BSEA gingival plaque - MicrobiomeBenchmarkData - 35S
Source:vignettes/articles/subgingival_plaque.Rmd
subgingival_plaque.Rmd
library(bugphyzzAnalyses)
library(MicrobiomeBenchmarkData)
library(bugphyzz)
library(EnrichmentBrowser)
library(mia)
library(dplyr)
library(DT)
library(purrr)
library(tibble)
library(tidyr)
library(ggplot2)Data
dat_name <- 'HMP_2012_16S_gingival_V35'
tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]]
tse_genus <- splitByRanks(tse)$genus
## Filter taxa with at least 1 count as abundance in at least 20% of samples
min_n_samples <- round(ncol(tse_genus) * 0.2)
tse_subset <- tse_genus[rowSums(assay(tse_genus) >= 1) >= min_n_samples,]
tse_subset
#> class: TreeSummarizedExperiment
#> dim: 37 311
#> metadata(1): agglomerated_by_rank
#> assays(1): counts
#> rownames(37): Streptococcus Neisseria ... Mogibacterium Peptococcus
#> rowData names(7): superkingdom phylum ... genus taxon_annotation
#> colnames(311): 700103497 700106940 ... 700111586 700109119
#> colData names(15): dataset subject_id ... sequencing_method
#> variable_region_16s
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: a LinkDataFrame (37 rows)
#> rowTree: 1 phylo tree(s) (45364 leaves)
#> colLinks: NULL
#> colTree: NULLNumber of samples per condition
table(tse_subset$body_subsite)
#>
#> subgingival_plaque supragingival_plaque
#> 152 159bugphyzz signatures
Signatures at the genus level:
discrete_types <- c(
"binary", "multistate-intersection", "multistate-union"
)
bp <- importBugphyzz(v = 0.5) |>
map(~ mutate(.x, NCBI_ID = as.character(NCBI_ID))) |>
map(~ {
attr_type <- unique(.x$Attribute_type)
if (attr_type %in% discrete_types) {
df <- .x |>
filter(
!(Validation < 0.7 & Evidence == "asr")
)
} else if (attr_type == "numeric") {
df <- .x |>
filter(
!(Validation < 0.5 & Evidence == "asr")
)
}
df
})
aer <- bp$aerophilicity
gn_sigs <- makeSignatures(
dat = aer, taxIdType = 'Taxon_name', taxLevel = 'genus'
)
## Only one genus is missing (TG5), which is ambiguous in the ncbi taxonomy
mean(as.integer(
rownames(tse_subset) %in% unique(unlist(gn_sigs, use.names = FALSE ))
))
#> [1] 0.972973Without ASR some annotations are missing:
gn_sigs_noasr <- makeSignatures(
dat = aer, taxIdType = 'Taxon_name', taxLevel = 'genus',
evidence = c("exp", "igc", "tas", "nas", "tax")
)
mean(as.integer(
rownames(tse_subset) %in% unique(unlist(gn_sigs_noasr, use.names = FALSE))
))
#> [1] 0.8108108Enrichment (GSEA)
-
*significant (Pvalue < 0.05) -
+enriched in supragingival plaque -
-enriched in subgingival plaque
gsea <- sbea(
method = 'gsea', se = edger, gs = gn_sigs, perm = 1000,
alpha = 0.1 # This is for DA taxa
)
gsea_tbl <- as.data.frame(gsea$res.tbl) |>
mutate(
GENE.SET = ifelse(PVAL < 0.05, paste0(GENE.SET, ' *'), GENE.SET),
PVAL = round(PVAL, 3),
) |>
rename(BUG.SET = GENE.SET)
myDataTable(gsea_tbl)Enrichment (GSEA) no asr
Even without ASR annotations, the enrichment results are still the same.
gsea <- sbea(
method = 'gsea', se = edger, gs = gn_sigs_noasr, perm = 1000,
alpha = 0.1 # This is for DA taxa
)
gsea_tbl <- as.data.frame(gsea$res.tbl) |>
mutate(
GENE.SET = ifelse(PVAL < 0.05, paste0(GENE.SET, ' *'), GENE.SET),
PVAL = round(PVAL, 3),
) |>
rename(BUG.SET = GENE.SET)
myDataTable(gsea_tbl)Enrichment (ORA)
Combine into a single table:
edger_up <- edger
gn_sigs_bk <- c(gn_sigs, list(background = rownames(edger)))
## Ora is performed by comparing the significant group (p.adjust < 0.1)
## against the background. The background should include all of the
## taxa in the experiment. That's why I assigned an adjusted p-value of
## 1 to all taxa with a negative FC in the code below.
rowData(edger_up)$ADJ.PVAL <- ifelse(
rowData(edger_up)$FC < 0, 1, rowData(edger_up)$ADJ.PVAL
)
ora_up <- sbea(
method = 'ora', se = edger_up, gs = gn_sigs_bk, perm = 0,
alpha = 0.1 # This is for DA taxa
)
ora_up_df <- as.data.frame(ora_up$res.tbl)
ora_up_df$BODY.SITE <- 'Supragingival plaque'
edger_down <- edger
rowData(edger_down)$ADJ.PVAL <- ifelse(
rowData(edger_down)$FC > 0, 1, rowData(edger_down)$ADJ.PVAL
)
ora_down <- sbea(
method = 'ora', se = edger_down, gs = gn_sigs_bk, perm = 0,
alpha = 0.1,
)
ora_down_df <- data.frame(ora_down$res.tbl)
ora_down_df$BODY.SITE <- 'Subgingival plaque'
ora <- bind_rows(ora_up_df, ora_down_df) |>
arrange(BODY.SITE, PVAL) |>
relocate(BODY.SITE) |>
mutate(
GENE.SET = ifelse(PVAL < 0.05, paste0(GENE.SET, ' *'), GENE.SET),
PVAL = round(PVAL, 3),
) |>
dplyr::rename(
BUG.SET = GENE.SET, NR.BUGS = NR.GENES, NR.SIG.BUGS = NR.SIG.GENES
) |>
filter(BUG.SET != "background")
myDataTable(ora)ORA - NO ASR
gn_sigs_noasr_bk <- c(gn_sigs_noasr, list(background = rownames(edger)))
ora_up_noasr <- sbea(
method = 'ora', se = edger_up, gs = gn_sigs_noasr_bk, perm = 0,
alpha = 0.1 # This is for DA taxa
)
ora_up_df_noasr <- as.data.frame(ora_up_noasr$res.tbl)
ora_up_df_noasr$BODY.SITE <- 'Supragingival plaque'
ora_down_noasr <- sbea(
method = 'ora', se = edger_down, gs = gn_sigs_noasr_bk, perm = 0,
alpha = 0.1 # This is for DA taxa
)
ora_down_df_noasr <- data.frame(ora_down_noasr$res.tbl)
ora_down_df_noasr$BODY.SITE <- 'Subgingival plaque'
ora_noasr <- bind_rows(ora_up_df_noasr, ora_down_df_noasr) |>
arrange(BODY.SITE, PVAL) |>
relocate(BODY.SITE) |>
mutate(
GENE.SET = ifelse(PVAL < 0.05, paste0(GENE.SET, ' *'), GENE.SET),
PVAL = round(PVAL, 3),
) |>
dplyr::rename(
BUG.SET = GENE.SET, NR.BUGS = NR.GENES, NR.SIG.BUGS = NR.SIG.GENES
) |>
filter(BUG.SET != "background")
myDataTable(ora_noasr)dbBact approach
bpSigs_g <- map(
bp, ~ {
makeSignatures(.x, taxIdType = "NCBI_ID", taxLevel = "genus")
}) |>
list_flatten(name_spec = "{inner}") |>
discard(is.null)
# bpSigs_g <- bpSigs_g[grep("aerophilicity", names(bpSigs_g))]
con <- rownames(edger)[which(rowData(edger)$FC > 0 & rowData(edger)$ADJ.PVAL < 0.1)]
con <- taxizedb::name2taxid(con, db = "ncbi", out_type = "summary")
con <- con$id[con$id != "508215"]
attr(con, "nexp") <- 1
cas <- rownames(edger)[which(rowData(edger)$FC < 0 & rowData(edger)$ADJ.PVAL < 0.1)]
cas <- cas[which(cas != "TG5")]
cas <- taxizedb::name2taxid(cas, db = "ncbi")
attr(cas, "nexp") <- 1
se <- dbEn2(con, cas, bpSigs_g)
dbHt(se)
se |>
rowData() |>
as.data.frame() |>
tibble::rownames_to_column(var = "bp_sig") |>
as_tibble() |>
arrange(P_value) |>
myDataTable()Quick checks
Abundance of taxa in the ‘down-regulated’ set:
## Couldn't make tidySummarizedExperiment work.
## Update code later.
counts1 <- assay(edger_down, 'counts')
counts1 <- log2(counts1 + 1)
counts1 <- counts1 |>
as.data.frame() |>
rownames_to_column(var = 'taxname') |>
pivot_longer(
cols = 2:last_col(), names_to = 'sample', values_to = 'abundance'
)
row_data1 <- edger_down |>
rowData() |>
as.data.frame() |>
rownames_to_column(var = 'taxname') |>
as_tibble()
col_data1 <- edger_down |>
colData() |>
as.data.frame() |>
rownames_to_column(var = 'sample') |>
as_tibble()
data1 <- left_join(counts1, row_data1, by = 'taxname') |>
left_join(col_data1, by = 'sample')
p1 <- data1 |>
filter(ADJ.PVAL < 0.1) |>
ggplot(aes(body_subsite, abundance)) +
labs(
title = 'Down-regulated supragingival = Up-regulated in subgingival'
) +
geom_boxplot() +
geom_point()
p1
Abundance of taxa in the ‘up-regulated’ set:
## Couldn't make tidySummarizedExperiment work.
## Update code later.
counts2 <- assay(edger_up, 'counts')
counts2 <- log2(counts2 + 1)
counts2 <- counts2 |>
as.data.frame() |>
rownames_to_column(var = 'taxname') |>
pivot_longer(
cols = 2:last_col(), names_to = 'sample', values_to = 'abundance'
)
row_data2 <- edger_up |>
rowData() |>
as.data.frame() |>
rownames_to_column(var = 'taxname') |>
as_tibble()
col_data2 <- edger_up |>
colData() |>
as.data.frame() |>
rownames_to_column(var = 'sample') |>
as_tibble()
data2 <- left_join(counts2, row_data2, by = 'taxname') |>
left_join(col_data2, by = 'sample')
p2 <- data2 |>
filter(ADJ.PVAL < 0.1) |>
ggplot(aes(body_subsite, abundance)) +
labs(
title = 'Up-regulated supragingival = Down-regulated in subgingival'
) +
geom_boxplot() +
geom_point()
p2
Session information
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