HMP_2012_16S_gingival_V35 - subgingival vs supgragingival plaque - whole
Source:vignettes/articles/HMP_2012_WMS_gingival.Rmd
HMP_2012_WMS_gingival.Rmd
library(MicrobiomeBenchmarkDataAnalyses)
library(MicrobiomeBenchmarkData)
library(dplyr)
library(purrr)
library(phyloseq)
library(mia)
library(benchdamic)
library(ggplot2)
library(ggpubr)Analyses were carried out in a subset of subjects: subjects with samples taken for both subgingival and supragingival body subsites in the same visit. Only one visit was considered per subject.
All analyses were performed at the OTU level.
Data
Get data:
dat_name <- "HMP_2012_WMS_gingival"
conditions_col <- 'body_subsite'
conditions <- c(condB = 'subgingival_plaque', condA = 'supragingival_plaque')
tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
tse
#> class: TreeSummarizedExperiment
#> dim: 235 16
#> metadata(0):
#> assays(1): counts
#> rownames(235): Parabacteroides_merdae Bacteroides_ovatus ...
#> Corynebacterium_kroppenstedtii Actinomyces_cardiffensis
#> rowData names(8): kingdom phylum ... species taxon_annotation
#> colnames(16): SRS013949 SRS013950 ... SRS063215 SRS065310
#> colData names(24): dataset subject_id ... curator BMI
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (235 rows)
#> rowTree: 1 phylo tree(s) (10430 leaves)
#> colLinks: NULL
#> colTree: NULL
tse_subset <- filterTaxa(tse, min_ab = 1, min_per = 0.2)
tse_subset
#> class: TreeSummarizedExperiment
#> dim: 189 16
#> metadata(0):
#> assays(1): counts
#> rownames(189): Streptococcus_parasanguinis Veillonella_dispar ...
#> Prevotella_baroniae Propionibacterium_acidifaciens
#> rowData names(8): kingdom phylum ... species taxon_annotation
#> colnames(16): SRS013949 SRS013950 ... SRS063215 SRS065310
#> colData names(24): dataset subject_id ... curator BMI
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (189 rows)
#> rowTree: 1 phylo tree(s) (10430 leaves)
#> colLinks: NULL
#> colTree: NULLUnique subjects per body subsite:
col_data <- tse_subset |>
colData() |>
as.data.frame() |>
tibble::rownames_to_column("sample_name") |>
as_tibble()
col_data |>
count(body_subsite)
#> # A tibble: 2 × 2
#> body_subsite n
#> <chr> <int>
#> 1 subgingival_plaque 7
#> 2 supragingival_plaque 9Unique subjects in the dataset:
Prior information
row_data <- as.data.frame(rowData(tse_subset))
prior_info <- row_data[, c('genus', 'taxon_annotation')]
prior_info$taxon_name <- rownames(row_data)
prior_info$new_names <- paste0(prior_info$taxon_name, '|', prior_info$genus)
prior_info <-
dplyr::relocate(prior_info, taxon_name, new_names, genus, taxon_annotation)
head(prior_info)
#> taxon_name
#> Streptococcus_parasanguinis Streptococcus_parasanguinis
#> Veillonella_dispar Veillonella_dispar
#> Veillonella_atypica Veillonella_atypica
#> Veillonella_parvula Veillonella_parvula
#> Haemophilus_parainfluenzae Haemophilus_parainfluenzae
#> Dialister_invisus Dialister_invisus
#> new_names
#> Streptococcus_parasanguinis Streptococcus_parasanguinis|Streptococcus
#> Veillonella_dispar Veillonella_dispar|Veillonella
#> Veillonella_atypica Veillonella_atypica|Veillonella
#> Veillonella_parvula Veillonella_parvula|Veillonella
#> Haemophilus_parainfluenzae Haemophilus_parainfluenzae|Haemophilus
#> Dialister_invisus Dialister_invisus|Dialister
#> genus taxon_annotation
#> Streptococcus_parasanguinis Streptococcus facultative_anaerobic
#> Veillonella_dispar Veillonella anaerobic
#> Veillonella_atypica Veillonella anaerobic
#> Veillonella_parvula Veillonella anaerobic
#> Haemophilus_parainfluenzae Haemophilus facultative_anaerobic
#> Dialister_invisus Dialister anaerobicConvert to phyloseq:
ps <- convertToPhyloseq(tse_subset)
sample_data(ps)[[conditions_col]] <-
factor(sample_data(ps)[[conditions_col]], levels = conditions)
ps
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 189 taxa and 16 samples ]
#> sample_data() Sample Data: [ 16 samples by 24 sample variables ]
#> tax_table() Taxonomy Table: [ 189 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 189 tips and 188 internal nodes ]Differential abundance analysis
Set normalization, weights, and DA methods parameters:
ps <- runNormalizations(set_norm_list(), 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)) {
## This was a nacessary change for when the version increased:
if (grepl("Seurat", names(DA_methods)[i])) {
names(DA_methods[[i]]$contrast) <- NULL
} else {
next
}
}
DA_methods <- DA_methods[grep("edgeR",names(DA_methods), invert = TRUE)]
names(DA_methods)
#> [1] "DA_DESeq2.1" "DA_DESeq2.1" "DA_limma.1"
#> [4] "DA_limma.1" "DA_metagenomeSeq.1" "DA_ALDEx2.1"
#> [7] "DA_MAST.1" "DA_Seurat.1" "ancombc.1"
#> [10] "wilcox.3" "wilcox.4" "ZINQ.9"
#> [13] "ZINQ.10" "lefse.12" "lefse.13"Run DA analysis:
tim <- system.time({
DA_output <- vector("list", length(DA_methods))
for (i in seq_along(DA_output)) {
message(
"Running method ", i, ": ", names(DA_methods)[i], " - ", Sys.time()
)
DA_output[[i]] <- tryCatch(
error = function(e) NULL,
runDA(DA_methods[i], ps, weights = zw, verbose = FALSE)
)
}
DA_output <- purrr::list_flatten(DA_output, name_spec = "{inner}")
DA_output <- purrr::discard(DA_output, is.null)
})
tim
#> user system elapsed
#> 17.161 0.206 17.353Enrichment analysis
Define a threshold for LEFSE with CLR
Lefser uses two values to define differential abundant taxa, p-value and LDA.
DA_output$lefse.TSS$statInfo$abs_score |> hist()
DA_output$lefse.CLR$statInfo$abs_score |> hist()
c(
lefse.TSS = median(DA_output$lefse.TSS$statInfo$abs_score),
lefse.CLR = median(DA_output$lefse.CLR$statInfo$abs_score)
)
#> lefse.TSS lefse.CLR
#> 3.2200222 0.4958766Create variables of thresholds:
direction <- get_direction_cols(DA_output, conditions_col, conditions)
adjThr<- rep(0.1, length(DA_output))
names(adjThr) <- names(DA_output)
esThr <- rep(0, length(DA_output))
names(esThr) <- names(DA_output)
esThr[grep("lefse.TSS", names(esThr))] <- 2
esThr[grep("lefse.CLR", names(esThr))] <- round(median(
DA_output$lefse.CLR$statInfo$abs_score
), 2)
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:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "new_names",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Extract summary of the enrichment analysis:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$summaries |>
purrr::map(function(x) {
pos <- which(colnames(x) != "pvalue")
x |>
tibble::rownames_to_column(var = "direction") |>
tidyr::pivot_longer(
names_to = "annotation", values_to = "n",
cols = 2
)
}) |>
dplyr::bind_rows()
}) |>
purrr::discard(~!nrow(.x)) |>
dplyr::bind_rows(.id = "method") |>
dplyr::relocate(method, pvalue) |>
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" ~ "Subgingival",
direction == "UP Abundant" ~ "Supragingival",
TRUE ~ direction
)
)
head(enrichmentSummary)
#> # A tibble: 6 × 6
#> method pvalue direction annotation n sig
#> <chr> <dbl> <chr> <chr> <int> <chr>
#> 1 DESeq2.poscounts 1 Subgingival aerobic 0 ""
#> 2 DESeq2.poscounts 0.675 Subgingival anaerobic 2 ""
#> 3 DESeq2.poscounts 0.625 Subgingival facultative_anaerobic 1 ""
#> 4 DESeq2.poscounts 0.585 Supragingival aerobic 2 ""
#> 5 DESeq2.poscounts 1.00 Supragingival anaerobic 1 ""
#> 6 DESeq2.poscounts 0.000000303 Supragingival facultative_anaerobic 15 "***"Plots
Enrichment plot
enPlot <- enrichmentSummary |>
dplyr::left_join(get_meth_class(), by = "method") |>
mutate(
direction = factor(
direction, levels = c("Supragingival", "Subgingival")
)
) |>
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 == "aerobic" ~ "Aerobic",
annotation == "anaerobic" ~ "Anaerobic",
annotation == "facultative_anaerobic" ~ "Facultative anaerobic",
TRUE ~ annotation
)
) |>
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"
)Putative true positives - putative false positives
Calculate TP - FP ratio (no threshold)
positives <- map(1:length(DA_output), .f = function(i) {
positives <- createPositives(
object = DA_output[i],
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation", namesCol = "new_names",
slot = slotV[i], colName = colNameV[i], type = typeV[i],
direction = direction[i],
threshold_pvalue = 1,
threshold_logfc = 0,
top = seq.int(from = 0, to = 50, by = 5),
alternative = "greater",
verbose = FALSE,
TP = list(c("DOWN Abundant", "anaerobic"), c("UP Abundant", "aerobic")),
FP = list(c("DOWN Abundant", "aerobic"), c("UP Abundant", "anaerobic"))
) |>
dplyr::left_join(get_meth_class(), by = 'method')
}) |>
bind_rows()Positives plot:
# names(vec) <- positives$base_method
positives <- positives |>
mutate(diff = jitter(TP - FP, amount = 1.5, factor = 2)) |>
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
)
)
vec <- positives$color
names(vec) <- positives$base_method
posPlot <- positives |>
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")
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)
#>
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