24.0
## [23] rvest_0.3.4 colorspace_1.4-1
## [25] plyr_1.8.4 Matrix_1.2-17
## [27] htmltools_0.3.6 pkgconfig_2.0.2
## [29] ShortRead_1.42.0 bookdown_0.12
## [31] zlibbioc_1.30.0 purrr_0.3.2
## [33] scales_1.0.0 webshot_0.5.1
## [35] BiocParallel_1.18.1 IRanges_2.18.1
## [37] withr_2.1.2 SummarizedExperiment_1.14.1
## [39] lazyeval_0.2.2 cli_1.1.0
## [41] magrittr_1.5 crayon_1.3.4
## [43] evaluate_0.14 fansi_0.4.0
## [45] MASS_7.3-51.4 hwriter_1.3.2
## [47] xml2_1.2.2 truncnorm_1.0-8
## [49] tools_3.6.1 data.table_1.12.2
## [51] hms_0.5.0 matrixStats_0.54.0
## [53] stringr_1.4.0 plotly_4.9.0
## [55] S4Vectors_0.22.0 munsell_0.5.0
## [57] DelayedArray_0.10.0 Biostrings_2.52.0
## [59] kableExtra_1.1.0 compiler_3.6.1
## [61] GenomeInfoDb_1.20.0 rlang_0.4.0
## [63] grid_3.6.1 RCurl_1.95-4.12
## [65] rstudioapi_0.10 htmlwidgets_1.3
## [67] labeling_0.3 bitops_1.0-6
## [69] rmarkdown_1.14 gtable_0.3.0
## [71] reshape2_1.4.3 R6_2.4.0
## [73] GenomicAlignments_1.20.1 zoo_1.8-6
## [75] lubridate_1.7.4 knitr_1.24
## [77] utf8_1.1.4 zeallot_0.1.0
## [79] readr_1.3.1 stringi_1.4.3
## [81] Rcpp_1.0.2 vctrs_0.2.0
## [83] tidyselect_0.2.5 xfun_0.8