Compare sequencing metrics from E coli data
Last updated: 2021-12-16
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Knit directory: rare-mutation-detection-rmarkdown/
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| File | Version | Author | Date | Message |
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| Rmd | ce8d35d | mcmero | 2021-12-16 | discordant variant analysis |
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| Rmd | 7664b09 | mcmero | 2021-12-16 | Updated to handle VCF output |
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| Rmd | ed42fa9 | Marek Cmero | 2021-12-15 | Added multiQC reports |
| html | de277d9 | Marek Cmero | 2021-12-15 | Build site. |
| Rmd | e610f97 | Marek Cmero | 2021-12-15 | Added ecoli analysis |
Compare sequencing metrics from E coli data
These are extra stats that are not available in the MultiQC reports. These reports can be found below:
library(ggplot2)
library(data.table)
library(dplyr)
library(R.utils)
library(UpSetR)
library(here)
library(vcfR)
library(tibble)load_data <- function(fdir, pattern, samples, read_func=read.delim) {
df <- list.files(
fdir,
full.names = TRUE,
recursive = TRUE,
pattern = pattern) %>%
lapply(., read_func)
for (i in seq(length(samples))) {
df[[i]]$Sample <- samples[i]
}
df <- rbindlist(df)
return(df)
}
extract_std <- function(genome_results) {
std <- genome_results[grep('std', genome_results$BamQC.report),] %>%
strsplit(., split='=') %>%
last() %>% last() %>%
gsub(' |X', '', .) %>% as.numeric()
return(std)
}
load_cov_stats <- function(cov, qualimap_dir, samples) {
cov_stats <- list.files(
qualimap_dir,
full.names = TRUE,
recursive = TRUE,
pattern = 'genome_results.txt') %>%
lapply(., read.delim) %>%
lapply(., extract_std) %>%
unlist()
cov_stats <- data.frame(cov_std=cov_stats, Sample=samples)
cov_stats <- data.table(cov)[,mean(Coverage), by=Sample] %>%
data.frame() %>% inner_join(., cov_stats, by='Sample')
colnames(cov_stats)[2] <- 'cov_mean'
return(cov_stats)
}qualimap_dir <- here('data/ecoli/QC/qualimap/')
qualimap_cons_dir <- here('data/ecoli/QC/consensus/qualimap/')
variant_dir <- here('data/ecoli/variants')
family_size_stats <- here('data/ecoli/family_sizes.txt')
samples <- list.files(qualimap_dir)
cov <- load_data(qualimap_dir, 'coverage_across_reference.txt', samples)
ccov <- load_data(qualimap_cons_dir, 'coverage_across_reference.txt', samples)
clip <- load_data(qualimap_dir, 'mapped_reads_clipping_profile', samples)
cclip <- load_data(qualimap_cons_dir, 'mapped_reads_clipping_profile', samples)
vars <- load_data(variant_dir, '.vcf', samples, read.table)
cov_stats <- load_cov_stats(cov, qualimap_dir, samples)
ccov_stats <- load_cov_stats(ccov, qualimap_cons_dir, samples)
fam <- read.delim(family_size_stats, sep='\t')Coverage boxplot
Using coverage summary data from Qualimap (I assume these are summarised to 10kb windows, though I couldn’t find this in the documentation).
# order by median coverage
median_cov <- data.table(cov)[,median(Coverage), by=Sample]
cov$Sample <- factor(cov$Sample, levels=median_cov[order(median_cov$V1)]$Sample)
ggplot(cov, aes(Coverage, Sample)) + geom_boxplot() + theme_bw() + ggtitle('Pre-duplex coverage')
median_cov <- data.table(ccov)[,median(Coverage), by=Sample]
ccov$Sample <- factor(ccov$Sample, levels=median_cov[order(median_cov$V1)]$Sample)
ggplot(ccov, aes(Coverage, Sample)) + geom_boxplot() + theme_bw() + ggtitle('Duplex coverage')
Coverage standard deviation bar plot
ggplot(melt(cov_stats), aes(value, Sample, fill=variable)) +
geom_bar(stat='identity', position = 'dodge') +
theme_bw() +
ggtitle('Coverage std & mean (pre-duplex)')
ggplot(melt(ccov_stats), aes(value, Sample, fill=variable)) +
geom_bar(stat='identity', position = 'dodge') +
theme_bw() +
ggtitle('Coverage std & mean (duplex)')
cov_stats$cov_cv <- cov_stats$cov_std / cov_stats$cov_mean
ggplot(cov_stats, aes(cov_cv, Sample)) +
geom_bar(stat='identity', position = 'dodge') +
theme_bw() +
ggtitle('Coverage CV (pre-duplex)')
ccov_stats$cov_cv <- ccov_stats$cov_std / ccov_stats$cov_mean
ggplot(ccov_stats, aes(cov_cv, Sample)) +
geom_bar(stat='identity', position = 'dodge') +
theme_bw() +
ggtitle('Coverage CV (duplex)')
Clipping profile
Pre-duplex reads prior to overlap clipping, but post-UMI removal.
ggplot(clip, aes(X.Read.position..bp., Clipping.profile)) +
geom_line() +
theme_bw() +
xlab('Read position') +
facet_wrap(~Sample) +
ggtitle('Pre-duplex clipping profile')
Duplex reads have been clipped to remove read overlap.
ggplot(cclip, aes(X.Read.position..bp., Clipping.profile)) +
geom_line() +
theme_bw() +
xlab('Read position') +
facet_wrap(~Sample) +
ggtitle('Duplex clipping profile')
Family size stats (duplex statistics)
- frac_fam_gt1: the fraction of families where family size is greater than one.
- frac_fam_paired: the fraction of paired families (a family on each strand with the same UMI).
- frac_fam_paired_gt1: the fraction of families that are greater than one in size, and also paired.
ggplot(fam, aes(len, sample)) +
geom_bar(stat='identity') +
theme_bw() +
ggtitle('Total family count')
mfam <- reshape2::melt(fam[,c('sample', 'min', 'max', 'mean', 'median')])
ggplot(mfam, aes(value, sample, colour=variable)) +
geom_point() +
theme_bw() +
coord_trans(x='log2') +
scale_x_continuous(breaks=seq(0, 25, 2)) +
theme(axis.text.x = element_text(size=6)) +
ggtitle('Family sizes')
mfam <- reshape2::melt(fam[,colnames(fam) %like% 'frac|sample'])
ggplot(mfam, aes(value, sample, fill=variable)) +
geom_bar(stat='identity', position='dodge') +
theme_bw() +
ggtitle('Family reads + paired statistics')
Variants
vars$Sample <- strsplit(vars$Sample, '\\.') %>% lapply(., head, 1) %>% unlist()
ulist <- NULL
for(sample in samples) {
ulist[[sample]] <- vars[vars$Sample %in% sample,]$V2
}
upset(fromList(ulist), order.by='freq', nsets=8)
Check how many variants have at least one reference base called.
vcfs <- list.files(variant_dir,
full.names = TRUE) %>%
lapply(., read.vcfR, verbose = FALSE) %>%
lapply(., vcfR2tidy,
format_fields = c('RDR', 'RDF', 'VDR', 'VDF'),
verbose = FALSE)
disc <- lapply(vcfs, function(vcf) {
has_ref <- vcf$gt$gt_RDR + vcf$gt$gt_RDF > 0
row <- c(sum(has_ref), nrow(vcf$gt))
return(row)
})
disc <- data.frame(disc, row.names = c('has_ref_bases','total_variants')) %>% t() %>%
data.frame(row.names = 1:nrow(.)) %>%
add_column(sample=samples)
ggplot(melt(disc), aes(value, sample, fill = variable)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw()
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tibble_3.1.4 vcfR_1.12.0 here_1.0.1 UpSetR_1.4.0
[5] R.utils_2.11.0 R.oo_1.24.0 R.methodsS3_1.8.1 dplyr_1.0.7
[9] data.table_1.14.0 ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 ape_5.5 lattice_0.20-45 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.2 R6_2.5.1
[9] plyr_1.8.6 evaluate_0.14 highr_0.9 pillar_1.6.2
[13] rlang_0.4.11 whisker_0.4 vegan_2.5-7 jquerylib_0.1.4
[17] Matrix_1.3-4 rmarkdown_2.11 labeling_0.4.2 splines_4.1.2
[21] pinfsc50_1.2.0 stringr_1.4.0 munsell_0.5.0 compiler_4.1.2
[25] httpuv_1.6.3 xfun_0.25 pkgconfig_2.0.3 mgcv_1.8-38
[29] htmltools_0.5.2 tidyselect_1.1.1 gridExtra_2.3 memuse_4.2-1
[33] fansi_0.5.0 permute_0.9-5 viridisLite_0.4.0 crayon_1.4.1
[37] withr_2.4.2 later_1.3.0 MASS_7.3-54 grid_4.1.2
[41] nlme_3.1-153 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0
[45] DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1 scales_1.1.1
[49] stringi_1.7.4 reshape2_1.4.4 farver_2.1.0 fs_1.5.0
[53] promises_1.2.0.1 bslib_0.3.1 ellipsis_0.3.2 generics_0.1.0
[57] vctrs_0.3.8 tools_4.1.2 glue_1.4.2 purrr_0.3.4
[61] parallel_4.1.2 fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2
[65] cluster_2.1.2 knitr_1.33 sass_0.4.0