HMP_2012_16S_gingival_V13 - subgingival vs supgragingival plaque
Source:vignettes/articles/HMP_2012_16S_gingival_V13.Rmd
HMP_2012_16S_gingival_V13.Rmd
library(MicrobiomeBenchmarkDataAnalyses)
library(MicrobiomeBenchmarkData)
library(mia)
library(phyloseq)
library(benchdamic)
library(dplyr)
library(purrr)
library(ggplot2)
library(gridExtra)Data
Import dataset:
dat_name <- 'HMP_2012_16S_gingival_V13'
conditions_col <- 'body_subsite'
conditions <- c(condB = 'subgingival_plaque', condA = 'supragingival_plaque')
tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
tse
#> class: TreeSummarizedExperiment
#> dim: 33127 311
#> metadata(0):
#> assays(1): counts
#> rownames(33127): OTU_97.1 OTU_97.10 ... OTU_97.9997 OTU_97.9999
#> 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 (33127 rows)
#> rowTree: 1 phylo tree(s) (33184 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: 1124 230
#> metadata(0):
#> assays(1): counts
#> rownames(1124): OTU_97.10 OTU_97.1000 ... OTU_97.994 OTU_97.995
#> 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 (1124 rows)
#> rowTree: 1 phylo tree(s) (33184 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_97.10 OTU_97.10 OTU_97.10|Veillonella Veillonella
#> OTU_97.1000 OTU_97.1000 OTU_97.1000|NA <NA>
#> OTU_97.10028 OTU_97.10028 OTU_97.10028|Rothia Rothia
#> OTU_97.101 OTU_97.101 OTU_97.101|Rothia Rothia
#> OTU_97.10165 OTU_97.10165 OTU_97.10165|Abiotrophia Abiotrophia
#> OTU_97.1017 OTU_97.1017 OTU_97.1017|Actinomyces Actinomyces
#> taxon_annotation
#> OTU_97.10 anaerobic
#> OTU_97.1000 <NA>
#> OTU_97.10028 facultative_anaerobic
#> OTU_97.101 facultative_anaerobic
#> OTU_97.10165 facultative_anaerobic
#> OTU_97.1017 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: [ 1124 taxa and 230 samples ]
#> sample_data() Sample Data: [ 230 samples by 15 sample variables ]
#> tax_table() Taxonomy Table: [ 1124 taxa by 5 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 1124 tips and 938 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
#> 198.608 23.948 198.819Enrichment 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)
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "new_names",
slot = "pValMat", colName = "adjP", type = "pvalue",
direction = direction,
threshold_pvalue = 0.1,
threshold_logfc = 0,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Plot enrichment
enrich_plot <- plot_enrichment(
enrichment = enrichment,
enrichment_col = "taxon_annotation",
levels_to_plot = c("aerobic", "anaerobic", "facultative_anaerobic"),
conditions = conditions
)
p <- plot_enrichment_2(
enrich_plot,
dir = c(up = 'Sup Plq', down = 'Sub Plq')
)
p
Putative true positives - putative false positives
Calculate TP - FP ratio (no threshold)
positives <- createPositives(
# object = DA_output,
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation", namesCol = "new_names",
slot = "pValMat", colName = "rawP", type = "pvalue",
# direction = direction,
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
positive_plots <- plot_positives(positives) |>
map( ~ {
.x +
theme(
axis.title = element_text(size = 17),
axis.text = element_text(size = 15),
legend.text = element_text(size = 13),
strip.text = element_text(size = 17)
)
})
grid.arrange(grobs = positive_plots, ncol = 3)
Session Info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> os Ubuntu 22.04.4 LTS
#> system x86_64, linux-gnu
#> 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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