Ravel_2011_16S_BV - Bacterial vaginosis - whole dataset
Source:vignettes/articles/Ravel_2011_16S_BV_whole.Rmd
Ravel_2011_16S_BV_whole.Rmd
library(MicrobiomeBenchmarkDataAnalyses)
library(MicrobiomeBenchmarkData)
library(mia)
library(phyloseq)
library(dplyr)
library(benchdamic)
library(purrr)
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(tidySummarizedExperiment)Data
dat_name <- 'Ravel_2011_16S_BV'
conditions_col <- 'study_condition'
conditions <- c(condB = 'healthy', condA = 'bacterial_vaginosis')
tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
col_data <- tse |>
colData() |>
as.data.frame() |>
dplyr::filter(study_condition %in% conditions)
col_data |>
summarise(
.by = c(
"ethnicity", "nugent_score_category", "study_condition"
),
range = paste0(min(nugent_score), "-", max(nugent_score)),
n = n()
) |>
arrange(ethnicity, study_condition, n)## ethnicity nugent_score_category study_condition range n
## 1 Asian high bacterial_vaginosis 7-9 13
## 2 Asian low healthy 0-3 69
## 3 Black high bacterial_vaginosis 7-10 42
## 4 Black low healthy 0-3 52
## 5 Hispanic high bacterial_vaginosis 7-10 32
## 6 Hispanic low healthy 0-3 50
## 7 White high bacterial_vaginosis 7-10 10
## 8 White low healthy 0-3 77
select_samples <- col_data |>
{\(y) split(y, factor(y$study_condition))}() |>
map(rownames) |>
flatten_chr()
tse_subset <- tse[, select_samples]
tse_genus <- agglomerateByRank(
tse_subset, rank = 'genus', na.rm = FALSE, onRankOnly = FALSE
) |>
filterTaxa(min_ab = 1, min_per = 0.2) |>
{\(y) magrittr::set_rownames(y, editMiaTaxaNames(y))}()
colData(tse_genus)$study_condition <-
factor(colData(tse_genus)$study_condition, levels = conditions)
tse_genus## class: TreeSummarizedExperiment
## dim: 30 345
## metadata(1): agglomerated_by_rank
## assays(1): counts
## rownames(30): genus:Mobiluncus genus:Gardnerella ... genus:Parvimonas
## genus:Peptoniphilus
## rowData names(7): kingdom class ... species taxon_annotation
## colnames(345): S008 S012 ... S393 S394
## colData names(17): dataset gender ... nugent_score_category
## community_group
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## rowLinks: NULL
## rowTree: NULL
## colLinks: NULL
## colTree: NULL
tse_genus |>
colData() |>
as_tibble() |>
summarise(
.by = c(
# "ethnicity", "nugent_score_category", "study_condition"
"nugent_score_category", "study_condition"
),
range = paste0(min(nugent_score), "-", max(nugent_score)),
n = n()
) |>
arrange(study_condition, n) |>
relocate(study_condition)## # A tibble: 2 × 4
## study_condition nugent_score_category range n
## <fct> <chr> <chr> <int>
## 1 healthy low 0-3 248
## 2 bacterial_vaginosis high 7-10 97Prior info (biological annotations)
prior_info <- tse_genus |>
rowData() |>
as.data.frame() |>
dplyr::select(genus, taxon_annotation) |>
rename(taxon_name = genus) |>
mutate(
taxon_annotation = case_when(
is.na(taxon_annotation) ~ "Unannotated",
TRUE ~ taxon_annotation
)
)
head(prior_info)## taxon_name taxon_annotation
## genus:Mobiluncus Mobiluncus bv-associated
## genus:Gardnerella Gardnerella bv-associated
## genus:Corynebacterium Corynebacterium bv-associated
## genus:Gemella Gemella bv-associated
## genus:Staphylococcus Staphylococcus bv-associated
## genus:Aerococcus Aerococcus bv-associatedDA analysis
Convert to phyloseq:
ps <- convertToPhyloseq(tse_genus)
sample_data(ps)[[conditions_col]] <- factor(
sample_data(ps)[[conditions_col]], levels = conditions
)Set method parameters:
norm_methods <- set_norm_list()
ps <- runNormalizations(norm_methods, ps, verbose = FALSE)
zw <- weights_ZINB(ps, design = conditions_col)
DA_methods <- set_DA_methods_list(conditions_col, conditions)
for (i in seq_along(DA_methods)) {
if (grepl("Seurat", names(DA_methods)[i])) {
names(DA_methods[[i]]$contrast) <- NULL
} else {
next
}
}
names(DA_methods)## [1] "DA_edgeR.1" "DA_edgeR.1" "DA_DESeq2.1"
## [4] "DA_DESeq2.1" "DA_limma.1" "DA_limma.1"
## [7] "DA_metagenomeSeq.1" "DA_ALDEx2.1" "DA_MAST.1"
## [10] "DA_Seurat.1" "ancombc.1" "wilcox.3"
## [13] "wilcox.4" "ZINQ.9" "ZINQ.10"
## [16] "lefse.12" "lefse.13"Run DA analysis:
tim <- system.time({
DA_output <- vector("list", length(DA_methods))
for (i in seq_along(DA_output)) {
message(
"Running method ", i, ": ", names(DA_methods)[i], " - ", Sys.time()
)
DA_output[[i]] <- tryCatch(
error = function(e) NULL,
runDA(DA_methods[i], ps, weights = zw, verbose = FALSE)
)
}
DA_output <- purrr::list_flatten(DA_output, name_spec = "{inner}")
DA_output <- purrr::discard(DA_output, is.null)
})
tim## user system elapsed
## 12.414 0.935 12.404Enrichment
We need a threshold for DA for lefse-CLR (It can’t be the same as when using lefse-TSS):
c(
lefse.TSS = median(DA_output$lefse.TSS$statInfo$abs_score),
lefse.CLR = median(DA_output$lefse.CLR$statInfo$abs_score)
)## lefse.TSS lefse.CLR
## 3.8541431 0.1815828Create some variables for selecting and ranking differentially abundant features:
direction <- get_direction_cols(DA_output, conditions_col, conditions)
## The methods based on lefse have artificial p-values because
## the lefser output doesn't provide such information
adjThr<- rep(0.1, length(DA_output))
names(adjThr) <- names(DA_output)
esThr <- case_when(
grepl("lefse.TSS", names(DA_output)) ~ 2,
grepl("lefse.CLR", names(DA_output)) ~ 0.15,
TRUE ~ 0
) |>
set_names(names(DA_output))
## Use effect size for lefser and adjusted p-value for all of the other methods
slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat")
colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP")
typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")Create enrichment:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "taxon_name",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Create enrichment summary:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$summaries |>
purrr::map(function(x) {
x |>
tibble::rownames_to_column(var = "direction") |>
tidyr::pivot_longer(
names_to = "annotation", values_to = "n",
cols = 2
)
}) |>
dplyr::bind_rows() |>
dplyr::relocate(pvalue)
}) |>
dplyr::bind_rows(.id = "method") |>
dplyr::mutate(
sig = dplyr::case_when(
pvalue < 0.05 & pvalue > 0.01 ~ "*",
pvalue < 0.01 & pvalue > 0.001 ~ "**",
pvalue < 0.001 ~ "***",
TRUE ~ ""
)
) |>
dplyr::mutate(
direction = dplyr::case_when(
direction == "DOWN Abundant" ~ "HV",
direction == "UP Abundant" ~ "BV",
TRUE ~ direction
)
)Create enrichment plot:
enPlot <- enrichmentSummary |>
left_join(get_meth_class(), by = "method") |>
mutate(
direction = factor(
direction, levels = c("BV", "HV")
)
) |>
filter(annotation != "Unannotated") |>
mutate(
method = case_when(
grepl("lefse", method) ~ sub("lefse", "LEfSe", method),
grepl("wilcox", method) ~ sub("wilcox", "Wilcox", method),
TRUE ~ method
)
) |>
mutate(
annotation = case_when(
annotation == "bv-associated" ~ "BV-associated",
annotation == "hv-associated" ~ "HV-associated"
)
) |>
ggplot(aes(method, n)) +
geom_col(
aes(fill = annotation),
position = position_dodge2(width = 0.9)
) +
geom_text(
aes(label = sig, color = annotation),
position = position_dodge2(width = 0.9)
) +
facet_grid(
direction ~ method_class, scales = "free_x", space = "free"
) +
scale_fill_viridis_d(option = "D", name = "Biological data") +
scale_color_viridis_d(option = "D", name = "Biological data") +
labs(
x = "DA method", y = "Number of DA taxa"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom"
)Plot putative true positves and true negatives ratio
Create ‘positives’ object. No thresholds were added.
positives <- map(1:length(DA_output), .f = function(i) {
positives <- createPositives(
object = DA_output[i],
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation", namesCol = "taxon_name",
slot = slotV[i], colName = colNameV[i], type = typeV[i],
direction = direction[i],
threshold_pvalue = 1,
threshold_logfc = 0,
top = seq.int(from = 2, to = 20, by = 2),
alternative = "greater",
verbose = FALSE,
TP = list(c("DOWN Abundant", "hv-associated"), c("UP Abundant", "bv-associated")),
FP = list(c("DOWN Abundant", "bv-associated"), c("UP Abundant", "hv-associated"))
) |>
left_join(get_meth_class(), by = 'method')
}) |>
bind_rows() |>
mutate(
base_method = case_when(
grepl("lefse", base_method) ~ sub("lefse", "LEfSe", base_method),
grepl("wilcox", base_method) ~ sub("wilcox", "Wilcox", base_method),
TRUE ~ base_method
),
method = case_when(
grepl("lefse", method) ~ sub("lefse", "LEfSe", method),
grepl("wilcox", method) ~ sub("wilcox", "Wilcox", method),
TRUE ~ method
)
)Create “positives” plot:
vec <- positives$color
names(vec) <- positives$base_method
posPlot <- positives |>
mutate(diff = TP - FP) |>
ggplot(aes(top, diff)) +
geom_line(
aes(
group = method, color = base_method, linetype = norm,
),
) +
geom_point(
aes(
color = base_method, shape = norm
),
) +
facet_wrap(~method_class, nrow = 1) +
labs(
x = "Top DA features", y = "TP - FP"
) +
scale_shape(name = "Normalization") +
scale_linetype(name = "Normalization") +
scale_color_manual(values = vec, name = "Base method") +
theme_minimal() +
theme(legend.position = "bottom")Combine enrichment and “positives” plot:
Perform DA with lefse and Wilcox
tssFun <- function(x) {
(x) / sum(x) * 1e6
}
clrFun <- function(x) {
log(x / exp(mean(log(x))))
}
assay(tse_genus, "TSS") <- apply(assay(tse_genus, "counts"), 2, tssFun)
## PseudoCount only needed for CLR
assay(tse_genus, "CLR") <- apply(assay(tse_genus, "counts") + 1, 2, clrFun)
data <- tse_genus |>
as_tibble() |>
rename(taxon_name = .feature, sample = .sample) |>
mutate(
taxon_annotation = ifelse(
is.na(taxon_annotation), 'Unannotated', taxon_annotation
)
)
head(data)## # A tibble: 6 × 29
## taxon_name sample counts TSS CLR dataset gender body_site ncbi_accession
## <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 genus:Mob… S008 24 13423. 0.905 Ravel_… female vagina SRR062677
## 2 genus:Gar… S008 1 559. -1.62 Ravel_… female vagina SRR062677
## 3 genus:Cor… S008 0 0 -2.31 Ravel_… female vagina SRR062677
## 4 genus:Gem… S008 7 3915. -0.234 Ravel_… female vagina SRR062677
## 5 genus:Sta… S008 0 0 -2.31 Ravel_… female vagina SRR062677
## 6 genus:Aer… S008 0 0 -2.31 Ravel_… female vagina SRR062677
## # ℹ 20 more variables: library_size <dbl>, sequencing_platform <chr>,
## # pmid <dbl>, study_condition <fct>, sequencing_method <chr>,
## # variable_region_16s <chr>, country <chr>, number_bases <dbl>,
## # ethnicity <chr>, ph <dbl>, nugent_score <dbl>, nugent_score_category <chr>,
## # community_group <chr>, kingdom <chr>, class <chr>, order <chr>,
## # family <chr>, genus <chr>, species <chr>, taxon_annotation <chr>Wilcox
Define function:
calcWilcox <- function(dat, val_col, log = FALSE) {
## Separate components
taxa <- split(dat, factor(dat$taxon_name))
taxa_names <- names(taxa)
taxa_annotations <- data |>
dplyr::select(tidyselect::starts_with('taxon')) |>
dplyr::distinct()
## Perform Wilcoxon test
pvalues <- vector('double', length(taxa))
names(pvalues) <- taxa_names
formula_chr <- paste0(val_col, ' ~ study_condition')
for (i in seq_along(pvalues)) {
df <- taxa[[i]]
res <- stats::wilcox.test(formula = as.formula(formula_chr), data = df)
pvalues[[i]] <- res$p.value
}
## Adjust P-values
adj_pvalues <- stats::p.adjust(pvalues, method = 'fdr')
## Calculate fold change
log_fold_change <- vector('double', length(taxa))
lll <- vector('double', length(taxa))
for (i in seq_along(log_fold_change)) {
df <- taxa[[i]]
healthy <- df |>
dplyr::filter(study_condition == 'healthy') |>
{\(y) y[[val_col]]}()
bv <- df |>
dplyr::filter(study_condition == 'bacterial_vaginosis') |>
{\(y) y[[val_col]]}()
bv <- mean(bv)
healthy <- mean(healthy)
if (log) {
log_fold_change[i] <- bv - healthy
} else {
if (bv >= healthy) { # control is healthy, condition of interest is bv
log_fold_change[i] <- log2(bv / healthy)
} else if (bv < healthy) {
log_fold_change[i] <- -log2(healthy / bv)
}
}
}
## Combine results and annotations
pval_results <- data.frame(
taxon_name = taxa_names,
rawP = pvalues,
adjP = adj_pvalues,
logFC = log_fold_change
)
dplyr::left_join(pval_results, taxa_annotations, by = 'taxon_name')
}Perform statistical test:
wilcoxRes <- list(
wilcox_counts = calcWilcox(data, 'counts'),
wilcox_relab = calcWilcox(data, 'TSS'),
wilcox_clr = calcWilcox(data, 'CLR', log = TRUE)
) |>
bind_rows(.id = 'method')Filter DA taxa
wilcox_DA <- wilcoxRes |>
dplyr::filter(adjP <= 0.1, abs(logFC) > 0) |>
mutate(DA = ifelse(logFC > 0, "OA", "UA"))Plot
wilcox_DA |>
dplyr::filter(taxon_annotation != 'Unannotated') |>
count(method, taxon_annotation, DA) |>
# mutate(n = ifelse(DA == 'UA', -n, n)) |>
mutate(
DA = case_when(
DA == "UA" ~ "HV",
DA == "OA" ~ "BV"
)
) |>
tidyr::complete(DA, method, taxon_annotation, fill = list(n = 0)) |>
mutate(method = sub('wilcox_', '', method)) |>
ggplot(aes(method, n)) +
geom_col(aes(fill = taxon_annotation), position = 'dodge') +
geom_hline(yintercept = 0) +
scale_y_continuous(breaks = \(x) pretty(x)) +
facet_wrap(. ~ DA) +
labs(
title = 'Wilcoxon test',
y = 'Number of DA taxa', x = 'Transformation method'
) +
theme_minimal()
Plot the abundances of the taxa that were incorrect
incorrect_taxa_wilcox_clr <- wilcox_DA |>
dplyr::filter(
method == 'wilcox_clr', DA == 'UA',
taxon_annotation == 'bv-associated'
) |>
pull(taxon_name)
incorrect_taxa_wilcox_clr## [1] "genus:Corynebacterium" "genus:Gemella"
## [3] "genus:Mobiluncus" "genus:Peptostreptococcus"
## [5] "genus:Staphylococcus" "genus:Streptococcus"Let’s plot their values for each matrix
transformations <- c('counts', 'TSS', 'CLR')
l1 <- vector('list', length(transformations))
names(l1) <- transformations
for (i in seq_along(transformations)) {
mat <- assay(tse_genus, transformations[i])
l1[[i]] <- mat[incorrect_taxa_wilcox_clr,] |>
as.data.frame() |>
tibble::rownames_to_column(var = 'taxon_name') |>
as_tibble()
}
wilcox_raw <- bind_rows(l1, .id = 'transformation') |>
{\(y) pivot_longer(
y, cols = 3:ncol(y), values_to = 'value', names_to = 'sample'
)}() |>
left_join(
distinct(data[,c('sample', 'study_condition')]), by = 'sample'
)
head(wilcox_raw)## # A tibble: 6 × 5
## transformation taxon_name sample value study_condition
## <chr> <chr> <chr> <dbl> <fct>
## 1 counts genus:Corynebacterium S008 0 bacterial_vaginosis
## 2 counts genus:Corynebacterium S012 0 bacterial_vaginosis
## 3 counts genus:Corynebacterium S023 4 bacterial_vaginosis
## 4 counts genus:Corynebacterium S025 0 bacterial_vaginosis
## 5 counts genus:Corynebacterium S030 0 bacterial_vaginosis
## 6 counts genus:Corynebacterium S031 0 bacterial_vaginosisBox plot of incorrect values:
l <- wilcox_raw |>
mutate(taxon_name = sub('genus:', '', taxon_name)) |>
{\(y) split(y, y$transformation)}()
l$counts$value <- log(l$counts$value + 1)
l$TSS$value <- log(l$TSS$value + 1)
## CLR is already in log scale
wilcox_raw <- reduce(l, bind_rows)
wilcox_genus_plot <- wilcox_raw |>
mutate(transformation = factor(
transformation, levels = c('counts', 'TSS', 'CLR'),
labels = c('log(counts + 1)', 'log(TSS + 1)', 'CLR')
)) |>
mutate(study_condition = factor(
study_condition, levels = c('healthy', 'bacterial_vaginosis'),
labels = c('HV', 'BV')
)) |>
ggplot(aes(taxon_name, value)) +
geom_boxplot(aes(color = study_condition), position = position_dodge2(0.9)) +
# geom_point(
# aes(color = study_condition), position = position_dodge2(0.9),
# size = 0.1
# ) +
facet_wrap(~ transformation, scales = 'free') +
labs(
y = 'Abundance values', x = 'Genus'
) +
# scale_color_manual(
# values = c('dodgerblue1', 'firebrick1')
# ) +
theme_bw() +
theme(
panel.grid.major.x = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, face = "italic")
)
wilcox_genus_plot
Lefse
Define a function for running Lefse:
calcLefse <- function(se, assay) {
res <- lefser::lefser(
se, kruskal.threshold = 0.05, wilcox.threshold = 0.05,
lda.threshold = 0.15, classCol = 'study_condition', assay = assay
)
return(res)
}Run lefse
taxa_annotations <-
dplyr::distinct(dplyr::select(data, dplyr::starts_with('taxon')))
lefse_DA <- list(
lefse_counts = calcLefse(tse_genus, 'counts'),
lefse_relab = calcLefse(tse_genus, 'TSS'),
lefse_clr = calcLefse(tse_genus, 'CLR')
) |>
bind_rows(.id = 'method') |>
mutate(
DA = ifelse(scores > 0.15, 'OA', 'UA')
) |>
rename(taxon_name = 'features') |>
left_join(taxa_annotations, by = 'taxon_name')## The outcome variable is specified as 'study_condition' and the reference category is 'healthy'.
## See `?factor` or `?relevel` to change the reference category.
## The outcome variable is specified as 'study_condition' and the reference category is 'healthy'.
## See `?factor` or `?relevel` to change the reference category.
## The outcome variable is specified as 'study_condition' and the reference category is 'healthy'.
## See `?factor` or `?relevel` to change the reference category.
head(lefse_DA)## method taxon_name scores DA taxon_annotation
## 1 lefse_counts genus:Lactobacillus -2.9235121 UA hv-associated
## 2 lefse_counts order:Lactobacillales -0.6252234 UA Unannotated
## 3 lefse_counts genus:Limosilactobacillus -0.5824322 UA Unannotated
## 4 lefse_counts family:Coriobacteriaceae 0.2447420 OA Unannotated
## 5 lefse_counts genus:Anaeroglobus 0.2695696 OA Unannotated
## 6 lefse_counts genus:Finegoldia 0.6394299 OA UnannotatedPlot lefse results:
lefse_DA |>
dplyr::filter(taxon_annotation != 'Unannotated') |>
count(method, taxon_annotation, DA) |>
# mutate(n = ifelse(DA == 'UA', -n, n)) |>
mutate(
DA = case_when(
DA == "UA" ~ "HV",
DA == "OA" ~ "BV"
)
) |>
mutate(method = sub('lefse_', '', method)) |>
tidyr::complete(method, taxon_annotation, DA, fill = list(n = 0)) |>
ggplot(aes(method, n)) +
geom_col(aes(fill = taxon_annotation), position = 'dodge') +
geom_hline(yintercept = 0) +
facet_wrap(. ~ DA) +
scale_y_continuous(breaks = pretty) +
labs(
title = 'LEfSe test',
y = 'Number of DA taxa', x = 'Transformation/Normalization method'
) +
theme_minimal()
incorrect_taxa_lefse_clr <- lefse_DA |>
dplyr::filter(
method %in% c('lefse_clr', 'lefse_relab_clr'), DA == 'UA',
taxon_annotation == 'bv-associated'
) |>
pull(taxon_name) |>
unique()
incorrect_taxa_lefse_clr ## the same as in wilcox.## [1] "genus:Staphylococcus" "genus:Streptococcus" "genus:Corynebacterium"Compositions with TSS and CLR
Plot TSS:
first_set <- data |>
filter(
nugent_score_category == 'low',
# taxon_annotation == 'hv-associated'
taxon_name == 'genus:Lactobacillus'
) |>
arrange(desc(TSS)) |>
pull(sample)
second_set <- data |>
filter(
nugent_score_category == 'high',
# taxon_annotation == 'hv-associated'
taxon_name == 'genus:Lactobacillus'
) |>
arrange(desc(TSS)) |>
pull(sample)
samples_order <- c(first_set, second_set)
p1 <- data |>
mutate(
sample = factor(sample, levels = samples_order),
nugent_score_category = factor(
nugent_score_category, levels = c('low', 'high'),
labels = c('Low Nugent score', 'High Nugent score')
),
taxon_annotation = case_when(
taxon_annotation == "hv-associated" ~ "Health-associated",
taxon_annotation == "bv-associated" ~ "BV-associated",
TRUE ~ taxon_annotation
),
taxon_annotation = factor(
taxon_annotation, levels = c('Health-associated', 'BV-associated', 'Unannotated')[3:1]
)
) |>
ggplot(aes(sample, TSS )) +
geom_col(aes(fill = taxon_annotation), width = 1) +
scale_fill_manual(values = c('gray60', 'firebrick2', 'dodgerblue2')) +
labs(
x = "Samples",
y = "Relative abundance values (TSS)",
title = "Relative abundance",
subtitle = "Samples are ordered according Lactobacillus relab."
) +
facet_wrap(~nugent_score_category, ncol = 2, scales = "free_x") +
theme_bw() +
theme(
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
p1
Plot CLR:
p2 <- data |>
mutate(
sample = factor(sample, levels = samples_order),
nugent_score_category = factor(
nugent_score_category, levels = c('low', 'high'),
labels = c('Low Nugent score', 'High Nugent score')
),
taxon_annotation = case_when(
taxon_annotation == "hv-associated" ~ "Health-associated",
taxon_annotation == "bv-associated" ~ "BV-associated",
TRUE ~ taxon_annotation
),
taxon_annotation = factor(
taxon_annotation, levels = c('Health-associated', 'BV-associated', 'Unannotated')[3:1]
)
) |>
ggplot(aes(sample, exp(CLR))) +
geom_col(aes(fill = taxon_annotation), width = 1) +
scale_fill_manual(values = c('gray60', 'firebrick2', 'dodgerblue2')) +
labs(
x = "Samples",
y = "Geometric mean normalization (exp(CLR))",
title = "Relative abundance",
subtitle = "Samples are ordered according Lactobacillus relab."
) +
facet_wrap(~nugent_score_category, ncol = 2, scales = "free_x") +
theme_bw() +
theme(
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
p2
Distributions
Get Latobacillus relative abundance per sample
sample_sizes <- filter(data, taxon_name == 'genus:Lactobacillus') |>
select(sample, lact_tss = TSS, lact_clr = CLR)
data_with_lact <- left_join(data, sample_sizes, by = 'sample')Plotting log(CLR) vs log(Relab) of Lactobacillus, Prevotella, Actinomyces, and Corynebacterium.
plot_1b <- data_with_lact |>
filter(taxon_name == 'genus:Streptococcus') |>
mutate(
study_condition = factor(
study_condition, levels = c('bacterial_vaginosis', 'healthy'),
labels = c('BV', 'HV')
)
) |>
ggplot(aes(log(TSS + 1), CLR)) +
geom_point(
aes(color = study_condition, size = lact_tss),
alpha = 0.3
) +
labs(
# title = 'Relative abundace vs CLR',
title = expression(italic('Streptococcus') ~ '(BV-associated)'),
x = 'log(TSS + 1)'
) +
scale_color_discrete(name = 'Condition') +
# scale_size(name = 'Lactobacillus Rel. Ab.') +
scale_size(name = expression(italic('Lactobacillus') ~ 'Rel. Ab.')) +
theme_bw()
plot_2b <- data_with_lact |>
filter(taxon_name == 'genus:Corynebacterium') |>
mutate(
study_condition = factor(
study_condition, levels = c('bacterial_vaginosis', 'healthy'),
labels = c('BV', 'HV')
)
) |>
ggplot(aes(log(TSS + 1), CLR)) +
geom_point(
aes(color = study_condition, size = lact_tss),
alpha = 0.3
) +
labs(
# title = 'Relative abundace vs CLR',
title = expression(italic('Corynebacterium') ~ '(BV-associated)'),
x = 'log(TSS + 1)'
) +
scale_color_discrete(name = 'Condition') +
# scale_size(name = 'Lactobacillus Rel. Ab.') +
scale_size(name = expression(italic('Lactobacillus') ~ 'Rel. Ab.')) +
theme_bw()
plot_3b <- data_with_lact |>
filter(taxon_name == 'genus:Prevotella') |>
mutate(
study_condition = factor(
study_condition, levels = c('bacterial_vaginosis', 'healthy'),
labels = c('BV', 'HV')
)
) |>
ggplot(aes(log(TSS + 1), CLR)) +
geom_point(
aes(color = study_condition, size = lact_tss),
alpha = 0.3
) +
labs(
# title = 'Relative abundace vs CLR',
title = expression(italic("Prevotella") ~ "(BV-associated)"),
x = 'log(TSS + 1)'
) +
scale_color_discrete(name = 'Condition') +
# scale_size(name = 'Lactobacillus Rel. Ab.') +
scale_size(name = expression(italic('Lactobacillus') ~ 'Rel. Ab.')) +
theme_bw()
plot_4b <- data_with_lact |>
filter(taxon_name == 'genus:Lactobacillus') |>
mutate(
study_condition = factor(
study_condition, levels = c('bacterial_vaginosis', 'healthy'),
labels = c('BV', 'HV')
)
) |>
ggplot(aes(log(TSS + 1), CLR)) +
geom_point(
aes(color = study_condition, size = lact_tss),
alpha = 0.3
) +
labs(
# title = 'Relative abundace vs CLR',
# title = 'Lactobacillus (HV-associated)',
title = expression(italic("Lactobacillus") ~ "(HV-associated)"),
x = 'log(TSS + 1)'
) +
scale_color_discrete(name = 'Condition') +
# scale_size(name = 'Lactobacillus Rel. Ab.') +
scale_size(name = expression(italic('Lactobacillus') ~ 'Rel. Ab.')) +
theme_bw()
plotsb <- ggpubr::ggarrange(
plot_4b, plot_3b, plot_1b, plot_2b, align = 'hv',
common.legend = TRUE, legend = 'bottom',
labels = c('a)', 'b)', 'c)', 'd)')
)
plotsb
Session info
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.2 (2024-10-31)
## os Ubuntu 24.04.1 LTS
## system x86_64, linux-gnu
## ui X11
## language en
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2025-01-11
## pandoc 3.6 @ /usr/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
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## AnnotationDbi 1.68.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
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## pan 1.9 2023-12-07 [1] RSPM (R 4.4.0)
## parallelly 1.41.0 2024-12-18 [1] RSPM (R 4.4.0)
## patchwork 1.3.0 2024-09-16 [1] RSPM (R 4.4.0)
## pbapply 1.7-2 2023-06-27 [1] RSPM (R 4.4.0)
## pcaPP 2.0-5 2024-08-19 [1] RSPM (R 4.4.0)
## permute 0.9-7 2022-01-27 [1] RSPM (R 4.4.0)
## phyloseq * 1.50.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## pillar 1.10.1 2025-01-07 [1] RSPM (R 4.4.0)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.4.0)
## pkgdown 2.1.1 2024-09-17 [1] RSPM (R 4.4.0)
## plotly 4.10.4 2024-01-13 [1] RSPM (R 4.4.0)
## plyr 1.8.9 2023-10-02 [1] RSPM (R 4.4.0)
## png 0.1-8 2022-11-29 [1] RSPM (R 4.4.0)
## polyclip 1.10-7 2024-07-23 [1] RSPM (R 4.4.0)
## prettyunits 1.2.0 2023-09-24 [1] RSPM (R 4.4.0)
## progress 1.2.3 2023-12-06 [1] RSPM (R 4.4.0)
## progressr 0.15.1 2024-11-22 [1] RSPM (R 4.4.0)
## promises 1.3.2 2024-11-28 [1] RSPM (R 4.4.0)
## proxy 0.4-27 2022-06-09 [1] RSPM (R 4.4.0)
## purrr * 1.0.2 2023-08-10 [1] RSPM (R 4.4.0)
## quadprog 1.5-8 2019-11-20 [1] RSPM (R 4.4.0)
## quantreg 5.99.1 2024-11-22 [1] RSPM (R 4.4.0)
## R6 2.5.1 2021-08-19 [1] RSPM (R 4.4.0)
## ragg 1.3.3 2024-09-11 [1] RSPM (R 4.4.0)
## RANN 2.6.2 2024-08-25 [1] RSPM (R 4.4.0)
## rARPACK 0.11-0 2016-03-10 [1] RSPM (R 4.4.0)
## rbibutils 2.3 2024-10-04 [1] RSPM (R 4.4.0)
## rbiom 1.0.3 2021-11-05 [1] RSPM (R 4.4.0)
## RColorBrewer 1.1-3 2022-04-03 [1] RSPM (R 4.4.0)
## Rcpp 1.0.13-1 2024-11-02 [1] RSPM (R 4.4.0)
## RcppAnnoy 0.0.22 2024-01-23 [1] RSPM (R 4.4.0)
## RcppHNSW 0.6.0 2024-02-04 [1] RSPM (R 4.4.0)
## RcppParallel 5.1.9 2024-08-19 [1] RSPM (R 4.4.0)
## RcppZiggurat 0.1.6 2020-10-20 [1] RSPM (R 4.4.0)
## Rdpack 2.6.2 2024-11-15 [1] RSPM (R 4.4.0)
## readxl 1.4.3 2023-07-06 [1] RSPM (R 4.4.0)
## reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.4.0)
## reticulate 1.40.0 2024-11-15 [1] RSPM (R 4.4.0)
## Rfast 2.1.3 2024-12-31 [1] RSPM (R 4.4.0)
## rhdf5 2.50.1 2024-12-09 [1] Bioconductor 3.20 (R 4.4.2)
## rhdf5filters 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## Rhdf5lib 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## rlang 1.1.4 2024-06-04 [1] RSPM (R 4.4.0)
## rmarkdown 2.29 2024-11-04 [1] RSPM (R 4.4.0)
## Rmpfr 1.0-0 2024-11-18 [1] RSPM (R 4.4.0)
## rmutil 1.1.10 2022-10-27 [1] RSPM (R 4.4.0)
## rngtools 1.5.2 2021-09-20 [1] RSPM (R 4.4.0)
## robustbase 0.99-4-1 2024-09-27 [1] RSPM (R 4.4.0)
## ROCR 1.0-11 2020-05-02 [1] RSPM (R 4.4.0)
## rootSolve 1.8.2.4 2023-09-21 [1] RSPM (R 4.4.0)
## rpart 4.1.23 2023-12-05 [2] CRAN (R 4.4.2)
## RSpectra 0.16-2 2024-07-18 [1] RSPM (R 4.4.0)
## RSQLite 2.3.9 2024-12-03 [1] RSPM (R 4.4.0)
## rstatix 0.7.2 2023-02-01 [1] RSPM (R 4.4.0)
## rstudioapi 0.17.1 2024-10-22 [1] RSPM (R 4.4.0)
## rsvd 1.0.5 2021-04-16 [1] RSPM (R 4.4.0)
## Rtsne 0.17 2023-12-07 [1] RSPM (R 4.4.0)
## S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## sandwich 3.1-1 2024-09-15 [1] RSPM (R 4.4.0)
## sass 0.4.9 2024-03-15 [1] RSPM (R 4.4.0)
## ScaledMatrix 1.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## scales 1.3.0 2023-11-28 [1] RSPM (R 4.4.0)
## scater 1.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## scattermore 1.2 2023-06-12 [1] RSPM (R 4.4.0)
## sctransform 0.4.1 2023-10-19 [1] RSPM (R 4.4.0)
## scuttle 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.4.0)
## Seurat 5.1.0 2024-05-10 [1] RSPM (R 4.4.0)
## SeuratObject 5.0.2 2024-05-08 [1] RSPM (R 4.4.0)
## shape 1.4.6.1 2024-02-23 [1] RSPM (R 4.4.0)
## shiny 1.10.0 2024-12-14 [1] RSPM (R 4.4.0)
## SingleCellExperiment * 1.28.1 2024-11-10 [1] Bioconductor 3.20 (R 4.4.2)
## slam 0.1-55 2024-11-13 [1] RSPM (R 4.4.0)
## SnowballC 0.7.1 2023-04-25 [1] RSPM (R 4.4.0)
## softImpute 1.4-1 2021-05-09 [1] RSPM (R 4.4.0)
## sp 2.1-4 2024-04-30 [1] RSPM (R 4.4.0)
## spam 2.11-0 2024-10-03 [1] RSPM (R 4.4.0)
## SparseArray 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## SparseM 1.84-2 2024-07-17 [1] RSPM (R 4.4.0)
## sparseMatrixStats 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## spatial 7.3-17 2023-07-20 [2] CRAN (R 4.4.2)
## spatstat.data 3.1-4 2024-11-15 [1] RSPM (R 4.4.0)
## spatstat.explore 3.3-4 2025-01-08 [1] RSPM (R 4.4.0)
## spatstat.geom 3.3-4 2024-11-18 [1] RSPM (R 4.4.0)
## spatstat.random 3.3-2 2024-09-18 [1] RSPM (R 4.4.0)
## spatstat.sparse 3.1-0 2024-06-21 [1] RSPM (R 4.4.0)
## spatstat.univar 3.1-1 2024-11-05 [1] RSPM (R 4.4.0)
## spatstat.utils 3.1-2 2025-01-08 [1] RSPM (R 4.4.0)
## stable 1.1.6 2022-03-02 [1] RSPM (R 4.4.0)
## stabledist 0.7-2 2024-08-17 [1] RSPM (R 4.4.0)
## statip 0.2.3 2019-11-17 [1] RSPM (R 4.4.0)
## statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0)
## stringi 1.8.4 2024-05-06 [1] RSPM (R 4.4.0)
## stringr 1.5.1 2023-11-14 [1] RSPM (R 4.4.0)
## SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## survey 4.4-2 2024-03-20 [1] RSPM (R 4.4.0)
## survival 3.8-3 2024-12-17 [2] RSPM (R 4.4.0)
## systemfonts 1.1.0 2024-05-15 [1] RSPM (R 4.4.0)
## tensor 1.5 2012-05-05 [1] RSPM (R 4.4.0)
## testthat 3.2.2 2024-12-10 [1] RSPM (R 4.4.0)
## textshaping 0.4.1 2024-12-06 [1] RSPM (R 4.4.0)
## TH.data 1.1-2 2023-04-17 [1] RSPM (R 4.4.0)
## tibble 3.2.1 2023-03-20 [1] RSPM (R 4.4.0)
## tidyr * 1.3.1 2024-01-24 [1] RSPM (R 4.4.0)
## tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0)
## tidySummarizedExperiment * 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## tidytext 0.4.2 2024-04-10 [1] RSPM (R 4.4.0)
## tidytree 0.4.6 2023-12-12 [1] RSPM (R 4.4.0)
## timeDate 4041.110 2024-09-22 [1] RSPM (R 4.4.0)
## timeSeries 4041.111 2024-09-22 [1] RSPM (R 4.4.0)
## tokenizers 0.3.0 2022-12-22 [1] RSPM (R 4.4.0)
## treeio 1.30.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## TreeSummarizedExperiment * 2.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## truncnorm 1.0-9 2023-03-20 [1] RSPM (R 4.4.0)
## ttservice * 0.4.1 2024-06-07 [1] RSPM (R 4.4.0)
## UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## utf8 1.2.4 2023-10-22 [1] RSPM (R 4.4.0)
## uwot 0.2.2 2024-04-21 [1] RSPM (R 4.4.0)
## vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.4.0)
## vegan 2.6-8 2024-08-28 [1] RSPM (R 4.4.0)
## vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0)
## viridis 0.6.5 2024-01-29 [1] RSPM (R 4.4.0)
## viridisLite 0.4.2 2023-05-02 [1] RSPM (R 4.4.0)
## withr 3.0.2 2024-10-28 [1] RSPM (R 4.4.0)
## Wrench 1.24.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## xfun 0.50 2025-01-07 [1] RSPM (R 4.4.0)
## XML 3.99-0.18 2025-01-01 [1] RSPM (R 4.4.0)
## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.4.0)
## XVector * 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## yaml 2.3.10 2024-07-26 [1] RSPM (R 4.4.0)
## yulab.utils 0.1.9 2025-01-07 [1] RSPM (R 4.4.0)
## zCompositions 1.5.0-4 2024-06-19 [1] RSPM (R 4.4.0)
## zinbwave 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## ZINQ 2.0 2025-01-10 [1] Github (wdl2459/ZINQ-v2@40391a6)
## zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
## zoo 1.8-12 2023-04-13 [1] RSPM (R 4.4.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library
##
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