Bacterial vaginosis vs healthy vaginal samples (OTU)
Ravel_2011_16S_BV
Samuel Gamboa
17 January, 2025
Source:vignettes/articles/Ravel_2011_16S_BV.Rmd
Ravel_2011_16S_BV.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]]
tse
#> class: TreeSummarizedExperiment
#> dim: 247 394
#> metadata(0):
#> assays(1): counts
#> rownames(247): Lactobacillus iners Lactobacillus crispatus ...
#> Bacilli_2 Microbacterium
#> rowData names(7): kingdom class ... species taxon_annotation
#> colnames(394): S001 S002 ... 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: NULLSelect samples with low and high Nugent scores only:
select_samples <- which(colData(tse)$study_condition %in% conditions)
tse_subset <- tse[, select_samples]
tse_subset
#> class: TreeSummarizedExperiment
#> dim: 247 345
#> metadata(0):
#> assays(1): counts
#> rownames(247): Lactobacillus iners Lactobacillus crispatus ...
#> Bacilli_2 Microbacterium
#> rowData names(7): kingdom class ... species taxon_annotation
#> colnames(345): S001 S002 ... 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: NULLAgglomerate by genus:
## all(colSums(assay(tse_subset)) == colSums(assay(tse_genus)))
## the code in the line above should be TRUE before filtering
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): S001 S002 ... 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: NULLSample counts per condition:
col_data <- as_tibble(colData(tse_genus))
col_data |>
summarise(
.by = c(
"nugent_score_category", "study_condition"
),
range = paste0(min(nugent_score), "-", max(nugent_score)),
n = n()
) |>
arrange(study_condition, n) |>
relocate(study_condition, n)
#> # A tibble: 2 × 4
#> study_condition n nugent_score_category range
#> <fct> <int> <chr> <chr>
#> 1 healthy 248 low 0-3
#> 2 bacterial_vaginosis 97 high 7-10Prior 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]]$contrast) <- NULL
} else {
next
}
}
names(DA_methods)
#> [1] "edgeR.TMM" "edgeR.TMM.w" "DESeq2.poscounts"
#> [4] "DESeq2.poscounts.w" "Limma-Voom.TMM" "Limma-Voom.TMM.w"
#> [7] "metagenomeSeq.CSS" "ALDEx2-Wilcox" "MAST"
#> [10] "Seurat-Wilcox" "ANCOM-BC" "Wilcox.TSS"
#> [13] "Wilcox.CLR" "ZINQ.TSS" "ZINQ.CLR"
#> [16] "LEfSe.TSS" "LEfSe.CLR"Run DA analysis:
tim <- system.time({
DA_output <- imap(DA_methods, ~ {
message("Running method ", .y, " - ", Sys.time())
tryCatch(
error = function(e) NULL,
runDA(list(.x), ps, weights = zw, verbose = FALSE)
)
}) |>
list_flatten(name_spec = "{outer}") |>
discard(is.null)
DA_output <- map2(DA_output, names(DA_output), ~ {
.x$name <- .y
.x
})
})
tim
#> user system elapsed
#> 13.936 1.075 13.997Enrichment
Set threshold variables:
direction <- get_direction_cols(DA_output, conditions_col, conditions)
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)) ~ median(DA_output$LEfSe.CLR$statInfo$abs_score),
TRUE ~ 0
) |>
set_names(names(DA_output))
slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat")
colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP")
typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")Run enrichment analysis:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "taxon_name",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Create enrichment summary:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$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(getMethodClass(), by = "method") |>
filter(annotation != "Unannotated") |>
mutate(
direction = factor(direction, levels = c("BV", "HV"))
) |>
mutate(
annotation = case_when(
annotation == "bv-associated" ~ "BV-associated",
annotation == "hv-associated" ~ "HV-associated"
) |>
forcats::fct_relevel("BV-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_y_continuous(breaks = pretty) +
scale_fill_discrete(name = "Feature annotations") +
scale_color_discrete(name = "Feature annotations") +
labs(
x = "DA method", y = "Number of DAFs"
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
strip.background = element_rect(fill = "white")
)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(getMethodClass(), by = 'method')
}) |>
bind_rows()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 DAFs", y = "TP - FP"
) +
scale_shape(name = "Normalization") +
scale_linetype(name = "Normalization") +
scale_color_manual(values = vec, name = "Base DA method") +
theme_bw() +
theme(
legend.position = "bottom",
strip.background = element_rect(fill = "white")
)Combine enrichment and “positives” plot:
Perform DA with lefse and Wilcox
Convert the TSE to tibble:
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)
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:Mobi… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> 2 genus:Gard… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> 3 genus:Cory… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> 4 genus:Geme… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> 5 genus:Stap… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> 6 genus:Aero… S001 0 0 -0.424 Ravel_… female vagina SRR062670
#> # 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) {
taxa <- split(dat, factor(dat$taxon_name))
taxa_names <- names(taxa)
taxa_annotations <- data |>
dplyr::select(tidyselect::starts_with('taxon')) |>
dplyr::distinct()
pvalues <- vector('double', length(taxa))
names(pvalues) <- taxa_names
formula_chr <- paste0(val_col, ' ~ study_condition')
for (i in seq_along(pvalues)) {
df <- taxa[[i]]
res <- stats::wilcox.test(formula = as.formula(formula_chr), data = df)
pvalues[[i]] <- res$p.value
}
adj_pvalues <- stats::p.adjust(pvalues, method = 'fdr')
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)
}
}
}
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 S001 0 healthy
#> 2 counts genus:Corynebacterium S002 3 healthy
#> 3 counts genus:Corynebacterium S003 7 healthy
#> 4 counts genus:Corynebacterium S004 3 healthy
#> 5 counts genus:Corynebacterium S006 0 healthy
#> 6 counts genus:Corynebacterium S007 0 healthyBox 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('bacterial_vaginosis', 'healthy'),
labels = c('BV', 'HV')
)) |>
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"),
plot.margin = margin(10, 10, 10, 50) # top, right, bottom, left
)
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')
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', ncol = 2, nrow = 2,
common.legend = TRUE, legend = 'bottom',
labels = c('a)', 'b)', 'c)', 'd)')
)
plotsb
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> 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 2025-01-17
#> pandoc 3.6 @ /usr/bin/ (via rmarkdown)
#>
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