E. coli K12 analysis
Last updated: 2022-11-10
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Knit directory: rare-mutation-detection/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f2ecd7b | Marek Cmero | 2022-11-10 | Added experimental setup table, paired T-test, plots for poster. |
| html | 55d7990 | Marek Cmero | 2022-09-13 | Build site. |
| Rmd | 22dbb2c | Marek Cmero | 2022-09-13 | Added raw variant call comparison for xGEN-xGEN samples |
| html | 10ee194 | Marek Cmero | 2022-09-08 | Build site. |
| Rmd | 2803c25 | Marek Cmero | 2022-09-08 | Minor dimension update |
| html | f4000d4 | Marek Cmero | 2022-09-08 | Build site. |
| Rmd | c5835c8 | Marek Cmero | 2022-09-08 | Plot updates |
| html | e1c0c28 | Marek Cmero | 2022-09-06 | Build site. |
| Rmd | 5cbe59d | Marek Cmero | 2022-09-06 | Plot fixes; added more family metrics |
| html | f90d40a | Marek Cmero | 2022-08-18 | Build site. |
| Rmd | 7ff227e | Marek Cmero | 2022-08-18 | Added downsampling experiments and some presentation-only plots. |
| html | 22d32ef | Marek Cmero | 2022-05-31 | Build site. |
| Rmd | e722dc0 | Marek Cmero | 2022-05-31 | Fix typo, minor code update |
| 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 | 491e97d | Marek Cmero | 2022-05-19 | Build site. |
| Rmd | 434e8b9 | Marek Cmero | 2022-05-19 | Added in silico mixtures to navigation |
| html | faf9130 | Marek Cmero | 2022-05-18 | Build site. |
| Rmd | aacf423 | Marek Cmero | 2022-05-18 | Add coverage & variant results without requiring SSC |
| html | 4da2244 | Marek Cmero | 2022-05-11 | Build site. |
| Rmd | 71e857b | Marek Cmero | 2022-05-11 | Summary plot with all experimental factors |
| html | cc380cc | Marek Cmero | 2022-05-11 | Build site. |
| Rmd | 48b6d2e | Marek Cmero | 2022-05-11 | Added statistical tests with xGen rep 1 outlier removed |
| html | 7c4f403 | Marek Cmero | 2022-04-25 | Build site. |
| Rmd | 30f532f | Marek Cmero | 2022-04-25 | Include all samples in variant upset plot |
| html | fcb6578 | Marek Cmero | 2022-04-11 | Build site. |
| Rmd | 6f2c2bb | Marek Cmero | 2022-04-11 | Add family stats, boxplot fixes |
| html | a2f0a4a | Marek Cmero | 2022-04-08 | Build site. |
| Rmd | bffbb7e | Marek Cmero | 2022-04-08 | Repeat variant analysis without filtering strand bias |
| html | c246dc2 | Marek Cmero | 2022-04-07 | Build site. |
| Rmd | e10a166 | Marek Cmero | 2022-04-07 | Added variant call upset plot |
| html | a860101 | Marek Cmero | 2022-04-06 | Build site. |
| Rmd | 5dcf0e9 | Marek Cmero | 2022-04-06 | Added relationship plots |
| html | 81272b2 | Marek Cmero | 2022-04-05 | Build site. |
| Rmd | 43c95e3 | Marek Cmero | 2022-04-05 | Fix figures |
| html | f13e13a | Marek Cmero | 2022-04-05 | Build site. |
| Rmd | db75aa7 | Marek Cmero | 2022-04-05 | Added statistical tests |
| html | def2130 | Marek Cmero | 2022-04-05 | Build site. |
| Rmd | 1e5e696 | Marek Cmero | 2022-04-05 | Added descriptions for metrics. General plot improvements. |
| html | 953b83e | Marek Cmero | 2022-03-31 | Build site. |
| 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
Experimental setup
| End-prep | Mung Bean nuclease units | S1 Nuclease units | Protocol | PCR | Samples | Key |
|---|---|---|---|---|---|---|
| MB1 | 1 | 0 | Nanoseq | limited | 2 | 1+0 |
| MB2 | 2 | 0 | Nanoseq | limited | 2 | 2+0 |
| MB3 | 3 | 0 | Nanoseq | limited | 2 | 3+0 |
| MB-S1 | 2 | 1 | Nanoseq | limited | 2 | 2+1 |
| xGEN | 0 | 0 | xGEN | limited | 2 | 0+0 |
| xGEN | 0 | 0 | xGEN | unlimited | 1 | 0+0 |
| xGEN | 0 | 0 | xGEN | limited | 2 | 0+0 |
| xGEN | 0 | 0 | xGEN | unlimited | 1 | 0+0 |
MultiQC reports:
- AGRF_CAGRF22029764_HJK2GDSX3 MultiQC pre-duplex
- AGRF_CAGRF22029764_HJK2GDSX3 MultiQC duplex
- AGRF_CAGRF22029764_HJK2GDSX3 MultiQC duplex without SSCs
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)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')
qualimap_cons_nossc_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/QC/consensus/qualimap_nossc')
metadata_file <- here('data/metadata/NovaSeq data E coli.xlsx')
variant_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/variants')
variant_nossc_dir <- here('data/ecoli/AGRF_CAGRF22029764_HJK2GDSX3/variants_nossc')
variant_raw_dir <- here('data/ecoli/variant_calling')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_df <- load_variants(variant_dir, sample_names)
var_df_nossc <- load_variants(variant_nossc_dir, sample_names[-9])
var_df_raw <- tmp <- load_variants(variant_raw_dir, c('xGEN-xGENRep1_default_filter', 'xGEN-xGENRep1_minimal_filter'))
# 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)
qmap_cons_cov_nossc <- get_qmap_coverage(qualimap_cons_nossc_dir, sample_names[-9])
# # uncomment below to calculate metrics
# # 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$sample <- gsub('-HJK2GDSX3', '', sample_names)
# cache metrics object
# saveRDS(metrics, file = here('data/ecoli_k12_metrics.rds'))
metrics <- readRDS(here('data/ecoli_k12_metrics.rds'))
metrics$single_family_fraction <- metrics$single_families / metrics$total_families
# 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$sample)] <- 'xGen'
mm <- inner_join(mm, metadata, by=c('sample' = 'sample name'))
colnames(mm)[2] <- 'metric'
mm$nuclease <- paste(mm$`Mung bean unit`, mm$`S1 unit`, sep='+')Metric comparison plots
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$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
geom_hline(yintercept = 0.81, alpha = 0.4) +
ggtitle(metric)
Fraction of singleton reads
Shows the number of single-read families divided by the total number of reads. As suggested by Stoler et al. 2016, this metric can server as a proxy for error rate, as (uncorrected) barcode mismatches will manifest as single-read families. The lower the fraction of singletons, the better.
metric <- 'frac_singletons'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
Single family fraction
Similar to traction of singletons, this is the number of single read families, divided by the total families.
metric <- 'single_family_fraction'
ggplot(mm[mm$metric == metric,], aes(sample, value)) +
geom_histogram(stat = 'identity', position = 'dodge') +
theme_bw() +
coord_flip() +
ggtitle(metric)
| Version | Author | Date |
|---|---|---|
| e1c0c28 | Marek Cmero | 2022-09-06 |
| 22d32ef | Marek Cmero | 2022-05-31 |
| b524238 | Marek Cmero | 2022-05-26 |
| cc380cc | Marek Cmero | 2022-05-11 |
| 7c4f403 | Marek Cmero | 2022-04-25 |
| fcb6578 | Marek Cmero | 2022-04-11 |
| a2f0a4a | Marek Cmero | 2022-04-08 |
| c246dc2 | Marek Cmero | 2022-04-07 |
| a860101 | Marek Cmero | 2022-04-06 |
| 81272b2 | Marek Cmero | 2022-04-05 |
| f13e13a | Marek Cmero | 2022-04-05 |
| def2130 | Marek Cmero | 2022-04-05 |
| 953b83e | Marek Cmero | 2022-03-31 |
| 05412f6 | Marek Cmero | 2022-03-28 |
Drop-out rate
This is the same calculation as F-EFF in the NanoSeq Analysis pipeline:
“This shows the fraction of read bundles missing one of the two original strands beyond what would be expected under random sampling (assuming a binomial process). Good values are between 0.10-0.30, and larger values are likely due to DNA damage such as modified bases or internal nicks that prevent amplification of one of the two strands. Larger values do not impact the quality of the results, just reduce the efficiency of the protocol.”
This is similar to the singleton fraction, but taking into account loss of pairs due to sampling. The optimal range is shown by the lines.
metric <- 'drop_out_rate'
ggplot(mm[mm$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)
| Version | Author | Date |
|---|---|---|
| e1c0c28 | Marek Cmero | 2022-09-06 |
| faf9130 | Marek Cmero | 2022-05-18 |
| 4da2244 | Marek Cmero | 2022-05-11 |
| cc380cc | Marek Cmero | 2022-05-11 |
| 7c4f403 | Marek Cmero | 2022-04-25 |
| fcb6578 | Marek Cmero | 2022-04-11 |
| a2f0a4a | Marek Cmero | 2022-04-08 |
| c246dc2 | Marek Cmero | 2022-04-07 |
| a860101 | Marek Cmero | 2022-04-06 |
| 81272b2 | Marek Cmero | 2022-04-05 |
| f13e13a | Marek Cmero | 2022-04-05 |
| def2130 | Marek Cmero | 2022-04-05 |
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.
- single_families: number of single-read families.
- 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.
tmp <- data.table(mm)[,list(meanval = mean(value), minval = min(value), maxval= max(value)), by=c('sample', 'metric')] %>% data.frame()
tmp <- left_join(mm, tmp, by = c('sample', 'metric'))
ggplot(tmp[tmp$metric %like% 'pair|gt1|single_families', ], aes(sample, meanval, fill = metric)) +
geom_bar(stat='identity', position='dodge') +
geom_errorbar( aes(x = sample, ymin = minval, ymax = maxval), position = 'dodge', colour = 'grey') +
theme_bw() +
coord_flip() +
scale_fill_brewer(palette = 'Dark2') +
theme(legend.position = 'right')
Compare metrics side-by-side
Compare protocols and nucleases directly, the first plot includes the outlier sample and the second removes it.
metric_optimals <- list('duplicate_rate' = 0.81,
'frac_singletons' = 0,
'drop_out_rate' = c(0.1, 0.3),
'efficiency' = 0.07,
'gc_deviation' = 0,
'duplex_coverage_ratio' = 30)
gg_prot <- list(geom_boxplot(outlier.shape = NA),
geom_jitter(width = 0.1, size = 2, aes(colour = nuclease, shape = nuclease)),
theme_bw(),
theme(legend.position = 'bottom'))
gg_nuc <- list(geom_boxplot(outlier.shape = NA),
geom_jitter(width = 0.1, size = 2, aes(colour = protocol, shape = protocol)),
theme_bw(),
theme(legend.position = 'bottom'))
mmt <- mm
mmt$replicate <- str_split(mmt$sample, 'Rep') %>% lapply(., dplyr::last) %>% unlist() %>% as.numeric()
mmt$sample <- str_split(mmt$sample, 'Rep') %>% lapply(., dplyr::first) %>% unlist()
for(metric in names(metric_optimals)) {
# plot all samples
threshold <- metric_optimals[metric][[1]]
tmp <- mmt[mmt$metric %in% metric,]
p1 <- ggplot(tmp, aes(sample, value)) +
geom_point() +
theme_bw() +
coord_flip() +
geom_hline(yintercept = threshold, alpha = 0.4) +
ggtitle(paste(metric, '(line = optimal)'))
p2 <- ggplot(tmp, aes(protocol, value)) +
gg_prot + geom_hline(yintercept = threshold, alpha = 0.4)
p3 <- ggplot(tmp, aes(nuclease, value)) +
gg_nuc + geom_hline(yintercept = threshold, alpha = 0.4)
show(p1 + p2 + p3)
# repeat with removed outlier
tmp <- mmt[mmt$metric %in% metric & !(mmt$sample %in% 'xGEN-xGEN' & mmt$replicate == 1),]
p1 <- ggplot(tmp, aes(sample, value)) +
geom_point() +
theme_bw() +
coord_flip() +
geom_hline(yintercept = threshold, alpha = 0.4) +
ggtitle(paste(metric, '(line = optimal)'))
p2 <- ggplot(tmp, aes(protocol, value)) +
gg_prot + geom_hline(yintercept = threshold, alpha = 0.4)
p3 <- ggplot(tmp, aes(nuclease, value)) +
gg_nuc + geom_hline(yintercept = threshold, alpha = 0.4)
show(p1 + p2 + p3)
}
| Version | Author | Date |
|---|---|---|
| 10ee194 | Marek Cmero | 2022-09-08 |
| f4000d4 | Marek Cmero | 2022-09-08 |
| e1c0c28 | Marek Cmero | 2022-09-06 |
| faf9130 | Marek Cmero | 2022-05-18 |
| 4da2244 | Marek Cmero | 2022-05-11 |
| cc380cc | Marek Cmero | 2022-05-11 |
| 7c4f403 | Marek Cmero | 2022-04-25 |
| fcb6578 | Marek Cmero | 2022-04-11 |
| a2f0a4a | Marek Cmero | 2022-04-08 |
| c246dc2 | Marek Cmero | 2022-04-07 |
| a860101 | Marek Cmero | 2022-04-06 |
| 81272b2 | Marek Cmero | 2022-04-05 |
| f13e13a | Marek Cmero | 2022-04-05 |
| def2130 | Marek Cmero | 2022-04-05 |
Facet summary plots
Facet boxplots by nuclease and protocol to show overall results.
ggplot(mm, aes(protocol, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free') +
ggtitle('by protocol')
ggplot(mm, aes(nuclease, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free') +
ggtitle('by nuclease')
Plots again removing the outlier xGEN rep 1.
mmo <- mm[mm$sample != 'xGEN-xGENRep1',]
mmo$replicate <- str_split(mmo$sample, 'Rep') %>% lapply(., dplyr::last) %>% unlist() %>% as.numeric()
mmo$sample <- str_split(mmo$sample, 'Rep') %>% lapply(., dplyr::first) %>% unlist()
ggplot(mmo, aes(protocol, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free') +
ggtitle('by protocol')
ggplot(mmo, aes(nuclease, value)) +
geom_boxplot() +
theme_bw() +
facet_wrap(~metric, scales = 'free') +
ggtitle('by nuclease')
Summary plot including separated by all experimental factors.
ggplot(mmo, aes(sample, value, colour = protocol, shape = nuclease)) +
geom_point() +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~metric, scales = 'free') +
scale_colour_brewer(palette = 'Dark2') +
ggtitle('by protocol')
Statistical test results by protocol
For each metric, take the average of each replicate and perform a two-sided, unpaired T-test between protocols.
stats <- NULL
metric_names <- unique(mmo$metric) %>% as.character()
for(metric_name in metric_names) {
nano <- mmo[mmo$metric == metric_name & mmo$protocol == 'NanoSeq',]
xgen <- mmo[mmo$metric == metric_name & mmo$protocol == 'xGen',]
nano_vals <- data.table(nano)[, mean(value), by = nuclease]$V1
xgen_vals <- data.table(xgen)[, mean(value), by = nuclease]$V1
wtest <- t.test(nano_vals, xgen_vals)
stats <- rbind(stats,
data.frame(metric = metric_name, pvalue = wtest$p.value))
}
stats$significant <- stats$pvalue < 0.05
print(stats) metric pvalue significant
1 frac_singletons 8.856688e-01 FALSE
2 efficiency 1.762439e-02 TRUE
3 drop_out_rate 1.080330e-02 TRUE
4 gc_single 2.282058e-04 TRUE
5 gc_both 6.467558e-05 TRUE
6 gc_deviation 3.656097e-04 TRUE
7 total_families 7.121985e-01 FALSE
8 family_mean 4.081459e-01 FALSE
9 family_median 6.178040e-01 FALSE
10 family_max 5.178585e-01 FALSE
11 families_gt1 9.146511e-01 FALSE
12 single_families 6.628176e-01 FALSE
13 paired_families 1.878931e-03 TRUE
14 paired_and_gt1 2.903205e-03 TRUE
15 duplicate_rate 4.691898e-01 FALSE
16 duplex_coverage_ratio 2.145076e-01 FALSE
17 single_family_fraction 7.668823e-01 FALSEPaired T-test (pairing nucleases, removing standard xGEN).
stats <- NULL
metric_names <- unique(mmo$metric) %>% as.character()
for(metric_name in metric_names) {
nano <- mmo[mmo$metric == metric_name & mmo$protocol == 'NanoSeq',]
xgen <- mmo[mmo$metric == metric_name & mmo$protocol == 'xGen' & mmo$nuclease != '0+0',]
nano_vals <- data.table(nano)[, mean(value), by = nuclease]$V1
xgen_vals <- data.table(xgen)[, mean(value), by = nuclease]$V1
wtest <- t.test(nano_vals, xgen_vals, paired=TRUE)
stats <- rbind(stats,
data.frame(metric = metric_name, pvalue = wtest$p.value))
}
stats$significant <- stats$pvalue < 0.05
print(stats) metric pvalue significant
1 frac_singletons 8.105516e-01 FALSE
2 efficiency 1.819141e-02 TRUE
3 drop_out_rate 4.226603e-03 TRUE
4 gc_single 1.825631e-05 TRUE
5 gc_both 3.123511e-05 TRUE
6 gc_deviation 4.471721e-04 TRUE
7 total_families 8.563265e-01 FALSE
8 family_mean 4.336622e-01 FALSE
9 family_median 4.860036e-01 FALSE
10 family_max 6.300620e-01 FALSE
11 families_gt1 9.925513e-01 FALSE
12 single_families 8.008165e-01 FALSE
13 paired_families 2.554397e-04 TRUE
14 paired_and_gt1 1.494647e-02 TRUE
15 duplicate_rate 4.454420e-01 FALSE
16 duplex_coverage_ratio 2.816812e-01 FALSE
17 single_family_fraction 9.071651e-01 FALSERerun tests removing outlier (xGEN rep1). The results are similar.
stats <- NULL
for(metric_name in metric_names) {
nano <- mmo[mmo$metric == metric_name & mmo$protocol == 'NanoSeq',]
xgen <- mmo[mmo$metric == metric_name & mmo$protocol == 'xGen',]
nano_vals <- data.table(nano)[, mean(value), by = nuclease]$V1
xgen_vals <- data.table(xgen)[, mean(value), by = nuclease]$V1
wtest <- t.test(nano_vals, xgen_vals)
stats <- rbind(stats,
data.frame(metric = metric_name, pvalue = wtest$p.value))
}
stats$significant <- stats$pvalue < 0.05
print(stats) metric pvalue significant
1 frac_singletons 8.856688e-01 FALSE
2 efficiency 1.762439e-02 TRUE
3 drop_out_rate 1.080330e-02 TRUE
4 gc_single 2.282058e-04 TRUE
5 gc_both 6.467558e-05 TRUE
6 gc_deviation 3.656097e-04 TRUE
7 total_families 7.121985e-01 FALSE
8 family_mean 4.081459e-01 FALSE
9 family_median 6.178040e-01 FALSE
10 family_max 5.178585e-01 FALSE
11 families_gt1 9.146511e-01 FALSE
12 single_families 6.628176e-01 FALSE
13 paired_families 1.878931e-03 TRUE
14 paired_and_gt1 2.903205e-03 TRUE
15 duplicate_rate 4.691898e-01 FALSE
16 duplex_coverage_ratio 2.145076e-01 FALSE
17 single_family_fraction 7.668823e-01 FALSETwo-way ANOVA analysis
We consider a two-way ANOVA, modelling the protocol, Mung Bean Unit and S1 Unit variables, as well as the interaction effect between the units and the protocol.
stats <- NULL
metric_names <- unique(mm$metric) %>% as.character()
for(metric_name in metric_names) {
x <- mm[mm$metric == metric_name,]
x$MungBeanUnit <- as.factor(x$`Mung bean unit`)
x$S1Unit <- as.factor(x$`S1 unit`)
x <- x[,c('MungBeanUnit', 'S1Unit', 'protocol', 'nuclease', 'value')]
x_aov <- aov(value ~ MungBeanUnit * protocol + S1Unit * protocol, data = x) %>% summary() %>% dplyr::first()
stats <- rbind(stats,
data.frame(metric = metric_name,
variable = rownames(x_aov)[1:5],
pvalue = x_aov[['Pr(>F)']][1:5]))
}
stats$significant <- stats$pvalue < 0.05
print(stats) metric variable pvalue significant
1 frac_singletons MungBeanUnit 3.179448e-01 FALSE
2 frac_singletons protocol 9.702279e-01 FALSE
3 frac_singletons S1Unit 8.553539e-01 FALSE
4 frac_singletons MungBeanUnit:protocol 9.858376e-01 FALSE
5 frac_singletons protocol:S1Unit 8.540793e-01 FALSE
6 efficiency MungBeanUnit 6.743589e-01 FALSE
7 efficiency protocol 3.377609e-03 TRUE
8 efficiency S1Unit 4.674623e-01 FALSE
9 efficiency MungBeanUnit:protocol 8.509683e-01 FALSE
10 efficiency protocol:S1Unit 2.278366e-01 FALSE
11 drop_out_rate MungBeanUnit 4.118682e-01 FALSE
12 drop_out_rate protocol 2.566346e-04 TRUE
13 drop_out_rate S1Unit 9.042622e-02 FALSE
14 drop_out_rate MungBeanUnit:protocol 8.387882e-01 FALSE
15 drop_out_rate protocol:S1Unit 3.182162e-01 FALSE
16 gc_single MungBeanUnit 2.845364e-03 TRUE
17 gc_single protocol 4.201084e-07 TRUE
18 gc_single S1Unit 2.691266e-02 TRUE
19 gc_single MungBeanUnit:protocol 9.742888e-01 FALSE
20 gc_single protocol:S1Unit 7.452944e-01 FALSE
21 gc_both MungBeanUnit 3.374303e-04 TRUE
22 gc_both protocol 3.194918e-09 TRUE
23 gc_both S1Unit 9.138191e-03 TRUE
24 gc_both MungBeanUnit:protocol 8.678217e-01 FALSE
25 gc_both protocol:S1Unit 5.614184e-01 FALSE
26 gc_deviation MungBeanUnit 6.443318e-01 FALSE
27 gc_deviation protocol 9.738905e-03 TRUE
28 gc_deviation S1Unit 5.442060e-01 FALSE
29 gc_deviation MungBeanUnit:protocol 9.592822e-01 FALSE
30 gc_deviation protocol:S1Unit 8.839586e-01 FALSE
31 total_families MungBeanUnit 4.304880e-01 FALSE
32 total_families protocol 8.735318e-01 FALSE
33 total_families S1Unit 8.883185e-01 FALSE
34 total_families MungBeanUnit:protocol 8.394811e-01 FALSE
35 total_families protocol:S1Unit 2.211659e-01 FALSE
36 family_mean MungBeanUnit 3.721341e-01 FALSE
37 family_mean protocol 2.541551e-01 FALSE
38 family_mean S1Unit 2.914712e-01 FALSE
39 family_mean MungBeanUnit:protocol 2.723545e-01 FALSE
40 family_mean protocol:S1Unit 1.501251e-01 FALSE
41 family_median MungBeanUnit 6.347858e-01 FALSE
42 family_median protocol 4.810155e-01 FALSE
43 family_median S1Unit 3.250056e-01 FALSE
44 family_median MungBeanUnit:protocol 4.997581e-01 FALSE
45 family_median protocol:S1Unit 3.250056e-01 FALSE
46 family_max MungBeanUnit 3.849415e-01 FALSE
47 family_max protocol 5.270992e-01 FALSE
48 family_max S1Unit 1.424842e-01 FALSE
49 family_max MungBeanUnit:protocol 9.819906e-01 FALSE
50 family_max protocol:S1Unit 6.056582e-02 FALSE
51 families_gt1 MungBeanUnit 1.793349e-01 FALSE
52 families_gt1 protocol 9.876271e-01 FALSE
53 families_gt1 S1Unit 6.881757e-01 FALSE
54 families_gt1 MungBeanUnit:protocol 6.170001e-01 FALSE
55 families_gt1 protocol:S1Unit 3.016320e-02 TRUE
56 single_families MungBeanUnit 3.440723e-01 FALSE
57 single_families protocol 9.487606e-01 FALSE
58 single_families S1Unit 8.838445e-01 FALSE
59 single_families MungBeanUnit:protocol 9.818450e-01 FALSE
60 single_families protocol:S1Unit 7.210495e-01 FALSE
61 paired_families MungBeanUnit 3.217762e-01 FALSE
62 paired_families protocol 2.319573e-04 TRUE
63 paired_families S1Unit 1.990511e-01 FALSE
64 paired_families MungBeanUnit:protocol 9.226482e-01 FALSE
65 paired_families protocol:S1Unit 8.092464e-01 FALSE
66 paired_and_gt1 MungBeanUnit 6.527043e-01 FALSE
67 paired_and_gt1 protocol 7.082361e-04 TRUE
68 paired_and_gt1 S1Unit 8.872835e-01 FALSE
69 paired_and_gt1 MungBeanUnit:protocol 5.304734e-01 FALSE
70 paired_and_gt1 protocol:S1Unit 2.688706e-01 FALSE
71 duplicate_rate MungBeanUnit 3.209744e-01 FALSE
72 duplicate_rate protocol 6.617113e-01 FALSE
73 duplicate_rate S1Unit 4.855983e-01 FALSE
74 duplicate_rate MungBeanUnit:protocol 8.160371e-01 FALSE
75 duplicate_rate protocol:S1Unit 5.516726e-01 FALSE
76 duplex_coverage_ratio MungBeanUnit 5.159691e-02 FALSE
77 duplex_coverage_ratio protocol 5.999337e-03 TRUE
78 duplex_coverage_ratio S1Unit 4.875101e-01 FALSE
79 duplex_coverage_ratio MungBeanUnit:protocol 6.215584e-02 FALSE
80 duplex_coverage_ratio protocol:S1Unit 4.129184e-03 TRUE
81 single_family_fraction MungBeanUnit 3.655666e-01 FALSE
82 single_family_fraction protocol 9.875561e-01 FALSE
83 single_family_fraction S1Unit 7.565118e-01 FALSE
84 single_family_fraction MungBeanUnit:protocol 9.917902e-01 FALSE
85 single_family_fraction protocol:S1Unit 8.631941e-01 FALSEWe remove the outlier xGEN rep 1 and test again.
stats <- NULL
metric_names <- unique(mmo$metric) %>% as.character()
for(metric_name in metric_names) {
x <- mmo[mmo$metric == metric_name,]
x$MungBeanUnit <- as.factor(x$`Mung bean unit`)
x$S1Unit <- as.factor(x$`S1 unit`)
x <- x[,c('MungBeanUnit', 'S1Unit', 'protocol', 'nuclease', 'value')]
x_aov <- aov(value ~ MungBeanUnit * protocol + S1Unit * protocol, data = x) %>% summary() %>% dplyr::first()
stats <- rbind(stats,
data.frame(metric = metric_name,
variable = rownames(x_aov)[1:5],
pvalue = x_aov[['Pr(>F)']][1:5]))
}
stats$significant <- stats$pvalue < 0.05
print(stats) metric variable pvalue significant
1 frac_singletons MungBeanUnit 3.747242e-01 FALSE
2 frac_singletons protocol 6.061218e-01 FALSE
3 frac_singletons S1Unit 2.820185e-02 TRUE
4 frac_singletons MungBeanUnit:protocol 1.145001e-01 FALSE
5 frac_singletons protocol:S1Unit 2.714409e-02 TRUE
6 efficiency MungBeanUnit 2.943575e-02 TRUE
7 efficiency protocol 8.567087e-07 TRUE
8 efficiency S1Unit 4.375525e-02 TRUE
9 efficiency MungBeanUnit:protocol 2.583454e-01 FALSE
10 efficiency protocol:S1Unit 3.175014e-03 TRUE
11 drop_out_rate MungBeanUnit 4.996962e-04 TRUE
12 drop_out_rate protocol 2.459532e-09 TRUE
13 drop_out_rate S1Unit 2.501322e-05 TRUE
14 drop_out_rate MungBeanUnit:protocol 9.115253e-02 FALSE
15 drop_out_rate protocol:S1Unit 1.679681e-03 TRUE
16 gc_single MungBeanUnit 1.711519e-03 TRUE
17 gc_single protocol 9.159550e-09 TRUE
18 gc_single S1Unit 1.523577e-03 TRUE
19 gc_single MungBeanUnit:protocol 9.253752e-01 FALSE
20 gc_single protocol:S1Unit 5.774184e-01 FALSE
21 gc_both MungBeanUnit 1.770799e-03 TRUE
22 gc_both protocol 3.727562e-09 TRUE
23 gc_both S1Unit 4.828938e-03 TRUE
24 gc_both MungBeanUnit:protocol 8.295304e-01 FALSE
25 gc_both protocol:S1Unit 5.064090e-01 FALSE
26 gc_deviation MungBeanUnit 1.765974e-01 FALSE
27 gc_deviation protocol 7.986248e-04 TRUE
28 gc_deviation S1Unit 3.553090e-01 FALSE
29 gc_deviation MungBeanUnit:protocol 9.053297e-01 FALSE
30 gc_deviation protocol:S1Unit 8.214325e-01 FALSE
31 total_families MungBeanUnit 2.929023e-01 FALSE
32 total_families protocol 5.053171e-01 FALSE
33 total_families S1Unit 5.556673e-01 FALSE
34 total_families MungBeanUnit:protocol 8.844339e-02 FALSE
35 total_families protocol:S1Unit 3.593070e-04 TRUE
36 family_mean MungBeanUnit 4.646379e-01 FALSE
37 family_mean protocol 8.429941e-02 FALSE
38 family_mean S1Unit 1.077790e-01 FALSE
39 family_mean MungBeanUnit:protocol 6.294463e-02 FALSE
40 family_mean protocol:S1Unit 3.389191e-02 TRUE
41 family_median MungBeanUnit 4.629868e-01 FALSE
42 family_median protocol 3.164774e-01 FALSE
43 family_median S1Unit 1.678507e-01 FALSE
44 family_median MungBeanUnit:protocol 2.615312e-01 FALSE
45 family_median protocol:S1Unit 1.678507e-01 FALSE
46 family_max MungBeanUnit 8.985047e-01 FALSE
47 family_max protocol 4.901973e-01 FALSE
48 family_max S1Unit 1.144701e-01 FALSE
49 family_max MungBeanUnit:protocol 9.783491e-01 FALSE
50 family_max protocol:S1Unit 4.537604e-02 TRUE
51 families_gt1 MungBeanUnit 4.921202e-01 FALSE
52 families_gt1 protocol 9.709211e-01 FALSE
53 families_gt1 S1Unit 3.554965e-01 FALSE
54 families_gt1 MungBeanUnit:protocol 1.121914e-01 FALSE
55 families_gt1 protocol:S1Unit 2.152397e-04 TRUE
56 single_families MungBeanUnit 3.561851e-01 FALSE
57 single_families protocol 5.002377e-01 FALSE
58 single_families S1Unit 1.446431e-01 FALSE
59 single_families MungBeanUnit:protocol 1.802640e-01 FALSE
60 single_families protocol:S1Unit 3.538918e-03 TRUE
61 paired_families MungBeanUnit 1.158679e-02 TRUE
62 paired_families protocol 2.650844e-08 TRUE
63 paired_families S1Unit 1.825979e-03 TRUE
64 paired_families MungBeanUnit:protocol 4.727076e-01 FALSE
65 paired_families protocol:S1Unit 4.522724e-01 FALSE
66 paired_and_gt1 MungBeanUnit 3.715875e-01 FALSE
67 paired_and_gt1 protocol 4.877137e-07 TRUE
68 paired_and_gt1 S1Unit 7.110938e-01 FALSE
69 paired_and_gt1 MungBeanUnit:protocol 4.055773e-02 TRUE
70 paired_and_gt1 protocol:S1Unit 1.317773e-02 TRUE
71 duplicate_rate MungBeanUnit 2.028567e-01 FALSE
72 duplicate_rate protocol 9.553308e-02 FALSE
73 duplicate_rate S1Unit 1.519542e-02 TRUE
74 duplicate_rate MungBeanUnit:protocol 7.348826e-02 FALSE
75 duplicate_rate protocol:S1Unit 3.151836e-02 TRUE
76 duplex_coverage_ratio MungBeanUnit 2.480884e-01 FALSE
77 duplex_coverage_ratio protocol 6.668694e-04 TRUE
78 duplex_coverage_ratio S1Unit 3.194763e-01 FALSE
79 duplex_coverage_ratio MungBeanUnit:protocol 1.030421e-02 TRUE
80 duplex_coverage_ratio protocol:S1Unit 4.306854e-04 TRUE
81 single_family_fraction MungBeanUnit 5.636520e-01 FALSE
82 single_family_fraction protocol 9.106823e-01 FALSE
83 single_family_fraction S1Unit 4.704794e-02 TRUE
84 single_family_fraction MungBeanUnit:protocol 6.635184e-01 FALSE
85 single_family_fraction protocol:S1Unit 2.340365e-01 FALSERelationships between variables
tmp <- mmo[,c('sample', 'metric', 'value', 'protocol', 'nuclease', 'replicate')]
dm <- reshape2::dcast(mmo, sample + protocol + nuclease + replicate ~ metric)
cols <- c(brewer.pal(5, 'Greens')[2:5],
brewer.pal(6, 'Blues')[2:6])
names(cols) <- as.factor(dm$sample) %>% levels()
ggplot(dm, aes(frac_singletons, drop_out_rate, colour=sample)) +
geom_point() +
theme_bw() +
scale_colour_manual(values = cols) +
ggtitle('Singletons vs. drop-out rate')
ggplot(dm, aes(efficiency, duplicate_rate, colour=sample)) +
geom_point() +
theme_bw() +
scale_colour_manual(values = cols) +
ggtitle('Efficiency vs. duplicate rate')
ggplot(dm, aes(efficiency, drop_out_rate, colour=sample)) +
geom_point() +
theme_bw() +
scale_colour_manual(values = cols) +
ggtitle('Efficiency vs. drop-out rate')
ggplot(dm, aes(efficiency, duplex_coverage_ratio, colour=sample)) +
geom_point() +
theme_bw() +
scale_colour_manual(values = cols) +
ggtitle('Efficiency vs. duplex coverage ratio')
ggplot(dm, aes(duplicate_rate, duplex_coverage_ratio, colour=sample)) +
geom_point() +
theme_bw() +
scale_colour_manual(values = cols) +
ggtitle('Duplicate rate vs. duplex coverage ratio')
Focus on relationship between efficiency, duplicate rate and drop-out rate.
mt <- mm
mt$replicate <- str_split(mt$sample, 'Rep') %>% lapply(., dplyr::last) %>% unlist() %>% as.numeric()
mt$sample <- str_split(mt$sample, 'Rep') %>% lapply(., dplyr::first) %>% unlist()
mt <- mt[,c('sample', 'metric', 'value', 'protocol', 'nuclease', 'replicate')]
dm <- reshape2::dcast(mt, sample + protocol + nuclease + replicate ~ metric)
p1 <- ggplot(dm, aes(duplicate_rate, efficiency, colour=protocol, shape=nuclease)) +
geom_point(size = 3) +
theme_bw() +
scale_colour_brewer(palette = 'Dark2') +
ggtitle('Efficiency vs. duplicate rate')
p2 <- ggplot(dm, aes(drop_out_rate, efficiency, colour=protocol, shape=nuclease)) +
geom_point(size = 3) +
theme_bw() +
scale_colour_brewer(palette = 'Dark2') +
ggtitle('Efficiency vs. drop-out rate')
show(p1 + p2)
| Version | Author | Date |
|---|---|---|
| f4000d4 | Marek Cmero | 2022-09-08 |
Variant calls
Upset plot showing duplex variant calls. Variants were called in areas with at least 4x coverage with at least 2 supporting reads and a VAF of \(\geq2\).
ulist <- NULL
for(sample in sample_names) {
ids <- var_df[var_df$sample %in% sample,]$id
if (length(ids) > 0) {
ulist[[gsub(pattern = '-HJK2GDSX3', replacement = '', sample)]] <- ids
}
}
upset(fromList(ulist), order.by='freq', nsets=length(sample_names))
Raw variant calls
In the xGEN-xGEN samples, we compare the unrestricted PCR sample variants (most representative of a typical NGS experiment), called using raw reads (not duplex consensus), compared with the xGEN-xGEN samples with standard-end repair that were PCR restricted.
tmp <- rbind(var_df, var_df_raw)
ulist <- NULL
for(sample in c('xGEN-xGENRep2-HJK2GDSX3', 'xGEN-xGENRep3-HJK2GDSX3', 'xGEN-xGENRep1_default_filter', 'xGEN-xGENRep1_minimal_filter')) {
ids <- tmp[tmp$sample %in% sample,]$id
if (length(ids) > 0) {
ulist[[gsub(pattern = '-HJK2GDSX3', replacement = '', sample)]] <- ids
}
}
upset(fromList(ulist), order.by='freq', nsets=length(sample_names))
| Version | Author | Date |
|---|---|---|
| 55d7990 | Marek Cmero | 2022-09-13 |
Duplex coverage without requiring SSC
The pipeline was run only requiring a single read on each strand. Here we plot the difference in mean coverage. As we would expect, skipping SSC step increases duplex coverage. For some samples with disproportionately higher single-read families (NanoMB-S1), this increases duplex coverage significantly more.
ccov <- inner_join(qmap_cons_cov,
qmap_cons_cov_nossc,
by = 'Sample',
suffix = c('_ssc', '_nossc')) %>%
inner_join(., qmap_cov, by = 'Sample')
ccov$sample <- str_split(ccov$Sample, 'Rep') %>% lapply(., dplyr::first) %>% unlist()
ccov$duplex_cov_ratio <- ccov$coverage / ccov$coverage_ssc
ccov$duplex_cov_ratio_noscc <- ccov$coverage / ccov$coverage_nossc
ccov <- left_join(ccov, distinct(mmo[,c('sample', 'protocol', 'nuclease')]), by = 'sample')
p1 <- ggplot(ccov, aes(coverage_ssc, coverage_nossc, colour = protocol, shape = nuclease)) +
geom_point() +
theme_bw() +
xlim(0, 550) +
ylim(0, 550) +
xlab('with SSC') +
ylab('without SSC') +
geom_abline(slope = 1) +
theme(legend.position = 'left') +
scale_colour_brewer(palette = 'Dark2') +
ggtitle('Mean duplex coverage')
p2 <- ggplot(ccov, aes(duplex_cov_ratio, duplex_cov_ratio_noscc, colour = protocol, shape = nuclease)) +
geom_point() +
theme_bw() +
xlim(0, 100) +
ylim(0, 100) +
xlab('with SSC') +
ylab('without SSC') +
geom_abline(slope = 1) +
theme(legend.position = 'right') +
scale_colour_brewer(palette = 'Dark2') +
ggtitle('Duplex coverage ratio')
p1 + p2
Variant calls without SSC
Here we show the variant calls from the duplex sequences without SSC in the same Upset plot format.
for(sample in sample_names) {
ids <- var_df_nossc[var_df_nossc$sample %in% sample,]$id
if (length(ids) > 0) {
ulist[[sample]] <- ids
}
}
upset(fromList(ulist), order.by='freq', nsets=length(sample_names))
Input cells
Estimate the number of input cells using formula \(d / e / c = n\) where d = mean duplex coverage, e = duplex efficiency, c = coverage per genome equivalent and n = number of cells.
coverage_per_genome <- 10
qmap_cons_cov$Sample <- gsub('-HJK2GDSX3', '', qmap_cons_cov$Sample)
metrics <- inner_join(metrics, qmap_cons_cov, by = c('sample' = 'Sample'))
metrics$estimated_cells <- metrics$coverage / metrics$efficiency / coverage_per_genome
ggplot(metrics[!metrics$sample %in% 'xGEN-xGENRep1',], aes(sample, estimated_cells)) +
geom_bar(stat = 'identity') +
theme_minimal() +
coord_flip()
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] highr_0.9 fastmap_1.1.0 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 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