E. coli in silico mixture results
Last updated: 2022-08-16
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Knit directory: rare-mutation-detection/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e6092ec | Marek Cmero | 2022-08-16 | Added duplicate rate stat to 0.1% spike-in sample |
| html | b524238 | Marek Cmero | 2022-05-26 | Build site. |
| Rmd | afd79e5 | Marek Cmero | 2022-05-26 | Added revised model, in silico mixtures redone with 1 supporting read, added input cell estimates |
| html | 39c5a18 | Marek Cmero | 2022-05-20 | Build site. |
| Rmd | d97ddd6 | Marek Cmero | 2022-05-20 | Update experiment text |
| html | 4499502 | Marek Cmero | 2022-05-20 | Build site. |
| Rmd | 77280c2 | Marek Cmero | 2022-05-20 | Added 0.001 mix results |
| html | 98aadc0 | Marek Cmero | 2022-05-20 | Build site. |
| Rmd | 727938e | Marek Cmero | 2022-05-20 | Added mixture results for 0.01 and 0.05 |
| html | 491e97d | Marek Cmero | 2022-05-19 | Build site. |
| Rmd | 434e8b9 | Marek Cmero | 2022-05-19 | Added in silico mixtures to navigation |
| html | ff1f665 | Marek Cmero | 2022-05-19 | Build site. |
| Rmd | 572a31d | Marek Cmero | 2022-05-19 | Added initial results from in silico mixture experiment |
In this experiment, the duplex reads from NanoSeq MB2 rep 1 from E coli K12 was mixed in pairs (in silico) at four depths (0.1x, 1x, 5x and 10x) with the duplex reads from NanoSeq MB2 (MGI) from E coli BL21 at four depths (99.9x, 99x, 95x and 90x). Variants called required a coverage of at least 4 and at least 1 supporting variant read. There are >33k SNP and INDEL differences between the E coli species. Given the mixture and total depth we can calculate the number of SNPs that we expect to find.
library(ggplot2)
library(data.table)
library(dplyr)
library(here)
library(tibble)
library(stringr)
library(Rsamtools)
library(GenomicRanges)
library(seqinr)
library(readxl)
library(patchwork)
library(RColorBrewer)
library(UpSetR)
library(vcfR)source(here('code/load_data.R'))variant_dir <- here('data/mixtures/min_1_read')var_df <- load_variants(variant_dir, c('NanoMB2-0.001', 'NanoMB2-0.01', 'NanoMB2-0.05', 'NanoMB2-0.10')) %>% calculate_vafs()
var_df$sample <- factor(var_df$sample, levels = c('NanoMB2-0.001', 'NanoMB2-0.01', 'NanoMB2-0.05', 'NanoMB2-0.10'))VAF mixture distributions
NanoSeq MB2 mixes:
- 0.1:99.9x KL12:BL21 (0.001 mix)
- 1:99x K12:BL21 (0.01 mix)
- 5:95x K12:BL21 (0.05 mix)
- 10:90x K12:BL21 (0.10 mix)
# NOTE: variant number obtained from nucdiff difference between two genomes
# cat G000204/workflow/jafarJ_201021_duplex/duplex-pipe/ref/nucdiff/ecoli_BL21_vs_ATCCecoli_BL21_vs_ATCC_1.snps | cut -f 1-2 | grep -v "\." | cut -f 1 | sort | uniq | wc -l
vaf_sm <- data.table(var_df)[, list(VAF_mean = mean(VAF), nvars = length(POS)), by=sample] %>%
mutate(VAF_mix = as.character(sample) %>% strsplit('-') %>% lapply(last) %>% unlist() %>% as.numeric())
vaf_sm$expected <- (1 - (1 - vaf_sm$VAF_mix) ^ 100) * 33655
print(vaf_sm) sample VAF_mean nvars VAF_mix expected
1: NanoMB2-0.001 0.01774953 3100 0.001 3204.22
2: NanoMB2-0.01 0.01768270 18358 0.010 21336.18
3: NanoMB2-0.05 0.04655841 32537 0.050 33455.74
4: NanoMB2-0.10 0.08925551 33697 0.100 33654.11ggplot(var_df, aes(VAF)) +
geom_histogram(bins = 50) +
theme_minimal() +
facet_wrap(~sample) +
scale_x_continuous(breaks = seq(0, 1, 0.1), limits = c(0, 1))
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] vcfR_1.12.0 UpSetR_1.4.0 RColorBrewer_1.1-3
[4] patchwork_1.1.1 readxl_1.3.1 seqinr_4.2-8
[7] Rsamtools_2.6.0 Biostrings_2.58.0 XVector_0.30.0
[10] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7 IRanges_2.24.1
[13] S4Vectors_0.28.1 BiocGenerics_0.36.1 stringr_1.4.0
[16] tibble_3.1.7 here_1.0.1 dplyr_1.0.7
[19] data.table_1.14.0 ggplot2_3.3.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-152 bitops_1.0-7 fs_1.5.0
[4] rprojroot_2.0.2 tools_4.0.5 bslib_0.3.0
[7] utf8_1.2.2 R6_2.5.1 vegan_2.5-7
[10] DBI_1.1.1 mgcv_1.8-35 colorspace_2.0-3
[13] permute_0.9-5 ade4_1.7-18 withr_2.5.0
[16] tidyselect_1.1.1 gridExtra_2.3 compiler_4.0.5
[19] git2r_0.28.0 cli_3.3.0 labeling_0.4.2
[22] sass_0.4.0 scales_1.2.0 digest_0.6.29
[25] rmarkdown_2.11 pkgconfig_2.0.3 htmltools_0.5.2
[28] fastmap_1.1.0 highr_0.9 rlang_1.0.2
[31] rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.1
[34] farver_2.1.0 jsonlite_1.7.2 BiocParallel_1.24.1
[37] RCurl_1.98-1.3 magrittr_2.0.3 GenomeInfoDbData_1.2.4
[40] Matrix_1.3-2 Rcpp_1.0.7 munsell_0.5.0
[43] fansi_1.0.3 ape_5.5 lifecycle_1.0.1
[46] stringi_1.7.5 whisker_0.4 yaml_2.2.1
[49] MASS_7.3-53.1 zlibbioc_1.36.0 plyr_1.8.6
[52] pinfsc50_1.2.0 grid_4.0.5 promises_1.2.0.1
[55] crayon_1.5.1 lattice_0.20-44 splines_4.0.5
[58] knitr_1.33 pillar_1.7.0 glue_1.6.2
[61] evaluate_0.14 memuse_4.2-1 vctrs_0.4.1
[64] httpuv_1.6.3 cellranger_1.1.0 gtable_0.3.0
[67] purrr_0.3.4 assertthat_0.2.1 xfun_0.22
[70] later_1.3.0 viridisLite_0.4.0 cluster_2.1.2
[73] ellipsis_0.3.2