HMP_2012_16S_gingival_V35 - subgingival vs supgragingival plaque
Source:vignettes/articles/HMP_2012_16S_gingival_V35.Rmd
HMP_2012_16S_gingival_V35.Rmd
library(MicrobiomeBenchmarkDataAnalyses)
library(MicrobiomeBenchmarkData)
library(mia)
library(phyloseq)
library(benchdamic)
library(dplyr)
library(purrr)
library(ggplot2)
library(gridExtra)
library(ggpubr)Data
Import dataset:
dat_name <- 'HMP_2012_16S_gingival_V35'
conditions_col <- 'body_subsite'
conditions <- c(condB = 'subgingival_plaque', condA = 'supragingival_plaque')
tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
tse
#> class: TreeSummarizedExperiment
#> dim: 17949 311
#> metadata(0):
#> assays(1): counts
#> rownames(17949): OTU_97.1 OTU_97.10 ... OTU_97.9991 OTU_97.9995
#> rowData names(7): superkingdom phylum ... genus taxon_annotation
#> colnames(311): 700103497 700106940 ... 700111586 700109119
#> colData names(15): dataset subject_id ... sequencing_method
#> variable_region_16s
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (17949 rows)
#> rowTree: 1 phylo tree(s) (45364 leaves)
#> colLinks: NULL
#> colTree: NULLExploring some of the data
Let’s convert the col_data into a tibble (jsut for ease of handling):
col_data <- tse |>
colData() |>
as.data.frame() |>
tibble::rownames_to_column("sample_name") |>
as_tibble()Total number of subjects:
The number of male and female subjects:
col_data |>
select(subject_id, gender) |>
unique() |>
count(gender) |>
arrange(-n)
#> # A tibble: 2 × 2
#> gender n
#> <chr> <int>
#> 1 female 67
#> 2 male 65Number of subjects per visit number:
col_data |>
select(subject_id, visit_number) |>
unique() |>
count(visit_number) |>
arrange(-n)
#> # A tibble: 3 × 2
#> visit_number n
#> <dbl> <int>
#> 1 1 88
#> 2 2 76
#> 3 3 1Number of subjects per run_center:
col_data |>
select(subject_id, run_center) |>
unique() |>
count(run_center) |>
arrange(-n)
#> # A tibble: 8 × 2
#> run_center n
#> <chr> <int>
#> 1 WUGC 74
#> 2 JCVI 32
#> 3 BCM 17
#> 4 BCM,WUGC 6
#> 5 JCVI,WUGC 6
#> 6 BCM,JCVI 3
#> 7 WUGC,BCM 2
#> 8 BI,BCM 1
sample_names <- vector("list", length(subjects))
names(sample_names) <- subjects
for (i in seq_along(subjects)) {
current_subject <- subjects[i]
sub_dat <- col_data |>
filter(subject_id == current_subject) |>
slice_max(order_by = visit_number, with_ties = TRUE, n = 1)
if (nrow(sub_dat) < 2) {
next
}
lgl_vct <- all(sort(sub_dat[["body_subsite"]]) == conditions)
if (isFALSE(lgl_vct)) {
next
}
sample_names[[i]] <- sub_dat
}
sample_names <- discard(sample_names, is.null)
col_data_subset <- bind_rows(sample_names)The number of female and male samples is still practically the same
col_data_subset |>
count(gender)
#> # A tibble: 2 × 2
#> gender n
#> <chr> <int>
#> 1 female 118
#> 2 male 112
selected_samples <- col_data_subset |>
pull(sample_name)
tse_subset <- tse[, selected_samples]
tse_subset <- filterTaxa(tse_subset)
tse_subset
#> class: TreeSummarizedExperiment
#> dim: 1556 230
#> metadata(0):
#> assays(1): counts
#> rownames(1556): OTU_97.10005 OTU_97.10006 ... OTU_97.9966 OTU_97.9991
#> rowData names(7): superkingdom phylum ... genus taxon_annotation
#> colnames(230): 700103497 700103496 ... 700109120 700109119
#> colData names(15): dataset subject_id ... sequencing_method
#> variable_region_16s
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (1556 rows)
#> rowTree: 1 phylo tree(s) (45364 leaves)
#> colLinks: NULL
#> colTree: NULLPrior information
OTU level:
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 new_names genus
#> OTU_97.10005 OTU_97.10005 OTU_97.10005|Capnocytophaga Capnocytophaga
#> OTU_97.10006 OTU_97.10006 OTU_97.10006|Actinomyces Actinomyces
#> OTU_97.10007 OTU_97.10007 OTU_97.10007|Corynebacterium Corynebacterium
#> OTU_97.10081 OTU_97.10081 OTU_97.10081|NA <NA>
#> OTU_97.10093 OTU_97.10093 OTU_97.10093|NA <NA>
#> OTU_97.10103 OTU_97.10103 OTU_97.10103|Actinomyces Actinomyces
#> taxon_annotation
#> OTU_97.10005 facultative_anaerobic
#> OTU_97.10006 anaerobic
#> OTU_97.10007 aerobic
#> OTU_97.10081 <NA>
#> OTU_97.10093 <NA>
#> OTU_97.10103 anaerobicConvert to phyloseq
ps <- makePhyloseqFromTreeSummarizedExperiment(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: [ 1556 taxa and 230 samples ]
#> sample_data() Sample Data: [ 230 samples by 15 sample variables ]
#> tax_table() Taxonomy Table: [ 1556 taxa by 5 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 1556 tips and 1540 internal nodes ]Differential abundance analysis
Select methods for DA:
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]]$contrast) <- NULL
} else {
next
}
}
names(DA_methods)
#> [1] "DA_edgeR.1" "DA_edgeR.1" "DA_DESeq2.1"
#> [4] "DA_DESeq2.1" "DA_limma.1" "DA_limma.1"
#> [7] "DA_metagenomeSeq.1" "DA_ALDEx2.1" "DA_MAST.1"
#> [10] "DA_Seurat.1" "ancombc.1" "wilcox.3"
#> [13] "wilcox.4" "ZINQ.9" "ZINQ.10"
#> [16] "lefse.12" "lefse.13"Run all of the differential analysis (DA) methods:
tim <- system.time({
DA_output <- vector("list", length(DA_methods))
for (i in seq_along(DA_output)) {
message(
"Running method ", i, ": ", names(DA_methods)[i], " - ", Sys.time()
)
DA_output[[i]] <- tryCatch(
error = function(e) NULL,
runDA(DA_methods[i], ps, weights = zw, verbose = FALSE)
)
}
DA_output <- purrr::list_flatten(DA_output, name_spec = "{inner}")
DA_output <- purrr::discard(DA_output, is.null)
})
tim
#> user system elapsed
#> 282.720 32.524 283.212Enrichment analysis
Get the column name indicating the direction of the features (increased or decreased). This is the stats output.
direction <- get_direction_cols(DA_output, conditions_col, conditions)Enrichment (adjP <= 0.1)
Lefse is the strictest method, defining DA with p-values and effect size thresholds (based on linear discriminant analysis). CLR affects the magnitud of the effect size, so let’s define a new one.
DA_output$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$abs_score)
)
#> lefse.TSS lefse.CLR
#> 2.16749921 0.06591452Create variables of thresholds:
adjThr<- rep(0.1, length(DA_output))
names(adjThr) <- names(DA_output)
esThr <- rep(0, length(DA_output))
names(esThr) <- names(DA_output)
esThr[grep("lefse.TSS", names(esThr))] <- 2
esThr[grep("lefse.CLR", names(esThr))] <- 0.06
slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat")
colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP")
typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")Run enrichment:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "new_names",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Extract summary of the enrichment analysis:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$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() |>
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" ~ "Subgingival",
direction == "UP Abundant" ~ "Supragingival",
TRUE ~ direction
)
)Create enrichment plot
enPlot <- enrichmentSummary |>
left_join(get_meth_class(), by = "method") |>
mutate(
direction = factor(
direction, levels = c("Supragingival", "Subgingival")
)
) |>
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"))
) |>
left_join(get_meth_class(), by = 'method')
}) |> bind_rows()
# names(positives) <- names(DA_output)
# positives <- createPositives(
# object = DA_output,
# priorKnowledge = prior_info,
# enrichmentCol = "taxon_annotation", namesCol = "new_names",
# slot = slotV, colName = colNameV, type = typeV,
# direction = direction,
# 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"))
# ) |>
# left_join(get_meth_class(), by = 'method')Plot TP - FP
vec <- positives$color
names(vec) <- positives$base_method
posPlot <- positives |>
mutate(diff = jitter(TP - FP, amount = 1.5, factor = 2)) |>
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 plots:
Session Info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> os Ubuntu 22.04.4 LTS
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#> ui X11
#> language en
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2024-09-24
#> pandoc 3.2 @ /usr/bin/ (via rmarkdown)
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