E coli spike-in experiments
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 | a099d38 | Marek Cmero | 2022-08-05 | Build site. |
| Rmd | a0a39ad | Marek Cmero | 2022-08-05 | Added sample 1-10-K12 to analysis |
| html | 565d602 | Marek Cmero | 2022-07-13 | Build site. |
| Rmd | 847a8c2 | Marek Cmero | 2022-07-13 | Added variant analysis for E coli spike ins |
| html | 9a639c3 | Marek Cmero | 2022-07-12 | Build site. |
| Rmd | 2ec3680 | Marek Cmero | 2022-07-12 | Added preliminary QC results/metrics from spike-in experiments |
E coli spike-in experiment results
E coli K12 strain was spiked into E coli BL21 with different proportions:
| Lib Name | Spike in % | ~Cell equivalent* |
|---|---|---|
| 0-K12Rep1 | 0%K12Rep1(BL2 only) | 318 |
| 0-K12Rep2 | 0%K12Rep2 (BL2 only) | 202 |
| 1-K12Rep1 | 1%K12Rep1 | 601 |
| 1-K12Rep2 | 1%K12Rep2 | 585 |
| 10-K12Rep1 | 10%K12Rep1 | 86 |
| 10-K12Rep2 | 10%K12Rep2 | 74 |
| 1_10-K12Rep1 | 0.1%K12Rep1 | 11,139 |
| 5-K12Rep1 | 5%K12Rep1 | 188 |
| 5-K12Rep2 | 5%K12Rep2 | 228 |
*based on R1 unique read number.
The 1_10-K12Rep1 sample is currently omitted in this analysis as it is too large to process with the existing script.
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)
library(patchwork)
library(RColorBrewer)
library(UpSetR)
library(vcfR)
library(tidyr)source(here('code/load_data.R'))
source(here('code/plot.R'))
source(here('code/efficiency_nanoseq_functions.R'))genome_max <- 4528118
cores <- 8genomeFile <- here('data/ref/Escherichia_coli_strain_BL21_TaKaRa.fasta')
rinfo_dir <- here('data/ecoli/AGRF_CAGRF220410419_HFVGHDSX3/QC/read_info')
markdup_dir <- here('data/ecoli/AGRF_CAGRF220410419_HFVGHDSX3/QC/mark_duplicates')
qualimap_dir <- here('data/ecoli/AGRF_CAGRF220410419_HFVGHDSX3/QC/qualimap')
qualimap_cons_dir <- here('data/ecoli/AGRF_CAGRF220410419_HFVGHDSX3/QC/consensus/qualimap')
variant_dir <- here('data/ecoli/AGRF_CAGRF220410419_HFVGHDSX3/variants')sample_names <- list.files(rinfo_dir) %>%
str_split('\\.txt.gz') %>%
lapply(., dplyr::first) %>%
unlist() %>%
str_split('_') %>%
lapply(., head, 2) %>%
lapply(., paste, collapse='-') %>%
unlist()
# load variant data
var_sample_names <- list.files(variant_dir) %>%
str_split('_HFVGHDSX3') %>%
lapply(., dplyr::first) %>%
unlist()
var_df <- load_variants(variant_dir, var_sample_names) %>% calculate_vafs()
# 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)
# uncomment below to calculate metrics
# calculate metrics for nanoseq
rlen <- 151; skips <- 5
metrics <- calc_metrics_new_rbs(rinfo_dir, cores = cores) %>% bind_rows()
metrics$duplicate_rate <- mdup
metrics$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$sample <- gsub('-HFVGHDSX3', '', sample_names)
# cache metrics object
# saveRDS(metrics, file = here('data/metrics.rds'))
# prepare for plotting
mm <- data.frame(reshape2::melt(metrics))
colnames(mm)[2] <- 'metric'ggplot(mm, aes(sample, value)) +
geom_point() +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~metric, scales = 'free') +
scale_colour_brewer(palette = 'Dark2')
Metric comparison plots
Duplicate rate
Fraction of duplicate reads calculated by Picard’s MarkDuplicates. This is based on barcode-aware aligned duplicates mapping to the same 5’ positions for both read pairs. The NanoSeq Analysis pipeline states the optimal empirical duplicate rate is 75-76% (marked in the plot).
metric <- 'duplicate_rate'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
geom_hline(yintercept = c(0.75, 0.76), alpha = 0.4) +
ggtitle(metric)
Fraction of singleton reads
Shows the number of single-read families divided by the total number of reads. As suggested by Stoler et al. 2016, this metric can server as a proxy for error rate, as (uncorrected) barcode mismatches will manifest as single-read families. The lower the fraction of singletons, the better.
metric <- 'frac_singletons'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
Drop-out rate
This is the same calculation as F-EFF in the NanoSeq Analysis pipeline:
“This shows the fraction of read bundles missing one of the two original strands beyond what would be expected under random sampling (assuming a binomial process). Good values are between 0.10-0.30, and larger values are likely due to DNA damage such as modified bases or internal nicks that prevent amplification of one of the two strands. Larger values do not impact the quality of the results, just reduce the efficiency of the protocol.”
This is similar to the singleton fraction, but taking into account loss of pairs due to sampling. The optimal range is shown by the lines.
metric <- 'drop_out_rate'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
geom_hline(yintercept = c(0.1, 0.3), alpha = 0.4) +
ggtitle(metric)
Efficiency
Efficiency is the number of duplex bases divided by the number of sequenced bases. According the NanoSeq Analysis pipeline, this value is maximised at ~0.07 when duplicate rates and strand drop-outs are optimal.
metric <- 'efficiency'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
geom_hline(yintercept = c(0.07), alpha = 0.4) +
ggtitle(metric)
GC deviation
GC deviation is the absolute difference between GC_BOTH
and GC_SINGLE calculated by the NanoSeq Analysis
pipeline. The lower this deviation, the better.
“GC_BOTH and GC_SINGLE: the GC content of RBs with both strands and with just one strand. The two values should be similar between them and similar to the genome average. If there are large deviations that is possibly due to biases during PCR amplification. If GC_BOTH is substantially larger than GC_SINGLE, DNA denaturation before dilution may have taken place.”
metric <- 'gc_deviation'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
Duplex Coverage ratio
The mean sequence (pre-duplex) coverage divided by mean duplex coverage. Indicates the yield of how much duplex coverage we get at each sample’s sequence coverage. Abascal et al. report that their yield was approximately 30x (marked on the plot).
metric <- 'duplex_coverage_ratio'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
geom_hline(yintercept = 30, alpha = 0.4) +
ggtitle(metric)
Family statistics
Comparison of family pair sizes between samples (these are calculated from total reads of paired AB and BA families).
ggplot(mm[mm$metric %like% 'family', ], aes(value, sample, colour = metric)) +
geom_point() +
coord_trans(x='log2') +
scale_x_continuous(breaks=seq(0, 94, 8)) +
theme(axis.text.x = element_text(size=5)) +
theme_bw() +
ggtitle('Family pair sizes')
The following plot shows:
- families_gt1: number of family pairs where at least one family (AB or BA) has > 1 reads.
- paired_families: number of family pairs where both families (AB and BA) have > 0 reads.
- paired_and_gt1: number of family pairs where both families (AB and BA) have > 1 reads.
p1 <- ggplot(mm[mm$metric %like% 'pair|gt1', ], aes(value, sample, fill = metric)) +
geom_bar(stat='identity', position='dodge') +
theme_bw() +
ggtitle('Family statistics')
p2 <- ggplot(mm[mm$metric %like% 'pair|gt1' & !mm$sample %like% '1-10', ], aes(value, sample, fill = metric)) +
geom_bar(stat='identity', position='dodge') +
theme_bw() +
ggtitle('Family statistics without 0.1% spike-in sample')
p1 + p2
Variant calling analysis
Here we show the VAF mean, number of variants called, as well as a number of other metrics used in estimating number of variants called.
# number of differing variant sites between the E coli genomes
N_TOTAL_VARS <- 33655
COVERAGE_PER_GENOME <- 10
vaf_sm <- data.table(var_df)[, list(VAF_mean = mean(VAF), nvars = length(POS)), by=sample] %>%
mutate(VAF_mix = as.character(sample) %>% strsplit('-K12Rep') %>% lapply(dplyr::first) %>% unlist()) %>%
left_join(., select(metrics, c(sample, efficiency)), by='sample') %>%
separate(col = sample, sep = 'Rep', into = c('sample', 'replicate'))
vaf_sm$VAF_mix[vaf_sm$VAF_mix == '1-10'] <- 0.1
# vaf_sm$efficiency[vaf_sm$sample == ] <- 0.002 # a guess
# vaf_sm$cells <- c(318, 202, 11139, 601, 585, 86, 74, 188, 228)
vaf_sm$VAF_mix <- as.numeric(vaf_sm$VAF_mix) / 100
vaf_sm$cells <- c(318, 202, 11139, 601, 585, 86, 74, 188, 228)
vaf_sm$coverage <- qmap_cons_cov$coverage
vaf_sm$expected_coverage <- vaf_sm$cells * COVERAGE_PER_GENOME * vaf_sm$efficiency
print(vaf_sm) sample replicate VAF_mean nvars VAF_mix efficiency cells coverage
1: 0-K12 1 0.144585508 192 0.000 0.003411398 318 16.4145
2: 0-K12 2 0.159597801 122 0.000 0.002459236 202 8.5023
3: 1-10-K12 1 0.005226775 14202 0.001 0.003678547 11139 424.9759
4: 1-K12 1 0.070934203 8096 0.010 0.002135843 601 20.4558
5: 1-K12 2 0.087725317 6204 0.010 0.001740567 585 16.3451
6: 10-K12 1 0.222747259 1796 0.100 0.001806526 86 2.3988
7: 10-K12 2 0.230245999 723 0.100 0.001463852 74 1.8268
8: 5-K12 1 0.186009906 5788 0.050 0.001680279 188 5.1634
9: 5-K12 2 0.185332683 6394 0.050 0.001364435 228 5.3801
expected_coverage
1: 10.848247
2: 4.967657
3: 409.753348
4: 12.836414
5: 10.182318
6: 1.553612
7: 1.083251
8: 3.158924
9: 3.110913Expected coverage
Here we plot the observed mean coverage versus the expected coverage, the latter is calculated as \(n * c * d\) where \(n =\) number of input cells, \(c =\) target coverage per genome equivalent (10) and \(d =\) duplex efficiency.
We can see that the real coverage is higher than expected, this is likely due to the efficiency calculation being based on 2 minimum reads per strand, whereas we ran duplex consensus calling without SSC.
ggplot(vaf_sm, aes(expected_coverage, coverage, shape=sample)) +
geom_point() +
theme_minimal() +
geom_abline(slope = 1)
Expected variants
Here we use the revised model to estimate the number of variants we expected to call with 95% confidence, using the formula above.
vaf_sm$expected_variants <- (1 - (1 - vaf_sm$VAF_mix) ^ round(vaf_sm$expected_coverage)) * N_TOTAL_VARS
vaf_sm$expected_variants_cov <- (1 - (1 - vaf_sm$VAF_mix) ^ round(vaf_sm$coverage)) * N_TOTAL_VARS
p1 <- ggplot(vaf_sm, aes(expected_variants, nvars, shape=sample)) +
geom_point() +
theme_minimal() +
geom_abline(slope = 1) +
scale_x_continuous(limits = c(0,15000)) +
scale_y_continuous(limits = c(0,15000)) +
ggtitle('Expected vs. actual variants, based on expected coverage')
p2 <- ggplot(vaf_sm, aes(expected_variants_cov, nvars, shape=sample)) +
geom_point() +
theme_minimal() +
geom_abline(slope = 1) +
scale_x_continuous(limits = c(0,15000)) +
scale_y_continuous(limits = c(0,15000)) +
ggtitle('Expected vs. actual variants, based on actual coverage')
p1 + p2
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] tidyr_1.1.3 vcfR_1.12.0 UpSetR_1.4.0
[4] RColorBrewer_1.1-3 patchwork_1.1.1 readxl_1.3.1
[7] seqinr_4.2-8 Rsamtools_2.6.0 Biostrings_2.58.0
[10] XVector_0.30.0 GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[13] IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.36.1
[16] stringr_1.4.0 tibble_3.1.7 here_1.0.1
[19] dplyr_1.0.7 data.table_1.14.0 ggplot2_3.3.6
[22] 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] highr_0.9 fastmap_1.1.0 rlang_1.0.2
[31] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4
[34] generics_0.1.1 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 reshape2_1.4.4
[61] glue_1.6.2 evaluate_0.14 memuse_4.2-1
[64] vctrs_0.4.1 httpuv_1.6.3 cellranger_1.1.0
[67] gtable_0.3.0 purrr_0.3.4 assertthat_0.2.1
[70] xfun_0.22 later_1.3.0 viridisLite_0.4.0
[73] cluster_2.1.2 ellipsis_0.3.2