Subgingival vs supgragingival plaque (WMS - Species)
HMP_2012_WMS_gingival
Samuel Gamboa
17 January, 2025
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)edgeR didn’t run in this dataset A pseudocount of 1 was added, otherwiser there were problems with infinite values when calculating log2 fold change
Data
Import 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: NULLFilter low-abundant features:
tse_subset <- filterTaxa(tse, min_ab = 1, min_per = 0.2)
assay(tse_subset) <- assay(tse_subset) + 1
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: NULLCounts per body subsite:
col_data <- as_tibble(colData(tse_subset))
count(col_data, body_subsite)
#> # A tibble: 2 × 2
#> body_subsite n
#> <chr> <int>
#> 1 subgingival_plaque 7
#> 2 supragingival_plaque 9Number of subjects:
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 method options:
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)) {
if (grepl("Seurat", names(DA_methods)[i])) {
names(DA_methods[[i]]$contrast) <- NULL
} else {
next
}
}
## edgeR keeps causing problems (segmentation fault C stack overflow)
DA_methods <- DA_methods[grep("edgeR",names(DA_methods), invert = TRUE)]
names(DA_methods)
#> [1] "DESeq2.poscounts" "DESeq2.poscounts.w" "Limma-Voom.TMM"
#> [4] "Limma-Voom.TMM.w" "metagenomeSeq.CSS" "ALDEx2-Wilcox"
#> [7] "MAST" "Seurat-Wilcox" "ANCOM-BC"
#> [10] "Wilcox.TSS" "Wilcox.CLR" "ZINQ.TSS"
#> [13] "ZINQ.CLR" "LEfSe.TSS" "LEfSe.CLR"Run DA analysis:
tim <- system.time({
DA_output <- imap(DA_methods, ~ {
message("Running method ", .y, " - ", Sys.time())
tryCatch(
error = function(e) NULL,
runDA(list(.x), ps, weights = zw, verbose = FALSE)
)
}) |>
list_flatten(name_spec = "{outer}") |>
discard(is.null)
DA_output <- map2(DA_output, names(DA_output), ~ {
.x$name <- .y
.x
})
})
# DA_output$MAST <- NULL
# DA_output$`Seurat-Wilcox` <- NULL
tim
#> user system elapsed
#> 28.955 0.118 29.047Enrichment analysis
Define a threshold for LEFSE with CLR
Define threshold variables:
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))] <-
median(DA_output$LEfSe.CLR$statInfo$abs_score)
slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat")
colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP")
typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")Run enrichment:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "new_names",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Extract summary of the enrichment analysis:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$summaries |>
purrr::map(function(x) {
x |>
tibble::rownames_to_column(var = "direction") |>
tidyr::pivot_longer(
names_to = "annotation", values_to = "n",
cols = 2
)
}) |>
dplyr::bind_rows()
}) |>
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" ~ "Subgingival",
direction == "UP Abundant" ~ "Supragingival",
TRUE ~ direction
)
)
head(enrichmentSummary)
#> # A tibble: 6 × 6
#> method direction pvalue annotation n sig
#> <chr> <chr> <dbl> <chr> <int> <chr>
#> 1 DESeq2.poscounts Subgingival 1 aerobic 0 ""
#> 2 DESeq2.poscounts Subgingival 0.000000388 anaerobic 39 "***"
#> 3 DESeq2.poscounts Subgingival 1.00 facultative_anaerobic 3 ""
#> 4 DESeq2.poscounts Supragingival 0.00207 aerobic 10 "**"
#> 5 DESeq2.poscounts Supragingival 1.00 anaerobic 9 ""
#> 6 DESeq2.poscounts Supragingival 0.0000465 facultative_anaerobic 22 "***"Plots
Enrichment plot
nn <- unique(enrichmentSummary$annotation)
nn <- nn[!is.na(nn)]
colorPalette <- palette.colors(palette = "Okabe-Ito")[2:(length(nn) + 1)]
enPlot <- enrichmentSummary |>
dplyr::left_join(getMethodClass(), by = "method") |>
mutate(
direction = factor(direction, levels = c("Supragingival", "Subgingival")),
annotation = factor(annotation, levels = nn)
) |>
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_manual(values = colorPalette, name = "Annotation") +
scale_color_manual(values = colorPalette, name = "Annotation") +
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")
)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(getMethodClass(), by = 'method')
}) |> bind_rows() |>
mutate(diff = jitter(TP - FP, amount = 1.5, factor = 2))Positives plot:
# 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 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")
)
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 2025-01-17
#> pandoc 3.6 @ /usr/bin/ (via rmarkdown)
#>
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