Last updated: 2021-12-16

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Knit directory: rare-mutation-detection-rmarkdown/

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File	Version	Author	Date	Message
Rmd	7664b09	mcmero	2021-12-16	Updated to handle VCF output
html	e5ed9a7	Marek Cmero	2021-12-15	Build site.
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)

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 summariesed to 10kb windows, though I coulnd’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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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)')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

ggplot(melt(ccov_stats), aes(value, Sample, fill=variable)) +
    geom_bar(stat='identity', position = 'dodge') +
    theme_bw() +
    ggtitle('Coverage std & mean (duplex)')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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)')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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)')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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')

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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)

Version	Author	Date
de277d9	Marek Cmero	2021-12-15

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] here_1.0.1        UpSetR_1.4.0      R.utils_2.11.0    R.oo_1.24.0      
[5] R.methodsS3_1.8.1 dplyr_1.0.7       data.table_1.14.0 ggplot2_3.3.5    
[9] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.1 xfun_0.25        bslib_0.3.1      reshape2_1.4.4  
 [5] purrr_0.3.4      colorspace_2.0-2 vctrs_0.3.8      generics_0.1.0  
 [9] htmltools_0.5.2  yaml_2.2.1       utf8_1.2.2       rlang_0.4.11    
[13] jquerylib_0.1.4  later_1.3.0      pillar_1.6.2     glue_1.4.2      
[17] withr_2.4.2      DBI_1.1.1        lifecycle_1.0.0  plyr_1.8.6      
[21] stringr_1.4.0    munsell_0.5.0    gtable_0.3.0     evaluate_0.14   
[25] labeling_0.4.2   knitr_1.33       fastmap_1.1.0    httpuv_1.6.3    
[29] fansi_0.5.0      highr_0.9        Rcpp_1.0.7       promises_1.2.0.1
[33] scales_1.1.1     jsonlite_1.7.2   farver_2.1.0     fs_1.5.0        
[37] gridExtra_2.3    digest_0.6.27    stringi_1.7.4    grid_4.1.2      
[41] rprojroot_2.0.2  tools_4.1.2      magrittr_2.0.1   sass_0.4.0      
[45] tibble_3.1.4     crayon_1.4.1     whisker_0.4      pkgconfig_2.0.3 
[49] ellipsis_0.3.2   assertthat_0.2.1 rmarkdown_2.11   R6_2.5.1        
[53] git2r_0.28.0     compiler_4.1.2