E. coli K12 analysis
Last updated: 2022-03-31
Checks: 7 0
Knit directory: rare-mutation-detection/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 05412f6 | Marek Cmero | 2022-03-28 | Build site. |
| Rmd | ea0ad82 | Marek Cmero | 2022-03-28 | Added singleton comparison + facet summary plots |
| html | 51aba0e | Marek Cmero | 2022-03-25 | Build site. |
| Rmd | a3895f7 | Marek Cmero | 2022-03-25 | Bug fix |
| html | ea4faf4 | Marek Cmero | 2022-03-25 | Build site. |
| Rmd | 5964f14 | Marek Cmero | 2022-03-25 | Added more comparison plots for ecoli K12 data |
| html | e5b39ad | Marek Cmero | 2022-03-25 | Build site. |
| Rmd | 1926d3d | Marek Cmero | 2022-03-25 | added K12 ecoli metrics |
Metrics for E. coli K12 data
MultiQC reports:
library(ggplot2)
library(data.table)
library(dplyr)
library(here)
library(tibble)
library(stringr)
library(Rsamtools)
library(GenomicRanges)
library(seqinr)
library(parallel)
library(readxl)source(here('code/load_data.R'))
source(here('code/plot.R'))
source(here('code/efficiency_nanoseq_functions.R'))# Ecoli genome max size
# genome_max <- 4528118
genome_max <- c('2e914854fabb46b9_1' = 4661751,
'2e914854fabb46b9_2' = 67365)
cores = 8genomeFile <- here('data/ref/Escherichia_coli_ATCC_10798.fasta')
rinfo_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/QC/read_info')
markdup_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/QC/mark_duplicates')
qualimap_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/QC/qualimap')
qualimap_cons_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/QC/consensus/qualimap')
metadata_file <- here('data/metadata/NovaSeq data E coli.xlsx')sample_names <- list.files(rinfo_dir) %>%
str_split('\\.txt.gz') %>%
lapply(., dplyr::first) %>%
unlist() %>%
str_split('_') %>%
lapply(., head, 2) %>%
lapply(., paste, collapse='-') %>%
unlist()
# load and fetch duplicate rate from MarkDuplicates output
mdup <- load_markdup_data(markdup_dir, sample_names)
# get mean coverage for pre and post-consensus reads
qmap_cov <- get_qmap_coverage(qualimap_dir, sample_names)
qmap_cons_cov <- get_qmap_coverage(qualimap_cons_dir, sample_names)
# calculate metrics for nanoseq
rlen <- 151; skips <- 5
metrics_nano <- calc_metrics_new_rbs(rinfo_dir, pattern = 'Nano', cores = cores)
# calculate metrics for xGen
rlen <- 151; skips <- 8
metrics_xgen <- calc_metrics_new_rbs(rinfo_dir, pattern = 'xGEN', cores = cores)
metrics <- c(metrics_nano, metrics_xgen) %>% bind_rows()
metrics$duplicate_rate <- as.numeric(mdup)
metrics$duplex_coverage_ratio <- qmap_cov$coverage / qmap_cons_cov$coverage
metrics$duplex_coverage_ratio[qmap_cons_cov$coverage < 1] <- 0 # fix when < 1 duplex cov
metrics$sample <- sample_names
# load metadata
metadata <- read_excel(metadata_file)
metadata$`sample name` <- gsub('_', '-', metadata$`sample name`)
# prepare for plotting
mm <- data.frame(melt(metrics))
mm$protocol <- 'NanoSeq'
mm$protocol[grep('xGEN', mm$sample)] <- 'xGen'
mm$sample <- gsub('-HJK2GDSX3', '', mm$sample)
mm <- inner_join(mm, metadata, by=c('sample' = 'sample name'))
colnames(mm)[2] <- 'metric'
mm$nuclease <- paste(mm$`Mung bean unit`, mm$`S1 unit`, sep='+')Metric comparison plots
metrics_to_plot <- as.character(mm$metric) %>% unique()
for(metric in metrics_to_plot) {
p <- ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
show(p)
}
Compare protocols and nucleases directly.
# singletons
plot_metric_boxplot(mm, 'protocol', 'frac_singletons', 'Fraction of singleton reads') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease))
plot_metric_boxplot(mm, 'nuclease', 'frac_singletons', 'Fraction of singleton reads') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol)) 
# duplicate rate
plot_metric_boxplot(mm, 'protocol', 'duplicate_rate', 'Duplicate rate (line = optimal)') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease)) +
geom_hline(yintercept = 0.81)
plot_metric_boxplot(mm, 'nuclease', 'duplicate_rate', 'Duplicate rate (line = optimal)') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol)) +
geom_hline(yintercept = 0.81)
# efficiency
plot_metric_boxplot(mm, 'protocol', 'efficiency', 'Efficiency (line = optimal)') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease)) +
geom_hline(yintercept = 0.07) 
plot_metric_boxplot(mm, 'nuclease', 'efficiency', 'Efficiency (line = optimal)') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol)) +
geom_hline(yintercept = 0.07) 
# drop out rate
plot_metric_boxplot(mm, 'protocol', 'drop_out_rate', 'Strand drop-out fraction (lines = optimal range)') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease)) +
geom_hline(yintercept = c(0.1, 0.3))
plot_metric_boxplot(mm, 'nuclease', 'drop_out_rate', 'Strand drop-out fraction (lines = optimal range)') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol)) +
geom_hline(yintercept = c(0.1, 0.3))
# GC deviation between strands
plot_metric_boxplot(mm, 'protocol', 'gc_deviation', 'GC deviation (both strands vs. one)') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease))
plot_metric_boxplot(mm, 'nuclease', 'gc_deviation', 'GC deviation (both strands vs. one)') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol))
# duplex coverage ratio
plot_metric_boxplot(mm, 'protocol', 'duplex_coverage_ratio', 'Duplex coverage ratio (total cov / duplex cov)') +
geom_jitter(width=0.1, aes(protocol, value, colour = nuclease)) +
geom_hline(yintercept = 30)
| Version | Author | Date |
|---|---|---|
| 05412f6 | Marek Cmero | 2022-03-28 |
plot_metric_boxplot(mm, 'nuclease', 'duplex_coverage_ratio', 'Duplex coverage ratio (total cov / duplex cov)') +
geom_jitter(width=0.1, aes(nuclease, value, colour = protocol)) +
geom_hline(yintercept = 30)
| Version | Author | Date |
|---|---|---|
| 05412f6 | Marek Cmero | 2022-03-28 |
Facet boxplots by nuclease and protocol to show overall results.
ggplot(mm, aes(nuclease, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free')
| Version | Author | Date |
|---|---|---|
| 05412f6 | Marek Cmero | 2022-03-28 |
ggplot(mm, aes(protocol, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free')
| Version | Author | Date |
|---|---|---|
| 05412f6 | Marek Cmero | 2022-03-28 |
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /stornext/System/data/apps/R/R-4.0.5/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/apps/R/R-4.0.5/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] readxl_1.3.1 seqinr_4.2-8 Rsamtools_2.6.0
[4] Biostrings_2.58.0 XVector_0.30.0 GenomicRanges_1.42.0
[7] GenomeInfoDb_1.26.7 IRanges_2.24.1 S4Vectors_0.28.1
[10] BiocGenerics_0.36.1 stringr_1.4.0 tibble_3.1.5
[13] here_1.0.1 dplyr_1.0.7 data.table_1.14.0
[16] ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 assertthat_0.2.1 rprojroot_2.0.2
[4] digest_0.6.27 utf8_1.2.2 plyr_1.8.6
[7] cellranger_1.1.0 R6_2.5.1 evaluate_0.14
[10] highr_0.9 pillar_1.6.4 zlibbioc_1.36.0
[13] rlang_0.4.12 whisker_0.4 jquerylib_0.1.4
[16] rmarkdown_2.11 labeling_0.4.2 BiocParallel_1.24.1
[19] RCurl_1.98-1.3 munsell_0.5.0 compiler_4.0.5
[22] httpuv_1.6.3 xfun_0.22 pkgconfig_2.0.3
[25] htmltools_0.5.2 tidyselect_1.1.1 GenomeInfoDbData_1.2.4
[28] fansi_0.5.0 crayon_1.4.2 withr_2.4.2
[31] later_1.3.0 MASS_7.3-53.1 bitops_1.0-7
[34] grid_4.0.5 jsonlite_1.7.2 gtable_0.3.0
[37] lifecycle_1.0.1 DBI_1.1.1 git2r_0.28.0
[40] magrittr_2.0.1 scales_1.1.1 stringi_1.7.5
[43] farver_2.1.0 reshape2_1.4.4 fs_1.5.0
[46] promises_1.2.0.1 bslib_0.3.0 ellipsis_0.3.2
[49] generics_0.1.1 vctrs_0.3.8 tools_4.0.5
[52] ade4_1.7-18 glue_1.4.2 purrr_0.3.4
[55] fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-0
[58] knitr_1.33 sass_0.4.0