E. coli K12 analysis
Last updated: 2022-03-25
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Knit directory: rare-mutation-detection/
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|---|---|---|---|---|
| Rmd | 1926d3d | Marek Cmero | 2022-03-25 | added K12 ecoli metrics |
Metrics for E. coli K12 data
library(ggplot2)
library(data.table)
library(dplyr)
library(here)
library(tibble)
library(stringr)
library(Rsamtools)
library(GenomicRanges)
library(seqinr)
library(parallel)source(here('code/load_data.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')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$sample <- sample_namesMetric comparison plots
mm <- data.frame(melt(metrics))
metrics_to_plot <- as.character(mm$variable) %>% unique()
for(metric in metrics_to_plot) {
p <- ggplot(mm[mm$variable == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
show(p)
}
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] seqinr_4.2-8 Rsamtools_2.6.0 Biostrings_2.58.0
[4] XVector_0.30.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[7] IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.36.1
[10] stringr_1.4.0 tibble_3.1.5 here_1.0.1
[13] dplyr_1.0.7 data.table_1.14.0 ggplot2_3.3.5
[16] 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] R6_2.5.1 evaluate_0.14 highr_0.9
[10] pillar_1.6.4 zlibbioc_1.36.0 rlang_0.4.12
[13] whisker_0.4 jquerylib_0.1.4 rmarkdown_2.11
[16] labeling_0.4.2 BiocParallel_1.24.1 RCurl_1.98-1.3
[19] munsell_0.5.0 compiler_4.0.5 httpuv_1.6.3
[22] xfun_0.22 pkgconfig_2.0.3 htmltools_0.5.2
[25] tidyselect_1.1.1 GenomeInfoDbData_1.2.4 fansi_0.5.0
[28] crayon_1.4.2 withr_2.4.2 later_1.3.0
[31] MASS_7.3-53.1 bitops_1.0-7 grid_4.0.5
[34] jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.1
[37] DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1
[40] scales_1.1.1 stringi_1.7.5 farver_2.1.0
[43] reshape2_1.4.4 fs_1.5.0 promises_1.2.0.1
[46] bslib_0.3.0 ellipsis_0.3.2 generics_0.1.1
[49] vctrs_0.3.8 tools_4.0.5 ade4_1.7-18
[52] glue_1.4.2 purrr_0.3.4 fastmap_1.1.0
[55] yaml_2.2.1 colorspace_2.0-0 knitr_1.33
[58] sass_0.4.0