library(MicrobiomeBenchmarkData)
library(MicrobiomeBenchmarkDataAnalyses)
library(lefser)
library(microbiomeMarker)
library(dplyr)
library(purrr)
library(stringr)
library(phyloseq)
library(mia)
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]]
col_data <- tse |>
colData() |>
as.data.frame() |>
tibble::rownames_to_column("sample_name") |>
as_tibble()
subjects <- col_data |>
pull(subject_id) |>
unique()
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[[conditions_col]]) == 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)
selected_samples <- col_data_subset |>
pull(sample_name)
tse_subset <- tse[, selected_samples]
tse_subset <- filterTaxa(tse_subset)
rankNames <- colnames(rowData(tse_subset))
rankNames <- stringr::str_replace(rankNames, "superkingdom", "kingdom")
colnames(rowData(tse_subset)) <- rankNames
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): kingdom 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: NULLConvert to phyloseq
## Annotations are lost
ps <- makePhyloseqFromTreeSE(tse_subset)
#> Warning: Tips of rowTree are renamed to match rownames.
# ps <- convertToPhyloseq(tse_subset)
colnames(tax_table(ps)) <- str_to_sentence(colnames(tax_table(ps)))
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 6 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 1556 tips and 1540 internal nodes ]
mmLefse <- run_lefse(
ps = ps,
group = "body_subsite",
taxa_rank = "none",
norm = "CPM", # This is the relative abundance but with a total ofset to 1e6
kw_cutoff = kwth,
wilcoxon_cutoff = wth,
lda_cutoff = ldath,
multigrp_strat = FALSE,
bootstrap_n = 30
)
## pvalue is from the KW
mmLefseTbl <- marker_table(mmLefse)
class(mmLefseTbl) <- NULL
mmLefseTbl <- as_tibble(mmLefseTbl)
dim(mmLefseTbl)
#> [1] 50 5
lefserInput <- relativeAb(tse_subset)
colData(lefserInput)$body_subsite <- factor(
colData(lefserInput)$body_subsite,
levels = c("supragingival_plaque", "subgingival_plaque")
)
lefserOutput <- lefser(
relab = lefserInput,
kruskal.threshold = kwth,
wilcox.threshold = wth,
lda.threshold = 3,
groupCol = "body_subsite",
blockCol = NULL
)
#> Warning in lefser(relab = lefserInput, kruskal.threshold = kwth,
#> wilcox.threshold = wth, : Variables in the input are collinear. Try only with
#> the terminal nodes using `get_terminal_nodes` function
lefserTbl <- as_tibble(lefserOutput)
dim(lefserTbl)
#> [1] 19 2
intersectOTUs <- intersect(mmLefseTbl$feature, lefserTbl$features)Positive LDA values = increased in subgingival plaque Negative LDA values = increased in supragingival plaque
lefserTbl |>
filter(features %in% intersectOTUs) |>
arrange(features)
#> # A tibble: 19 × 2
#> features scores
#> <chr> <dbl>
#> 1 OTU_97.126 3.00
#> 2 OTU_97.131 3.11
#> 3 OTU_97.17936 3.01
#> 4 OTU_97.214 3.33
#> 5 OTU_97.35359 3.01
#> 6 OTU_97.37368 3.06
#> 7 OTU_97.38234 3.42
#> 8 OTU_97.39904 3.26
#> 9 OTU_97.39982 3.43
#> 10 OTU_97.40468 3.56
#> 11 OTU_97.40495 3.12
#> 12 OTU_97.40650 3.18
#> 13 OTU_97.41422 -3.06
#> 14 OTU_97.42356 -3.41
#> 15 OTU_97.44594 -3.37
#> 16 OTU_97.44798 3.06
#> 17 OTU_97.44941 -3.41
#> 18 OTU_97.63 3.08
#> 19 OTU_97.99 3.11
mmLefseTbl |>
filter(feature %in% intersectOTUs) |>
arrange(feature)
#> # A tibble: 19 × 5
#> feature enrich_group ef_lda pvalue padj
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 OTU_97.126 subgingival_plaque 3.27 6.08e- 4 6.08e- 4
#> 2 OTU_97.131 subgingival_plaque 3.38 1.58e- 4 1.58e- 4
#> 3 OTU_97.17936 subgingival_plaque 3.27 1.76e- 4 1.76e- 4
#> 4 OTU_97.214 subgingival_plaque 3.59 4.40e- 3 4.40e- 3
#> 5 OTU_97.35359 subgingival_plaque 3.33 2.08e- 3 2.08e- 3
#> 6 OTU_97.37368 subgingival_plaque 3.29 8.97e- 6 8.97e- 6
#> 7 OTU_97.38234 subgingival_plaque 3.76 7.86e- 7 7.86e- 7
#> 8 OTU_97.39904 subgingival_plaque 3.55 1.15e- 6 1.15e- 6
#> 9 OTU_97.39982 subgingival_plaque 3.72 8.06e- 9 8.06e- 9
#> 10 OTU_97.40468 subgingival_plaque 3.85 7.87e- 8 7.87e- 8
#> 11 OTU_97.40495 subgingival_plaque 3.43 1.57e-11 1.57e-11
#> 12 OTU_97.40650 subgingival_plaque 3.51 1.75e- 7 1.75e- 7
#> 13 OTU_97.41422 supragingival_plaque 3.39 4.94e- 3 4.94e- 3
#> 14 OTU_97.42356 supragingival_plaque 3.80 1.81e- 3 1.81e- 3
#> 15 OTU_97.44594 supragingival_plaque 3.74 5.31e- 3 5.31e- 3
#> 16 OTU_97.44798 subgingival_plaque 3.44 2.18e- 3 2.18e- 3
#> 17 OTU_97.44941 supragingival_plaque 3.79 5.17e- 3 5.17e- 3
#> 18 OTU_97.63 subgingival_plaque 3.41 1.31e- 4 1.31e- 4
#> 19 OTU_97.99 subgingival_plaque 3.39 1.60e- 5 1.60e- 5
sessioninfo::session_info()
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#> ctype en_US.UTF-8
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#> date 2024-09-20
#> pandoc 3.2 @ /usr/bin/ (via rmarkdown)
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