HMP_2012_16S_gingival_V35 - subgingival vs supgragingival plaque - downsampling
Source:vignettes/articles/HMP_2012_16S_gingival_V35.Rmd
HMP_2012_16S_gingival_V35.Rmd
library(MicrobiomeBenchmarkDataAnalyses)
library(MicrobiomeBenchmarkData)
library(dplyr)
library(purrr)
library(phyloseq)
library(mia)
library(benchdamic)
library(ggplot2)
library(ggpubr)Analyses were carried out in a subset of subjects: subjects with samples taken for both subgingival and supragingival body subsites in the same visit. Only one visit was considered per subject.
All analyses were performed at the OTU level.
Data
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: NULLUnique subjects:
col_data <- tse |>
colData() |>
as.data.frame() |>
tibble::rownames_to_column("sample_name") |>
as_tibble()
subjects <- col_data |>
pull(subject_id) |>
unique()
length(subjects)
#> [1] 132
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) {
## Only get subjects with samples from both subgingival and supragingival
## plaque taken in the same visit.
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)
nrow(col_data_subset)
#> [1] 230The number of female and male samples is still practically the same:
col_data_subset |>
count(gender, body_subsite)
#> # A tibble: 4 × 3
#> gender body_subsite n
#> <chr> <chr> <int>
#> 1 female subgingival_plaque 59
#> 2 female supragingival_plaque 59
#> 3 male subgingival_plaque 56
#> 4 male supragingival_plaque 56This is a subset of V35, but still is larger than the subset included in the MicrobiomeBenchmarkData package:
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
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 6 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 1556 tips and 1540 internal nodes ]Differential abundance analysis
Perform normalization, calculate weights, and select DA methods:
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)) {
## This was a nacessary change for when the version increased:
if (grepl("Seurat", names(DA_methods)[i])) {
names(DA_methods[[i]]$contrast) <- NULL
} else {
next
}
}
names(DA_methods)
#> [1] "DA_edgeR.1" "DA_edgeR.1" "DA_DESeq2.1"
#> [4] "DA_DESeq2.1" "DA_limma.1" "DA_limma.1"
#> [7] "DA_metagenomeSeq.1" "DA_ALDEx2.1" "DA_MAST.1"
#> [10] "DA_Seurat.1" "ancombc.1" "wilcox.3"
#> [13] "wilcox.4" "ZINQ.9" "ZINQ.10"
#> [16] "lefse.12" "lefse.13"Run all of the differential analysis (DA) methods:
tim <- system.time({
DA_output <- vector("list", length(DA_methods))
for (i in seq_along(DA_output)) {
message(
"Running method ", i, ": ", names(DA_methods)[i], " - ", Sys.time()
)
DA_output[[i]] <- tryCatch(
error = function(e) NULL,
runDA(DA_methods[i], ps, weights = zw, verbose = FALSE)
)
}
DA_output <- purrr::list_flatten(DA_output, name_spec = "{inner}")
DA_output <- purrr::discard(DA_output, is.null)
})
tim
#> user system elapsed
#> 133.336 28.402 134.690Enrichment analysis
Define a threshold for LEFSE with CLR
Lefser uses two values to define differential abundant taxa, p-value and LDA.
DA_output$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$abs_score)
)
#> lefse.TSS lefse.CLR
#> 2.10563332 0.06591452Create variables of thresholds:
direction <- get_direction_cols(DA_output, conditions_col, conditions)
adjThr<- rep(0.1, length(DA_output))
names(adjThr) <- names(DA_output)
esThr <- rep(0, length(DA_output))
names(esThr) <- names(DA_output)
esThr[grep("lefse.TSS", names(esThr))] <- 2
esThr[grep("lefse.CLR", names(esThr))] <- 0.06
slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat")
colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP")
typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")Run enrichment:
enrichment <- createEnrichment(
object = DA_output,
priorKnowledge = prior_info,
enrichmentCol = "taxon_annotation",
namesCol = "new_names",
slot = slotV, colName = colNameV, type = typeV,
direction = direction,
threshold_pvalue = adjThr,
threshold_logfc = esThr,
top = NULL, # No top feature selected
alternative = "greater",
verbose = FALSE
)Extract summary of the enrichment analysis:
enrichmentSummary <- purrr::map(enrichment, ~ {
.x$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
)
)
head(enrichmentSummary)
#> # A tibble: 6 × 6
#> method pvalue direction annotation n sig
#> <chr> <dbl> <chr> <chr> <int> <chr>
#> 1 edgeR.TMM 1 e+ 0 Subgingival aerobic 0 ""
#> 2 edgeR.TMM 1.75e-39 Subgingival anaerobic 127 "***"
#> 3 edgeR.TMM 1 e+ 0 Subgingival facultative_anaerobic 10 ""
#> 4 edgeR.TMM 2.31e- 1 Supragingival aerobic 22 ""
#> 5 edgeR.TMM 9.96e- 1 Supragingival anaerobic 26 ""
#> 6 edgeR.TMM 4.82e- 2 Supragingival facultative_anaerobic 40 "*"Plots
Enrichment plot
enPlot <- enrichmentSummary |>
dplyr::left_join(get_meth_class(), by = "method") |>
mutate(
direction = factor(
direction, levels = c("Supragingival", "Subgingival")
)
) |>
mutate(
method = case_when(
grepl("lefse", method) ~ sub("lefse", "LEfSe", method),
grepl("wilcox", method) ~ sub("wilcox", "Wilcox", method),
TRUE ~ method
)
) |>
mutate(
annotation = case_when(
annotation == "aerobic" ~ "Aerobic",
annotation == "anaerobic" ~ "Anaerobic",
annotation == "facultative_anaerobic" ~ "Facultative anaerobic",
TRUE ~ annotation
)
) |>
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"))
) |>
dplyr::left_join(get_meth_class(), by = 'method')
}) |> bind_rows()Positives plot:
# names(vec) <- positives$base_method
positives <- positives |>
mutate(diff = jitter(TP - FP, amount = 1.5, factor = 2)) |>
mutate(
base_method = case_when(
grepl("lefse", base_method) ~ sub("lefse", "LEfSe", base_method),
grepl("wilcox", base_method) ~ sub("wilcox", "Wilcox", base_method),
TRUE ~ base_method
),
method = case_when(
grepl("lefse", method) ~ sub("lefse", "LEfSe", method),
grepl("wilcox", method) ~ sub("wilcox", "Wilcox", method),
TRUE ~ method
)
)
vec <- positives$color
names(vec) <- positives$base_method
posPlot <- positives |>
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")
Session Info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.2 (2024-10-31)
#> os Ubuntu 24.04.1 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language en
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2025-01-09
#> pandoc 3.6 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> abind 1.4-8 2024-09-12 [1] RSPM (R 4.4.0)
#> ade4 1.7-22 2023-02-06 [1] RSPM (R 4.4.0)
#> ALDEx2 1.38.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> ANCOMBC 2.8.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> annotate 1.84.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> AnnotationDbi 1.68.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> ape 5.8-1 2024-12-16 [1] RSPM (R 4.4.0)
#> aplot 0.2.4 2024-12-17 [1] RSPM (R 4.4.0)
#> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0)
#> base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.4.0)
#> beachmat 2.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> beeswarm 0.4.0 2021-06-01 [1] RSPM (R 4.4.0)
#> benchdamic * 1.11.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
#> biglm 0.9-3 2024-06-12 [1] RSPM (R 4.4.0)
#> Biobase * 2.66.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> BiocFileCache 2.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> BiocNeighbors 2.0.1 2024-11-28 [1] Bioconductor 3.20 (R 4.4.2)
#> BiocParallel 1.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> BiocSingular 1.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> biomformat 1.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> Biostrings * 2.74.1 2024-12-16 [1] Bioconductor 3.20 (R 4.4.2)
#> bit 4.5.0.1 2024-12-03 [1] RSPM (R 4.4.0)
#> bit64 4.5.2 2024-09-22 [1] RSPM (R 4.4.0)
#> bitops 1.0-9 2024-10-03 [1] RSPM (R 4.4.0)
#> blob 1.2.4 2023-03-17 [1] RSPM (R 4.4.0)
#> bluster 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> boot 1.3-31 2024-08-28 [2] CRAN (R 4.4.2)
#> brio 1.1.5 2024-04-24 [1] RSPM (R 4.4.0)
#> broom 1.0.7 2024-09-26 [1] RSPM (R 4.4.0)
#> bslib 0.8.0 2024-07-29 [1] RSPM (R 4.4.0)
#> cachem 1.1.0 2024-05-16 [1] RSPM (R 4.4.0)
#> car 3.1-3 2024-09-27 [1] RSPM (R 4.4.0)
#> carData 3.0-5 2022-01-06 [1] RSPM (R 4.4.0)
#> caTools 1.18.3 2024-09-04 [1] RSPM (R 4.4.0)
#> cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.4.0)
#> checkmate 2.3.2 2024-07-29 [1] RSPM (R 4.4.0)
#> class 7.3-22 2023-05-03 [2] CRAN (R 4.4.2)
#> cli 3.6.3 2024-06-21 [1] RSPM (R 4.4.0)
#> clue 0.3-66 2024-11-13 [1] RSPM (R 4.4.0)
#> cluster 2.1.8 2024-12-11 [2] RSPM (R 4.4.0)
#> codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.2)
#> coin 1.4-3 2023-09-27 [1] RSPM (R 4.4.0)
#> colorspace 2.1-1 2024-07-26 [1] RSPM (R 4.4.0)
#> CompQuadForm 1.4.3 2017-04-12 [1] RSPM (R 4.4.0)
#> corncob 0.4.1 2024-01-10 [1] RSPM (R 4.4.0)
#> corpcor 1.6.10 2021-09-16 [1] RSPM (R 4.4.0)
#> cowplot 1.1.3 2024-01-22 [1] RSPM (R 4.4.0)
#> crayon 1.5.3 2024-06-20 [1] RSPM (R 4.4.0)
#> curl 6.1.0 2025-01-06 [1] RSPM (R 4.4.0)
#> CVXR 1.0-15 2024-11-07 [1] RSPM (R 4.4.0)
#> data.table 1.16.4 2024-12-06 [1] RSPM (R 4.4.0)
#> DBI 1.2.3 2024-06-02 [1] RSPM (R 4.4.0)
#> dbplyr 2.5.0 2024-03-19 [1] RSPM (R 4.4.0)
#> dearseq 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> DECIPHER 3.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> decontam 1.26.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> DelayedArray 0.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> DelayedMatrixStats 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> deldir 2.0-4 2024-02-28 [1] RSPM (R 4.4.0)
#> DEoptimR 1.1-3-1 2024-11-23 [1] RSPM (R 4.4.0)
#> desc 1.4.3 2023-12-10 [1] RSPM (R 4.4.0)
#> DescTools 0.99.58 2024-11-08 [1] RSPM (R 4.4.0)
#> DESeq2 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> digest 0.6.37 2024-08-19 [1] RSPM (R 4.4.0)
#> directlabels 2024.1.21 2024-01-24 [1] RSPM (R 4.4.0)
#> DirichletMultinomial 1.48.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> doParallel 1.0.17 2022-02-07 [1] RSPM (R 4.4.0)
#> doRNG 1.8.6 2023-01-16 [1] RSPM (R 4.4.0)
#> dotCall64 1.2 2024-10-04 [1] RSPM (R 4.4.0)
#> dplyr * 1.1.4 2023-11-17 [1] RSPM (R 4.4.0)
#> e1071 1.7-16 2024-09-16 [1] RSPM (R 4.4.0)
#> edgeR 4.4.1 2024-12-02 [1] Bioconductor 3.20 (R 4.4.2)
#> ellipse 0.5.0 2023-07-20 [1] RSPM (R 4.4.0)
#> energy 1.7-12 2024-08-24 [1] RSPM (R 4.4.0)
#> evaluate 1.0.1 2024-10-10 [1] RSPM (R 4.4.0)
#> Exact 3.3 2024-07-21 [1] RSPM (R 4.4.0)
#> expm 1.0-0 2024-08-19 [1] RSPM (R 4.4.0)
#> farver 2.1.2 2024-05-13 [1] RSPM (R 4.4.0)
#> fastDummies 1.7.4 2024-08-16 [1] RSPM (R 4.4.0)
#> fastmap 1.2.0 2024-05-15 [1] RSPM (R 4.4.0)
#> fBasics 4041.97 2024-08-19 [1] RSPM (R 4.4.0)
#> filelock 1.0.3 2023-12-11 [1] RSPM (R 4.4.0)
#> fitdistrplus 1.2-2 2025-01-07 [1] RSPM (R 4.4.0)
#> forcats 1.0.0 2023-01-29 [1] RSPM (R 4.4.0)
#> foreach 1.5.2 2022-02-02 [1] RSPM (R 4.4.0)
#> foreign 0.8-87 2024-06-26 [2] CRAN (R 4.4.2)
#> Formula 1.2-5 2023-02-24 [1] RSPM (R 4.4.0)
#> formula.tools 1.7.1 2018-03-01 [1] RSPM (R 4.4.0)
#> fs 1.6.5 2024-10-30 [1] RSPM (R 4.4.0)
#> future 1.34.0 2024-07-29 [1] RSPM (R 4.4.0)
#> future.apply 1.11.3 2024-10-27 [1] RSPM (R 4.4.0)
#> genefilter 1.88.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> generics 0.1.3 2022-07-05 [1] RSPM (R 4.4.0)
#> GenomeInfoDb * 1.42.1 2024-11-28 [1] Bioconductor 3.20 (R 4.4.2)
#> GenomeInfoDbData 1.2.13 2025-01-09 [1] Bioconductor
#> GenomicRanges * 1.58.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> getopt 1.20.4 2023-10-01 [1] RSPM (R 4.4.0)
#> ggbeeswarm 0.7.2 2023-04-29 [1] RSPM (R 4.4.0)
#> ggdendro 0.2.0 2024-02-23 [1] RSPM (R 4.4.0)
#> ggfun 0.1.8 2024-12-03 [1] RSPM (R 4.4.0)
#> ggplot2 * 3.5.1 2024-04-23 [1] RSPM (R 4.4.0)
#> ggplotify 0.1.2 2023-08-09 [1] RSPM (R 4.4.0)
#> ggpubr * 0.6.0 2023-02-10 [1] RSPM (R 4.4.0)
#> ggrepel 0.9.6 2024-09-07 [1] RSPM (R 4.4.0)
#> ggridges 0.5.6 2024-01-23 [1] RSPM (R 4.4.0)
#> ggsignif 0.6.4 2022-10-13 [1] RSPM (R 4.4.0)
#> ggtree 3.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> gld 2.6.6 2022-10-23 [1] RSPM (R 4.4.0)
#> glmnet 4.1-8 2023-08-22 [1] RSPM (R 4.4.0)
#> globals 0.16.3 2024-03-08 [1] RSPM (R 4.4.0)
#> glue 1.8.0 2024-09-30 [1] RSPM (R 4.4.0)
#> gmp 0.7-5 2024-08-23 [1] RSPM (R 4.4.0)
#> goftest 1.2-3 2021-10-07 [1] RSPM (R 4.4.0)
#> gplots 3.2.0 2024-10-05 [1] RSPM (R 4.4.0)
#> gridExtra 2.3 2017-09-09 [1] RSPM (R 4.4.0)
#> gridGraphics 0.5-1 2020-12-13 [1] RSPM (R 4.4.0)
#> gsl 2.1-8 2023-01-24 [1] RSPM (R 4.4.0)
#> gtable 0.3.6 2024-10-25 [1] RSPM (R 4.4.0)
#> gtools 3.9.5 2023-11-20 [1] RSPM (R 4.4.0)
#> GUniFrac 1.8 2023-09-14 [1] RSPM (R 4.4.0)
#> haven 2.5.4 2023-11-30 [1] RSPM (R 4.4.0)
#> Hmisc 5.2-1 2024-12-02 [1] RSPM (R 4.4.0)
#> hms 1.1.3 2023-03-21 [1] RSPM (R 4.4.0)
#> htmlTable 2.4.3 2024-07-21 [1] RSPM (R 4.4.0)
#> htmltools 0.5.8.1 2024-04-04 [1] RSPM (R 4.4.0)
#> htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.4.0)
#> httpuv 1.6.15 2024-03-26 [1] RSPM (R 4.4.0)
#> httr 1.4.7 2023-08-15 [1] RSPM (R 4.4.0)
#> ica 1.0-3 2022-07-08 [1] RSPM (R 4.4.0)
#> igraph 2.1.3 2025-01-07 [1] RSPM (R 4.4.0)
#> inline 0.3.20 2024-11-10 [1] RSPM (R 4.4.0)
#> interp 1.1-6 2024-01-26 [1] RSPM (R 4.4.0)
#> IRanges * 2.40.1 2024-12-05 [1] Bioconductor 3.20 (R 4.4.2)
#> irlba 2.3.5.1 2022-10-03 [1] RSPM (R 4.4.0)
#> iterators 1.0.14 2022-02-05 [1] RSPM (R 4.4.0)
#> janeaustenr 1.0.0 2022-08-26 [1] RSPM (R 4.4.0)
#> jomo 2.7-6 2023-04-15 [1] RSPM (R 4.4.0)
#> jpeg 0.1-10 2022-11-29 [1] RSPM (R 4.4.0)
#> jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.4.0)
#> jsonlite 1.8.9 2024-09-20 [1] RSPM (R 4.4.0)
#> KEGGREST 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> KernSmooth 2.23-24 2024-05-17 [2] CRAN (R 4.4.2)
#> knitr 1.49 2024-11-08 [1] RSPM (R 4.4.0)
#> labeling 0.4.3 2023-08-29 [1] RSPM (R 4.4.0)
#> later 1.4.1 2024-11-27 [1] RSPM (R 4.4.0)
#> lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.2)
#> latticeExtra 0.6-30 2022-07-04 [1] RSPM (R 4.4.0)
#> lazyeval 0.2.2 2019-03-15 [1] RSPM (R 4.4.0)
#> lefser 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> leiden 0.4.3.1 2023-11-17 [1] RSPM (R 4.4.0)
#> libcoin 1.0-10 2023-09-27 [1] RSPM (R 4.4.0)
#> lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.4.0)
#> limma 3.62.1 2024-11-03 [1] Bioconductor 3.20 (R 4.4.2)
#> listenv 0.9.1 2024-01-29 [1] RSPM (R 4.4.0)
#> lme4 1.1-35.5 2024-07-03 [1] RSPM (R 4.4.0)
#> lmerTest 3.1-3 2020-10-23 [1] RSPM (R 4.4.0)
#> lmom 3.2 2024-09-30 [1] RSPM (R 4.4.0)
#> lmtest 0.9-40 2022-03-21 [1] RSPM (R 4.4.0)
#> locfit 1.5-9.10 2024-06-24 [1] RSPM (R 4.4.0)
#> logistf 1.26.0 2023-08-18 [1] RSPM (R 4.4.0)
#> lpSolve 5.6.23 2024-12-14 [1] RSPM (R 4.4.0)
#> Maaslin2 1.19.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.4.0)
#> MASS 7.3-61 2024-06-13 [2] CRAN (R 4.4.2)
#> MAST 1.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> Matrix 1.7-1 2024-10-18 [2] CRAN (R 4.4.2)
#> MatrixGenerics * 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> MatrixModels 0.5-3 2023-11-06 [1] RSPM (R 4.4.0)
#> matrixStats * 1.5.0 2025-01-07 [1] RSPM (R 4.4.0)
#> mediation 4.5.0 2019-10-08 [1] RSPM (R 4.4.0)
#> memoise 2.0.1 2021-11-26 [1] RSPM (R 4.4.0)
#> metagenomeSeq 1.47.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
#> mgcv 1.9-1 2023-12-21 [2] CRAN (R 4.4.2)
#> MGLM 0.2.1 2022-04-13 [1] RSPM (R 4.4.0)
#> mia * 1.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> mice 3.17.0 2024-11-27 [1] RSPM (R 4.4.0)
#> microbiome 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> MicrobiomeBenchmarkData * 1.8.0 2024-10-31 [1] Bioconductor 3.20 (R 4.4.2)
#> MicrobiomeBenchmarkDataAnalyses * 0.99.21 2025-01-09 [1] local
#> MicrobiomeStat 1.2 2024-04-01 [1] RSPM (R 4.4.0)
#> mime 0.12 2021-09-28 [1] RSPM (R 4.4.0)
#> miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.4.0)
#> minqa 1.2.8 2024-08-17 [1] RSPM (R 4.4.0)
#> mitml 0.4-5 2023-03-08 [1] RSPM (R 4.4.0)
#> mitools 2.4 2019-04-26 [1] RSPM (R 4.4.0)
#> mixOmics 6.30.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> modeest 2.4.0 2019-11-18 [1] RSPM (R 4.4.0)
#> modeltools 0.2-23 2020-03-05 [1] RSPM (R 4.4.0)
#> multcomp 1.4-26 2024-07-18 [1] RSPM (R 4.4.0)
#> MultiAssayExperiment * 1.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> multtest 2.62.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> munsell 0.5.1 2024-04-01 [1] RSPM (R 4.4.0)
#> mvtnorm 1.3-2 2024-11-04 [1] RSPM (R 4.4.0)
#> NADA 1.6-1.1 2020-03-22 [1] RSPM (R 4.4.0)
#> nlme 3.1-166 2024-08-14 [2] CRAN (R 4.4.2)
#> nloptr 2.1.1 2024-06-25 [1] RSPM (R 4.4.0)
#> nnet 7.3-19 2023-05-03 [2] CRAN (R 4.4.2)
#> NOISeq 2.50.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> numDeriv 2016.8-1.1 2019-06-06 [1] RSPM (R 4.4.0)
#> operator.tools 1.6.3 2017-02-28 [1] RSPM (R 4.4.0)
#> optparse 1.7.5 2024-04-16 [1] RSPM (R 4.4.0)
#> pan 1.9 2023-12-07 [1] RSPM (R 4.4.0)
#> parallelly 1.41.0 2024-12-18 [1] RSPM (R 4.4.0)
#> patchwork 1.3.0 2024-09-16 [1] RSPM (R 4.4.0)
#> pbapply 1.7-2 2023-06-27 [1] RSPM (R 4.4.0)
#> pcaPP 2.0-5 2024-08-19 [1] RSPM (R 4.4.0)
#> permute 0.9-7 2022-01-27 [1] RSPM (R 4.4.0)
#> phyloseq * 1.50.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> pillar 1.10.1 2025-01-07 [1] RSPM (R 4.4.0)
#> pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.4.0)
#> pkgdown 2.1.1 2024-09-17 [1] RSPM (R 4.4.0)
#> plotly 4.10.4 2024-01-13 [1] RSPM (R 4.4.0)
#> plyr 1.8.9 2023-10-02 [1] RSPM (R 4.4.0)
#> png 0.1-8 2022-11-29 [1] RSPM (R 4.4.0)
#> polyclip 1.10-7 2024-07-23 [1] RSPM (R 4.4.0)
#> prettyunits 1.2.0 2023-09-24 [1] RSPM (R 4.4.0)
#> progress 1.2.3 2023-12-06 [1] RSPM (R 4.4.0)
#> progressr 0.15.1 2024-11-22 [1] RSPM (R 4.4.0)
#> promises 1.3.2 2024-11-28 [1] RSPM (R 4.4.0)
#> proxy 0.4-27 2022-06-09 [1] RSPM (R 4.4.0)
#> purrr * 1.0.2 2023-08-10 [1] RSPM (R 4.4.0)
#> quadprog 1.5-8 2019-11-20 [1] RSPM (R 4.4.0)
#> quantreg 5.99.1 2024-11-22 [1] RSPM (R 4.4.0)
#> R6 2.5.1 2021-08-19 [1] RSPM (R 4.4.0)
#> ragg 1.3.3 2024-09-11 [1] RSPM (R 4.4.0)
#> RANN 2.6.2 2024-08-25 [1] RSPM (R 4.4.0)
#> rARPACK 0.11-0 2016-03-10 [1] RSPM (R 4.4.0)
#> rbibutils 2.3 2024-10-04 [1] RSPM (R 4.4.0)
#> rbiom 1.0.3 2021-11-05 [1] RSPM (R 4.4.0)
#> RColorBrewer 1.1-3 2022-04-03 [1] RSPM (R 4.4.0)
#> Rcpp 1.0.13-1 2024-11-02 [1] RSPM (R 4.4.0)
#> RcppAnnoy 0.0.22 2024-01-23 [1] RSPM (R 4.4.0)
#> RcppHNSW 0.6.0 2024-02-04 [1] RSPM (R 4.4.0)
#> RcppParallel 5.1.9 2024-08-19 [1] RSPM (R 4.4.0)
#> RcppZiggurat 0.1.6 2020-10-20 [1] RSPM (R 4.4.0)
#> Rdpack 2.6.2 2024-11-15 [1] RSPM (R 4.4.0)
#> readxl 1.4.3 2023-07-06 [1] RSPM (R 4.4.0)
#> reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.4.0)
#> reticulate 1.40.0 2024-11-15 [1] RSPM (R 4.4.0)
#> Rfast 2.1.3 2024-12-31 [1] RSPM (R 4.4.0)
#> rhdf5 2.50.1 2024-12-09 [1] Bioconductor 3.20 (R 4.4.2)
#> rhdf5filters 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> Rhdf5lib 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> rlang 1.1.4 2024-06-04 [1] RSPM (R 4.4.0)
#> rmarkdown 2.29 2024-11-04 [1] RSPM (R 4.4.0)
#> Rmpfr 1.0-0 2024-11-18 [1] RSPM (R 4.4.0)
#> rmutil 1.1.10 2022-10-27 [1] RSPM (R 4.4.0)
#> rngtools 1.5.2 2021-09-20 [1] RSPM (R 4.4.0)
#> robustbase 0.99-4-1 2024-09-27 [1] RSPM (R 4.4.0)
#> ROCR 1.0-11 2020-05-02 [1] RSPM (R 4.4.0)
#> rootSolve 1.8.2.4 2023-09-21 [1] RSPM (R 4.4.0)
#> rpart 4.1.23 2023-12-05 [2] CRAN (R 4.4.2)
#> RSpectra 0.16-2 2024-07-18 [1] RSPM (R 4.4.0)
#> RSQLite 2.3.9 2024-12-03 [1] RSPM (R 4.4.0)
#> rstatix 0.7.2 2023-02-01 [1] RSPM (R 4.4.0)
#> rstudioapi 0.17.1 2024-10-22 [1] RSPM (R 4.4.0)
#> rsvd 1.0.5 2021-04-16 [1] RSPM (R 4.4.0)
#> Rtsne 0.17 2023-12-07 [1] RSPM (R 4.4.0)
#> S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> sandwich 3.1-1 2024-09-15 [1] RSPM (R 4.4.0)
#> sass 0.4.9 2024-03-15 [1] RSPM (R 4.4.0)
#> ScaledMatrix 1.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> scales 1.3.0 2023-11-28 [1] RSPM (R 4.4.0)
#> scater 1.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> scattermore 1.2 2023-06-12 [1] RSPM (R 4.4.0)
#> sctransform 0.4.1 2023-10-19 [1] RSPM (R 4.4.0)
#> scuttle 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.4.0)
#> Seurat 5.1.0 2024-05-10 [1] RSPM (R 4.4.0)
#> SeuratObject 5.0.2 2024-05-08 [1] RSPM (R 4.4.0)
#> shape 1.4.6.1 2024-02-23 [1] RSPM (R 4.4.0)
#> shiny 1.10.0 2024-12-14 [1] RSPM (R 4.4.0)
#> SingleCellExperiment * 1.28.1 2024-11-10 [1] Bioconductor 3.20 (R 4.4.2)
#> slam 0.1-55 2024-11-13 [1] RSPM (R 4.4.0)
#> SnowballC 0.7.1 2023-04-25 [1] RSPM (R 4.4.0)
#> softImpute 1.4-1 2021-05-09 [1] RSPM (R 4.4.0)
#> sp 2.1-4 2024-04-30 [1] RSPM (R 4.4.0)
#> spam 2.11-0 2024-10-03 [1] RSPM (R 4.4.0)
#> SparseArray 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> SparseM 1.84-2 2024-07-17 [1] RSPM (R 4.4.0)
#> sparseMatrixStats 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> spatial 7.3-17 2023-07-20 [2] CRAN (R 4.4.2)
#> spatstat.data 3.1-4 2024-11-15 [1] RSPM (R 4.4.0)
#> spatstat.explore 3.3-3 2024-10-22 [1] RSPM (R 4.4.0)
#> spatstat.geom 3.3-4 2024-11-18 [1] RSPM (R 4.4.0)
#> spatstat.random 3.3-2 2024-09-18 [1] RSPM (R 4.4.0)
#> spatstat.sparse 3.1-0 2024-06-21 [1] RSPM (R 4.4.0)
#> spatstat.univar 3.1-1 2024-11-05 [1] RSPM (R 4.4.0)
#> spatstat.utils 3.1-2 2025-01-08 [1] RSPM (R 4.4.0)
#> stable 1.1.6 2022-03-02 [1] RSPM (R 4.4.0)
#> stabledist 0.7-2 2024-08-17 [1] RSPM (R 4.4.0)
#> statip 0.2.3 2019-11-17 [1] RSPM (R 4.4.0)
#> statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0)
#> stringi 1.8.4 2024-05-06 [1] RSPM (R 4.4.0)
#> stringr 1.5.1 2023-11-14 [1] RSPM (R 4.4.0)
#> SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> survey 4.4-2 2024-03-20 [1] RSPM (R 4.4.0)
#> survival 3.8-3 2024-12-17 [2] RSPM (R 4.4.0)
#> systemfonts 1.1.0 2024-05-15 [1] RSPM (R 4.4.0)
#> tensor 1.5 2012-05-05 [1] RSPM (R 4.4.0)
#> testthat 3.2.2 2024-12-10 [1] RSPM (R 4.4.0)
#> textshaping 0.4.1 2024-12-06 [1] RSPM (R 4.4.0)
#> TH.data 1.1-2 2023-04-17 [1] RSPM (R 4.4.0)
#> tibble 3.2.1 2023-03-20 [1] RSPM (R 4.4.0)
#> tidyr 1.3.1 2024-01-24 [1] RSPM (R 4.4.0)
#> tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0)
#> tidytext 0.4.2 2024-04-10 [1] RSPM (R 4.4.0)
#> tidytree 0.4.6 2023-12-12 [1] RSPM (R 4.4.0)
#> timeDate 4041.110 2024-09-22 [1] RSPM (R 4.4.0)
#> timeSeries 4041.111 2024-09-22 [1] RSPM (R 4.4.0)
#> tokenizers 0.3.0 2022-12-22 [1] RSPM (R 4.4.0)
#> treeio 1.30.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> TreeSummarizedExperiment * 2.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> truncnorm 1.0-9 2023-03-20 [1] RSPM (R 4.4.0)
#> UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> utf8 1.2.4 2023-10-22 [1] RSPM (R 4.4.0)
#> uwot 0.2.2 2024-04-21 [1] RSPM (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.4.0)
#> vegan 2.6-8 2024-08-28 [1] RSPM (R 4.4.0)
#> vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0)
#> viridis 0.6.5 2024-01-29 [1] RSPM (R 4.4.0)
#> viridisLite 0.4.2 2023-05-02 [1] RSPM (R 4.4.0)
#> withr 3.0.2 2024-10-28 [1] RSPM (R 4.4.0)
#> Wrench 1.24.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> xfun 0.50 2025-01-07 [1] RSPM (R 4.4.0)
#> XML 3.99-0.18 2025-01-01 [1] RSPM (R 4.4.0)
#> xtable 1.8-4 2019-04-21 [1] RSPM (R 4.4.0)
#> XVector * 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> yaml 2.3.10 2024-07-26 [1] RSPM (R 4.4.0)
#> yulab.utils 0.1.9 2025-01-07 [1] RSPM (R 4.4.0)
#> zCompositions 1.5.0-4 2024-06-19 [1] RSPM (R 4.4.0)
#> zinbwave 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> ZINQ 2.0 2025-01-09 [1] Github (wdl2459/ZINQ-v2@40391a6)
#> zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.2)
#> zoo 1.8-12 2023-04-13 [1] RSPM (R 4.4.0)
#>
#> [1] /usr/local/lib/R/site-library
#> [2] /usr/local/lib/R/library
#>
#> ──────────────────────────────────────────────────────────────────────────────