Gingival results
Samuel Gamboa-Tuz, Sehyun Oh
September 25, 2024
Source:vignettes/articles/gingival_results.Rmd
gingival_results.Rmd
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(microbiomeMarker)
library(benchdamic)
library(MicrobiomeBenchmarkData)
library(ggpubr)Import data
Create a temporary directory to store the files:
fName <- system.file(
"extdata", "gingival_results_2024-09-18.zip",
package = "MicrobiomeBenchmarkDataLefse", mustWork = TRUE
)
tmpDir <- tempfile()
unzip(zipfile = fName, exdir = tmpDir)
fls <- list.files(tmpDir, full.names = TRUE)
names(fls) <- list.files(tmpDir, full.names = FALSE)Read the data and make it uniform:
## Results from LEfSe (run in Docker)
lefseDat <- read_tsv(
grep("\\.res", fls, value = TRUE),
col_names = c("feature", "x", "sample", "lda", "rawP"),
show_col_types = FALSE
) |>
select(-x) |>
drop_na() |>
mutate(
feature = sub("(OTU)(97)(\\d+)$", "\\1_\\2.\\3", feature)
) |>
mutate(rawP = as.double(rawP)) |>
mutate(adjP = p.adjust(rawP)) |>
dplyr::rename(enGrp = sample) |>
mutate(software = "LEfSe") |>
tibble::column_to_rownames(var = "feature") |>
mutate(
lda = case_when(
enGrp == "subgingival_plaque" ~ lda,
enGrp == "supragingival_plaque" ~ -lda,
)
) |>
arrange(lda)
## Results from lefser
lefserDat <- read_csv(
grep("lefser\\.csv", fls, value = TRUE),
show_col_types = FALSE
) |>
tibble::column_to_rownames(var = "features") |>
dplyr::rename(lda = scores) |>
mutate(
## I got the enriched groups by comparing this result with
## lefseDat and mmDat
enGrp = case_when(
lda < 0 ~ "supragingival_plaque",
lda > 0 ~ "subgingival_plaque",
),
# lda = abs(lda),
rawP = 0.0099,
adjP = 0.099
) |>
mutate(software = "lefser") |>
arrange(lda)
## Results from microbiomeMarker
mmDat <- readRDS(
grep("mm\\.rds", fls, value = TRUE),
) |>
marker_table() |>
as.matrix() |>
as.data.frame() |>
magrittr::set_rownames(NULL) |>
tibble::column_to_rownames("feature") |>
dplyr::rename(
enGrp = enrich_group,
lda = ef_lda,
rawP = pvalue,
adjP = padj
) |>
mutate_at(
.vars = c("lda", "rawP", "adjP"),
.funs = as.double
) |>
mutate(software = "microbiomeMarker") |>
mutate(
lda = case_when(
enGrp == "subgingival_plaque" ~ lda,
enGrp == "supragingival_plaque" ~ -lda,
)
) |>
arrange(lda)
DA <- list(
LEfSe = lefseDat,
lefser = lefserDat,
microbiomeMarker = mmDat
)
map_int(DA, nrow)
#> LEfSe lefser microbiomeMarker
#> 327 281 410Comparison of absolute LDA scores
DA |>
bind_rows() |>
ggplot(aes(lda)) +
geom_histogram(aes(fill = software), binwidth = 0.1) +
scale_fill_viridis_d(option = "D", name = "Software") +
labs(
x = "LDA score",
y = "Frequency"
) +
theme_minimal()
Benchdamic-like plots
Create a list object compatible with the benchdamic
workflow:
createBenchObj <- function(x) {
pValMat <- x[, c("rawP", "adjP"), drop = FALSE]
statInfo <- x
name = unique(x[["software"]])
list(
pValMat = pValMat,
statInfo = statInfo,
name = name
)
}
DA_output <- map(DA, createBenchObj)
str(DA_output, max.level = 2)
#> List of 3
#> $ LEfSe :List of 3
#> ..$ pValMat :'data.frame': 327 obs. of 2 variables:
#> ..$ statInfo:'data.frame': 327 obs. of 5 variables:
#> ..$ name : chr "LEfSe"
#> $ lefser :List of 3
#> ..$ pValMat :'data.frame': 281 obs. of 2 variables:
#> ..$ statInfo:'data.frame': 281 obs. of 5 variables:
#> ..$ name : chr "lefser"
#> $ microbiomeMarker:List of 3
#> ..$ pValMat :'data.frame': 410 obs. of 2 variables:
#> ..$ statInfo:'data.frame': 410 obs. of 5 variables:
#> ..$ name : chr "microbiomeMarker"Get biological information
tse <- getBenchmarkData("HMP_2012_16S_gingival_V35", dryrun = FALSE)[[1]]
bioData <- tse |>
rowData() |>
as.data.frame()Plot of difference of putatitive TRUE and FALSE positives
cbfColors <- c("#D55E00", "#009E73", "#0072B2")
posData <- createPositives(
object = DA_output,
priorKnowledge = bioData, enrichmentCol = "taxon_annotation",
namesCol = NULL,
slot = "statInfo", colName = "lda", type = "logfc",
direction = "lda",
threshold_pvalue = 1, threshold_logfc = 0,
top = seq(5, 100, 5),
alternative = "g",
verbose = FALSE,
TP = list(
c("UP Abundant", "anaerobic"),
c("DOWN Abundant", "aerobic")
),
FP = list(
c("UP Abundant", "aerobic"),
c("DOWN Abundant", "anaerobic")
)
)
posPlot <- plotPositives(posData)
for (i in seq_along(posPlot$layers)) {
## Remove layers (geom_line and geom_point)
## This is necessary to add new layers with shapes and colors of choice
posPlot$layers[[1]] <- NULL
}
posPlot <- posPlot +
geom_line(
aes(x = top, y = TP - FP, linetype = method)
) +
geom_point(
aes(x = top, y = TP - FP, shape = method)
) +
scale_linetype(name = "Software") +
scale_shape_manual(values = c(1, 2, 3), name = "Software") +
scale_color_manual(values = cbfColors, name = "Software") +
theme_minimal() +
labs(title = NULL, subtitle = NULL) +
ylab("Putative TP - Putative FP") +
xlab("# of top features") +
theme(
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
posPlot
### Enrichemnt plot
enData <- createEnrichment(
object = DA_output,
priorKnowledge = bioData, enrichmentCol = "taxon_annotation",
namesCol = NULL,
slot = "statInfo", colName = "lda", type = "logfc",
direction = "lda",
## No threshold since all results included here
## are significant
threshold_pvalue = 1, threshold_logfc = 0,
top = NULL,
alternative = "greater",
verbose = FALSE
)
enPlot <- plotEnrichment(enData, "taxon_annotation", c("aerobic", "anaerobic"))
enPlot <- enPlot +
labs(x = "Software") +
theme_minimal()
enPlot
myFun <- function(x) {
x |>
tibble::rownames_to_column(var = "direction")
}
enDatMod <- map(enData, ~ {
.x$summaries |>
map(myFun) |>
bind_rows() |>
pivot_longer(
names_to = "annotation", values_to = "ntaxa",
cols = c(aerobic, anaerobic, facultative_anaerobic)
) |>
filter(!is.na(ntaxa))
}) |>
bind_rows(.id = "software") |>
mutate(
direction = case_when(
direction == "DOWN Abundant" ~ "Supragingival",
direction == "UP Abundant" ~ "Subgingival"
)
) |>
mutate(
sig = case_when(
pvalue < 0.05 & pvalue >= 0.01 ~ "*" ,
pvalue < 0.01 & pvalue >= 0.001 ~ "**",
pvalue < 0.001 ~ "***",
TRUE ~ ""
)
)
enPlotMod <- enDatMod |>
ggplot(aes(software, ntaxa)) +
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_wrap(~direction) +
scale_fill_manual(
values = cbfColors, name = "Annotations"
) +
labs(
x = "", y = "Number of DA taxa"
) +
guides(color="none") +
theme_minimal() +
theme(
axis.title = element_text(size = 12),
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
legend.position = "bottom",
strip.text = element_text(size = 12, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10)
)
enPlotMod
Session information
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.1 (2024-06-14)
#> 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 2024-09-25
#> pandoc 3.2 @ /usr/bin/ (via rmarkdown)
#>
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