QC On Trimmed Data
05 July, 2022
Last updated: 2022-07-05
Checks: 6 1
Knit directory:
20180328_Atkins_RatFracture/
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knitr::opts_chunk$set(
message = FALSE, warning = FALSE,
fig.height = 6, fig.width = 10
)library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(cowplot)
library(plotly)panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())
suffix <- "_L001_R1.fastq.gz"
pattern <- paste0("_CB2YGANXX_.+fastq.gz")
sp <- "Rnorvegicus"samples <- "data/targets.csv" %>%
here::here() %>%
read_csv() %>%
mutate(
Filename = paste0(File, suffix)
)group_cols <- hcl.colors(
n = length(unique(samples$group)),
palette = "Zissou 1"
) %>%
setNames(unique(samples$group))Quality Assessment on Trimmed Data
In the workflow, trimming was performed using the tool
AdapterRemoval with the settings:
- Minimum length after trimming: 50
- Minimum quality score to retain: 30
Overall Summary
rawFqc <- here::here("data/0_rawData/FastQC") %>%
list.files(pattern = "zip", full.names = TRUE) %>%
FastqcDataList() %>%
.[fqName(.) %in% samples$Filename]
trimFqc <- here::here("data/1_trimmedData/FastQC") %>%
list.files(pattern = "zip", full.names = TRUE) %>%
FastqcDataList() %>%
.[fqName(.) %in% samples$Filename]After trimming, the library showing the highest level of possible adapter content contained 2.42% of reads as containing possible adapter sequences.
bind_rows(
getSummary(rawFqc) %>% mutate(data = "Raw"),
getSummary(trimFqc) %>% mutate(data = "Trimmed"),
) %>%
left_join(samples) %>%
ggplot(aes(Category, Rat, fill = Status)) +
geom_tile() +
facet_grid(data~.) +
scale_fill_manual(values = getColours(pwf)[c("PASS", "WARN", "FAIL")]) +
scale_x_discrete(expand = expansion(c(0, 0))) +
scale_y_discrete(expand = expansion(c(0, 0))) +
theme(
axis.text.x = element_text(angle= 90, hjust = 1, vjust = 0.5),
strip.text.y = element_text(angle = 0)
)
Comparison of FastQC summaries before and after trimming
Library Sizes
readTotals(rawFqc) %>%
rename(Raw = Total_Sequences) %>%
left_join(
readTotals(trimFqc) %>%
rename(Trimmed = Total_Sequences)
) %>%
mutate(
Remaining = Trimmed / Raw,
Filename = str_remove_all(Filename, suffix)
) %>%
summarise(
across(c(Remaining, Trimmed), list(min = min, mean = mean, max = max))
) %>%
pivot_longer(everything()) %>%
separate(
name, into = c("Type", "Summary Statistic")
) %>%
pivot_wider(names_from = Type, values_from = value) %>%
mutate(
Remaining = percent(Remaining, accuracy = 0.1),
`Summary Statistic` = str_to_title(`Summary Statistic`)
) %>%
rename(Reads = Trimmed) %>%
pander(
caption = "*Summary statistics showing the results after trimming*"
)| Summary Statistic | Remaining | Reads |
|---|---|---|
| Min | 99.2% | 37,549,038 |
| Mean | 99.5% | 40,560,914 |
| Max | 99.8% | 44,761,237 |
readTotals(rawFqc) %>%
rename(Raw = Total_Sequences) %>%
left_join(
readTotals(trimFqc) %>%
rename(Retained = Total_Sequences),
by = "Filename"
) %>%
left_join(samples, by = "Filename")%>%
mutate(Discarded = Raw - Retained) %>%
pivot_longer(
cols = all_of(c("Retained", "Discarded")),
names_to = "Status",
values_to = "Reads"
) %>%
ggplot(aes(fct_rev(Rat), Reads/1e6, fill = Status)) +
geom_col() +
facet_grid(group~. , scales = "free_y", space = "free_y") +
scale_y_continuous(expand = expansion(c(0, 0.05))) +
scale_fill_viridis_d(option = "cividis", direction = -1) +
labs(
x = "Sample", y = "Reads (millions)"
) +
coord_flip()
Comparison of library sizes before and after trimming.
Sequence Length Distribution
ggplotly(
getModule(trimFqc, "Sequence_Length") %>%
group_by(Filename) %>%
mutate(
`Cumulative Total` = cumsum(Count),
`Cumulative Percent` = percent(`Cumulative Total` / max(`Cumulative Total`))
) %>%
ungroup() %>%
left_join(samples, by = "Filename") %>%
rename_all(str_to_title) %>%
arrange(Lower) %>%
mutate(Length = fct_inorder(Length)) %>%
ggplot(aes(Length, `Cumulative Total`, group = Filename, label = `Cumulative Percent`)) +
geom_line(aes(colour = Group), size = 1/4) +
scale_y_continuous(label = comma) +
scale_colour_manual(
values = group_cols
)
)Distribution of read lengths after trimming
GC Content
plotGcContent(
x = trimFqc,
pattern = pattern,
species = sp,
plotType = "line",
gcType = "Trans",
usePlotly = TRUE,
dendrogram = TRUE,
cluster = TRUE
)GC content shown as the % above and below the theoretical GC content for the Rnorvegicus transcriptome. The peaks at 62% and 71% are strongly suggestive of incomplete rRNA removal
Sequence Content
plotly::ggplotly(
getModule(trimFqc, module = "Per_base_sequence_content") %>%
mutate(Base = fct_inorder(Base)) %>%
group_by(Base) %>%
mutate(
across(c("A", "C", "G", "T"), function(x){x - mean(x)})
) %>%
pivot_longer(
cols = c("A", "C", "G", "T"),
names_to = "Nuc",
values_to = "resid"
) %>%
left_join(samples, by = "Filename") %>%
ggplot(
aes(Base, resid, group = Filename, colour = group)
) +
geom_line() +
facet_wrap(~Nuc) +
scale_colour_manual(values = group_cols) +
labs(
x = "Read Position", y = "Residual", colour = "Group"
) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
)
)Base and Position specific residuals for each sample. The mean base content at each position was calculated for each nucleotide, and the sample-specific residuals calculated.
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: plotly(v.4.10.0), cowplot(v.1.1.1), glue(v.1.6.2), pander(v.0.6.5), scales(v.1.2.0), yaml(v.2.3.5), forcats(v.0.5.1), stringr(v.1.4.0), dplyr(v.1.0.9), purrr(v.0.3.4), readr(v.2.1.2), tidyr(v.1.2.0), tidyverse(v.1.3.1), ngsReports(v.1.13.0), tibble(v.3.1.7), ggplot2(v.3.3.6), BiocGenerics(v.0.42.0) and workflowr(v.1.7.0)
loaded via a namespace (and not attached): bitops(v.1.0-7), fs(v.1.5.2), bit64(v.4.0.5), lubridate(v.1.8.0), RColorBrewer(v.1.1-3), httr(v.1.4.3), rprojroot(v.2.0.3), GenomeInfoDb(v.1.32.1), tools(v.4.2.0), backports(v.1.4.1), bslib(v.0.3.1), utf8(v.1.2.2), R6(v.2.5.1), DT(v.0.22), DBI(v.1.1.2), lazyeval(v.0.2.2), colorspace(v.2.0-3), withr(v.2.5.0), tidyselect(v.1.1.2), processx(v.3.5.3), bit(v.4.0.4), compiler(v.4.2.0), git2r(v.0.30.1), rvest(v.1.0.2), cli(v.3.3.0), xml2(v.1.3.3), ggdendro(v.0.1.23), labeling(v.0.4.2), sass(v.0.4.1), callr(v.3.7.0), digest(v.0.6.29), rmarkdown(v.2.14), XVector(v.0.36.0), pkgconfig(v.2.0.3), htmltools(v.0.5.2), highr(v.0.9), dbplyr(v.2.1.1), fastmap(v.1.1.0), htmlwidgets(v.1.5.4), rlang(v.1.0.2), readxl(v.1.4.0), rstudioapi(v.0.13), farver(v.2.1.0), jquerylib(v.0.1.4), generics(v.0.1.2), zoo(v.1.8-10), jsonlite(v.1.8.0), crosstalk(v.1.2.0), vroom(v.1.5.7), RCurl(v.1.98-1.6), magrittr(v.2.0.3), GenomeInfoDbData(v.1.2.8), Rcpp(v.1.0.8.3), munsell(v.0.5.0), S4Vectors(v.0.34.0), fansi(v.1.0.3), lifecycle(v.1.0.1), stringi(v.1.7.6), whisker(v.0.4), MASS(v.7.3-57), zlibbioc(v.1.42.0), grid(v.4.2.0), parallel(v.4.2.0), promises(v.1.2.0.1), crayon(v.1.5.1), lattice(v.0.20-45), Biostrings(v.2.64.0), haven(v.2.5.0), hms(v.1.1.1), knitr(v.1.39), ps(v.1.7.0), pillar(v.1.7.0), stats4(v.4.2.0), reprex(v.2.0.1), evaluate(v.0.15), getPass(v.0.2-2), data.table(v.1.14.2), modelr(v.0.1.8), vctrs(v.0.4.1), tzdb(v.0.3.0), httpuv(v.1.6.5), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.30), broom(v.0.8.0), later(v.1.3.0), viridisLite(v.0.4.0), IRanges(v.2.30.0), ellipsis(v.0.3.2) and here(v.1.0.1)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] plotly_4.10.0 cowplot_1.1.1 glue_1.6.2
[4] pander_0.6.5 scales_1.2.0 yaml_2.3.5
[7] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[10] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[13] tidyverse_1.3.1 ngsReports_1.13.0 tibble_3.1.7
[16] ggplot2_3.3.6 BiocGenerics_0.42.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.5.2 bit64_4.0.5
[4] lubridate_1.8.0 RColorBrewer_1.1-3 httr_1.4.3
[7] rprojroot_2.0.3 GenomeInfoDb_1.32.1 tools_4.2.0
[10] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[13] R6_2.5.1 DT_0.22 DBI_1.1.2
[16] lazyeval_0.2.2 colorspace_2.0-3 withr_2.5.0
[19] tidyselect_1.1.2 processx_3.5.3 bit_4.0.4
[22] compiler_4.2.0 git2r_0.30.1 rvest_1.0.2
[25] cli_3.3.0 xml2_1.3.3 ggdendro_0.1.23
[28] labeling_0.4.2 sass_0.4.1 callr_3.7.0
[31] digest_0.6.29 rmarkdown_2.14 XVector_0.36.0
[34] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9
[37] dbplyr_2.1.1 fastmap_1.1.0 htmlwidgets_1.5.4
[40] rlang_1.0.2 readxl_1.4.0 rstudioapi_0.13
[43] farver_2.1.0 jquerylib_0.1.4 generics_0.1.2
[46] zoo_1.8-10 jsonlite_1.8.0 crosstalk_1.2.0
[49] vroom_1.5.7 RCurl_1.98-1.6 magrittr_2.0.3
[52] GenomeInfoDbData_1.2.8 Rcpp_1.0.8.3 munsell_0.5.0
[55] S4Vectors_0.34.0 fansi_1.0.3 lifecycle_1.0.1
[58] stringi_1.7.6 whisker_0.4 MASS_7.3-57
[61] zlibbioc_1.42.0 grid_4.2.0 parallel_4.2.0
[64] promises_1.2.0.1 crayon_1.5.1 lattice_0.20-45
[67] Biostrings_2.64.0 haven_2.5.0 hms_1.1.1
[70] knitr_1.39 ps_1.7.0 pillar_1.7.0
[73] stats4_4.2.0 reprex_2.0.1 evaluate_0.15
[76] getPass_0.2-2 data.table_1.14.2 modelr_0.1.8
[79] vctrs_0.4.1 tzdb_0.3.0 httpuv_1.6.5
[82] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[85] xfun_0.30 broom_0.8.0 later_1.3.0
[88] viridisLite_0.4.0 IRanges_2.30.0 ellipsis_0.3.2
[91] here_1.0.1