Enrichment with dbBact approach

Setup

library(bugphyzzAnalyses)
library(bugsigdbr)
library(bugphyzz)
library(purrr)
library(dplyr)
library(stringr)
library(tidyr)
library(ComplexHeatmap)
library(ggplot2)

## Variables
body_sites <- c(
    skin = "skin", vagina = "vagina", mouth = "mouth", feces = "feces"
)
ranks <- c(genus = "genus", species = "species")
directions <- c(increased = "increased", decreased = "decreased")

perm_var <- 1000
freq_var <- 1

Data

Import bugphyzz and create signatures at the genus and species levels:

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
    })
bpSigs_g <- map(bp, ~ {
    makeSignatures(
        dat = .x,  taxIdType = "NCBI_ID", taxLevel = "genus",
        minSize = 10
    )
}) |> 
    list_flatten(name_spec = "{inner}") |> 
    discard(is.null)

bpSigs_s <- map(bp, ~ {
    makeSignatures(
        dat = .x,  taxIdType = "NCBI_ID", taxLevel = "species",
        minSize = 10
    )
}) |> 
    list_flatten(name_spec = "{inner}") |> 
    discard(is.null)

Import BugSigDB:

bsdb_doi <- "10.5281/zenodo.10627578" # v1.2.1
bsdb <- importBugSigDB(version = bsdb_doi)
bsdb <- bsdb |> 
    filter(`Host species` == "Homo sapiens") |> 
    filter(`Abundance in Group 1` %in% c("increased", "decreased")) |>
    filter(!is.na(`Body site`)) |> 
    mutate(exp = sub("^(bsdb:\\d+/\\d+)/\\d+", "\\1", `BSDB ID`))
dim(bsdb)
#> [1] 3242   51

Subset by body site:

uberon <- getOntology(onto = "uberon")
#> Loading required namespace: ontologyIndex

bsdb_subsets_by_bodysite <- vector("list", length(body_sites))
names(bsdb_subsets_by_bodysite) <- body_sites
for (i in seq_along(bsdb_subsets_by_bodysite)) {
    if (body_sites[i] == "skin") { ## Didn't find an ontology for skin
        bsdb_subsets_by_bodysite[[i]] <- bsdb |> 
            filter(grepl(body_sites[i], `Body site`, ignore.case = TRUE))
    } else {
        bsdb_subsets_by_bodysite[[i]] <- subsetByOntology(
            bsdb, column = "Body site", term = body_sites[i], ontology = uberon 
        )
    }
}

dats <- map(bsdb_subsets_by_bodysite, filterEvenDat)
map_int(dats, ~ nrow(.x$decreased)) == map_int(dats, ~ nrow(.x$increased))
#>   skin vagina  mouth  feces 
#>   TRUE   TRUE   TRUE   TRUE

Table summary 1

nexp_summary <- dats |> 
    map(~ {
        .x$increased |> 
            count(Condition, name = "nexp") |> 
            filter(nexp > 5)
    }) |> 
    bind_rows(.id = "bsite") |> 
    group_by(bsite) |> 
    slice_max(order_by = nexp, n = 10) |> 
    ungroup()
myDataTable(nexp_summary)

row_dat_list <- list()

Obesity - feces

obs_dat <- dats$feces$increased |> # It doesn't matter here if it's increased or decreased
    filter(Condition == "Obesity")

## Got these experiments with manual inspection of the data.frame
obs_exps = c(
    "bsdb:101/1",
    "bsdb:149/1",
    "bsdb:33/1",
    "bsdb:338/1",
    "bsdb:338/2",
    "bsdb:345/1",
    "bsdb:347/1",
    "bsdb:348/1",
    "bsdb:348/3",
    "bsdb:350/1",
    "bsdb:350/2",
    "bsdb:351/1",
    "bsdb:354/1",
    "bsdb:355/1",
    "bsdb:36/5",
    "bsdb:362/1",
    "bsdb:367/1",
    "bsdb:369/1",
    "bsdb:371/3",
    "bsdb:373/1",
    "bsdb:375/1",
    "bsdb:44/1",
    "bsdb:48/1",
    "bsdb:48/2",
    "bsdb:68/1",
    "bsdb:68/2",
    "bsdb:68/3",
    "bsdb:68/4",
    "bsdb:68/5",
    "bsdb:68/6",
    "bsdb:70/1",
    "bsdb:70/2",
    "bsdb:70/3",
    "bsdb:73/1",
    "bsdb:73/2",
    "bsdb:732/1",
    "bsdb:74/1",
    "bsdb:74/3",
    "bsdb:847/1",
    "bsdb:96/1",
    "bsdb:99/1"  
    
)
obs_dat_inc <- dats$feces$increased |> 
    filter(exp %in% obs_exps)
obs_dat_dec <- dats$feces$decreased |> 
    filter(exp %in% obs_exps)

length(obs_exps)
#> [1] 41

obs_sigs_s <- getPairedSigs(obs_dat_dec, obs_dat_inc, rank = "species", cat = TRUE)
obs_sigs_g <- getPairedSigs(obs_dat_dec, obs_dat_inc, rank = "genus", cat = TRUE)

Enrichment at the species level:

obs_feces_enriched_s <- dbEn2(
    obs_sigs_s$dec, obs_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
#> Registered S3 method overwritten by 'hoardr':
#>   method           from
#>   print.cache_info httr
#> Registered S3 methods overwritten by 'readr':
#>   method                    from 
#>   as.data.frame.spec_tbl_df vroom
#>   as_tibble.spec_tbl_df     vroom
#>   format.col_spec           vroom
#>   print.col_spec            vroom
#>   print.collector           vroom
#>   print.date_names          vroom
#>   print.locale              vroom
#>   str.col_spec              vroom

row_dat_list$obs_feces_sp <- obs_feces_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "Obesity",
        Rank = "species"
    )
dbHt(obs_feces_enriched_s, scm = TRUE)

obs_feces_enriched_g <- dbEn2(
    obs_sigs_g$dec, obs_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()

row_dat_list$obs_feces_gn <- obs_feces_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "Obesity",
        Rank = "genus"
    )
dbHt(obs_feces_enriched_g, scm = FALSE, col_pad = 20)

Parkinson’s disease - feces

pd <- dats$feces$increased |> 
    filter(Condition == "Parkinson's disease")
# pd |> 
#     relocate(exp, `Group 0 name`, `Group 1 name`) |> 
#     View()

pd_exps <- c(
    "bsdb:22/1",
    "bsdb:508/1",
    "bsdb:533/1",
    "bsdb:533/2",
    # "bsdb:537/1",
    # "bsdb:537/2",
    # "bsdb:537/3",
    # "bsdb:537/4",
    # "bsdb:537/6",
    "bsdb:537/7",
    "bsdb:537/8",
    "bsdb:554/1",
    "bsdb:560/1",
    "bsdb:568/1",
    # "bsdb:568/3",
    "bsdb:600/1",
    "bsdb:604/1",
    "bsdb:604/2",
    "bsdb:605/1",
    # "bsdb:605/2",
    "bsdb:607/1",
    "bsdb:607/2",
    "bsdb:716/1",
    # "bsdb:716/2",
    # "bsdb:716/3",
    # "bsdb:716/4",
    "bsdb:717/1",
    # "bsdb:717/2",
    "bsdb:717/3",
    # "bsdb:717/4",
    # "bsdb:717/5",
    # "bsdb:717/6",
    "bsdb:719/3",
    "bsdb:722/1",
    "bsdb:723/1",
    "bsdb:723/3",
    "bsdb:723/4",
    "bsdb:723/5",
    "bsdb:731/1",
    "bsdb:731/2",
    "bsdb:731/4",
    # "bsdb:741/4",
    "bsdb:742/1",
    "bsdb:743/1",
    "bsdb:746/1"
)
pd_dat_inc <- dats$feces$increased |> 
    filter(exp %in% pd_exps)
pd_dat_dec <- dats$feces$decreased |> 
    filter(exp %in% pd_exps)
length(pd_exps)
#> [1] 30

Get signatures:

pd_sigs_s <- getPairedSigs(
    pd_dat_dec, pd_dat_inc, rank = "species", cat = TRUE
)
pd_sigs_g <- getPairedSigs(
    pd_dat_dec, pd_dat_inc, rank = "genus", cat = TRUE
)

Enrichment at the species level:

pd_feces_enriched_s <- dbEn2(
    pd_sigs_s$dec, pd_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()

row_dat_list$pd_feces_sp <- pd_feces_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "pd",
        Rank = "species"
    )
dbHt(pd_feces_enriched_s, scm = FALSE, col_pad = 20)

Enrichment at the genus level:

pd_feces_enriched_g <- dbEn2(
    pd_sigs_g$dec, pd_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$pd_feces_gn <- pd_feces_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "pd",
        Rank = "genus"
    )
dbHt(pd_feces_enriched_g, scm = FALSE, col_pad = 20)

Colorectal cancer - feces

Get experiments and BSDB rows

crc_exps <- crc_dat <- dats$feces$increased |> # It doesn't matter here if it's increased or decreased
    filter(Condition == "Colorectal cancer") |>
    filter(
        grepl("control", `Group 0 name`, ignore.case = TRUE) &
            grepl("(colorectal cancer|crc)", `Group 1 name`, ignore.case = TRUE)
    ) |> 
    pull(exp)
crc_dat_inc <- dats$feces$increased |> 
    filter(exp %in% crc_exps)
crc_dat_dec <- dats$feces$decreased |> 
    filter(exp %in% crc_exps)
length(crc_exps)
#> [1] 22

Get signatures:

crc_sigs_s <- getPairedSigs(
    crc_dat_dec, crc_dat_inc, rank = "species", cat = TRUE
)
crc_sigs_g <- getPairedSigs(
    crc_dat_dec, crc_dat_inc, rank = "genus", cat = TRUE
)

Enrichment at the species level:

crc_feces_enriched_s <- dbEn2(
    crc_sigs_s$dec, crc_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()

row_dat_list$crc_feces_sp <- crc_feces_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "crc",
        Rank = "species"
    )
dbHt(crc_feces_enriched_s, scm = FALSE, col_pad = 20)

Enrichment at the genus level:

crc_feces_enriched_g <- dbEn2(
    crc_sigs_g$dec, crc_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$crc_feces_gn <- crc_feces_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "crc",
        Rank = "genus"
    )
dbHt(crc_feces_enriched_g, scm = FALSE, col_pad = 20)

Antimicrobial agent - feces

Interesting, but many different antibiotics are provided. It would have been more interesting if vancomycin would be among them.

aa_dat <- dats$feces$increased |> 
    filter(Condition == "Antimicrobial agent")
# aa_dat |>
#     relocate(exp, `Group 0 name`, `Group 1 name`) |>
#     View()
stringr::str_extract(aa_dat$`Group 1 definition`, "\\b\\w+in\\b")
#>  [1] NA               NA               NA               NA              
#>  [5] NA               NA               NA               NA              
#>  [9] NA               NA               NA               NA              
#> [13] NA               NA               NA               NA              
#> [17] NA               "penicillin"     "piperacillin"   NA              
#> [21] NA               NA               "levofloxacin"   "ciprofloxacin" 
#> [25] "ampicillin"     "ampicillin"     "Rifaximin"      "Rifaximin"     
#> [29] "within"         NA               "ciprofloxacin"  "nitrofurantoin"
#> [33] NA               "azithromycin"   "azithromycin"   "azithromycin"  
#> [37] NA

COVID-19 - feces

covid_dat <- dats$feces$increased |> # It doesn't matter here if it's increased or decreased
    filter(Condition == "COVID-19")

## Manual inspection
covid_exps <- c(
    "bsdb:734/1",
    "bsdb:428/1",
    "bsdb:441/1",
    "bsdb:464/3",
    "bsdb:481/1",
    "bsdb:486/1",
    "bsdb:486/2",
    "bsdb:486/3",
    "bsdb:487/3",
    "bsdb:496/1",
    "bsdb:511/4",
    "bsdb:511/5",
    "bsdb:453/4",
    "bsdb:782/3"
)

covid_dat_inc <- dats$feces$increased |> 
    filter(exp %in% covid_exps)
covid_dat_dec <- dats$feces$decreased |> 
    filter(exp %in% covid_exps)

Concatenate signatures:

covid_sigs_s <- getPairedSigs(covid_dat_dec, covid_dat_inc, rank = "species", cat = TRUE)
covid_sigs_g <- getPairedSigs(covid_dat_dec, covid_dat_inc, rank = "genus", cat = TRUE)

Enrichment at the species level:

covid_feces_enriched_s <- dbEn2(
    covid_sigs_s$dec, covid_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()

row_dat_list$covid_feces_sp <- covid_feces_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "covid",
        Rank = "species"
    )
dbHt(covid_feces_enriched_s, scm = FALSE)

covid_feces_enriched_g <- dbEn2(
    covid_sigs_g$dec, covid_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
#> Warning in taxizedb::taxid2name(Taxon, db = "ncbi", verbose = FALSE): No name
#> found for 2 of 137 taxon IDs

row_dat_list$covid_feces_gn <- covid_feces_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "covid",
        Rank = "genus"
    )
dbHt(covid_feces_enriched_g, scm = FALSE)

IBS - feces

ibs_dat <- dats$feces$increased |> # It doesn't matter here if it's increased or decreased
        filter(Condition == "Irritable bowel syndrome")
ibs_exps <- ibs_dat |> 
    pull(exp)
ibs_dat_inc <- dats$feces$increased |> 
    filter(exp %in% ibs_exps)
ibs_dat_dec <- dats$feces$decreased |> 
    filter(exp %in% ibs_exps)

# ibs_dat |> 
#     relocate(exp, `Group 0 name`, `Group 1 name`) |> 
#     View()

ibs_sigs_s <- getPairedSigs(
    ibs_dat_dec, ibs_dat_inc, rank = "species", cat = TRUE
)
ibs_sigs_g <- getPairedSigs(
    ibs_dat_dec, ibs_dat_inc, rank = "genus", cat = TRUE
)

ibs_feces_enriched_s <- dbEn2(
    ibs_sigs_s$dec, ibs_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$ibs_feces_sp <- ibs_feces_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "ibs",
        Rank = "species"
    )
dbHt(ibs_feces_enriched_s, scm = FALSE, col_pad = 20)

ibs_feces_enriched_g <- dbEn2(
    ibs_sigs_g$dec, ibs_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$ibs_feces_gn <- ibs_feces_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "feces",
        Condition = "ibs",
        Rank = "genus"
    )
dbHt(ibs_feces_enriched_g, scm = FALSE, col_pad = 20)

Smoking - mouth

smo_dat <- dats$mouth$increased |> # It doesn't matter here if it's increased or decreased
        filter(Condition == "Smoking behavior")
smo_exps <- smo_dat |> 
    filter(exp != "bsdb:368/1") |> 
    pull(exp)
smo_dat_inc <- dats$mouth$increased |> 
    filter(exp %in% smo_exps)
smo_dat_dec <- dats$mouth$decreased |> 
    filter(exp %in% smo_exps)

Get concatenated signatures:

smo_sigs_s <- getPairedSigs(
    smo_dat_dec, smo_dat_inc, rank = "species", cat = TRUE
)
smo_sigs_g <- getPairedSigs(
    smo_dat_dec, smo_dat_inc, rank = "genus", cat = TRUE
)

smo_mouth_enriched_s <- dbEn2(
    smo_sigs_s$dec, smo_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
#> Warning in taxizedb::taxid2name(Taxon, db = "ncbi", verbose = FALSE): No name
#> found for 1 of 75 taxon IDs

row_dat_list$smo_mouth_sp <- smo_mouth_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "mouth",
        Condition = "smoking",
        Rank = "species"
    )
dbHt(smo_mouth_enriched_s, scm = FALSE, col_pad = 20)

smo_mouth_enriched_g <- dbEn2(
    smo_sigs_g$dec, smo_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$smo_mouth_gn <- smo_mouth_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "mouth",
        Condition = "smoking",
        Rank = "genus"
    )
dbHt(smo_mouth_enriched_g, scm = FALSE, col_pad = 20)

Human papilloma virus infection - vagina

hpv_dat <- dats$vagina$increased |> 
    filter(Condition == "Human papilloma virus infection") |> 
    filter(grepl("HPV\\+",  `Group 1 name`))

hpv_exps <- hpv_dat |> 
    pull(exp)

hpv_dat_inc <- dats$vagina$increased |> 
    filter(exp %in% hpv_exps)
hpv_dat_dec <- dats$vagina$decreased |> 
    filter(exp %in% hpv_exps)

# hpv_dat |>
#     relocate(exp, `Group 0 name`, `Group 1 name`) |>
#     View()

Get concatenated signatures:

hpv_sigs_s <- getPairedSigs(
    hpv_dat_dec, hpv_dat_inc, rank = "species", cat = TRUE
)
hpv_sigs_g <- getPairedSigs(
    hpv_dat_dec, hpv_dat_inc, rank = "genus", cat = TRUE
)

Enrichment at the species level:

hpv_vagina_enriched_s <- dbEn2(
    hpv_sigs_s$dec, hpv_sigs_s$inc, term_list = bpSigs_s,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$hpv_vagina_sp <- hpv_vagina_enriched_s |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "vagina",
        Condition = "hpv",
        Rank = "species"
    )
dbHt(hpv_vagina_enriched_s, scm = TRUE)

Enrichment at the genus level:

hpv_vagina_enriched_g <- dbEn2(
    hpv_sigs_g$dec, hpv_sigs_g$inc, term_list = bpSigs_g,
    perm = perm_var, freq = 1
) |> 
    filterDbEnRes()
row_dat_list$hpv_vagina_gn <- hpv_vagina_enriched_g |> 
    rowData() |> 
    as.data.frame() |> 
    tibble::rownames_to_column(var = "bp_sig") |> 
    as_tibble() |> 
    mutate(
        Bsite = "vagina",
        Condition = "hpv",
        Rank = "genus"
    )
dbHt(hpv_vagina_enriched_g, scm = TRUE)

Summary table

sum_tbl <- row_dat_list |> 
    bind_rows() |> 
    # select(-PermP) |> 
    mutate(
        P_value = round(P_value, 3),
        FDR = round(FDR, 3),
        Effect_size = round(Effect_size, 2)
    )
    # mutate(
    #     FDR = p.adjust(P_value, method = "fdr"),
    #     PermP_FDR = p.adjust(PermP, method = "fdr")
    # ) |> 
    # filter(P_value < 0.1)
myDataTable(sum_tbl)

sum_tbl |> 
    ggplot(aes(P_value)) +
    geom_histogram(binwidth = 0.1)

sum_tbl |> 
    ggplot(aes(P_value)) +
    geom_histogram(binwidth = 0.1) +
    facet_grid(Condition ~ Rank) +
    scale_y_continuous(breaks = seq(0, 8, 2))

Summary table P-value < 0.1

sum_tbl2 <- sum_tbl |> 
    filter(P_value < 0.1) |> 
    arrange(Bsite, Condition, Rank, Direction, P_value) |>
    mutate(Direction = factor(Direction, levels = c("Control", "Case"))) |> 
    relocate(
        `Body site` = Bsite, Condition, Rank, Direction, `P-value` = P_value,
        `Effect size` = Effect_size, `Signature` = bp_sig
    ) |> 
    mutate(
        Signature = case_when(
            `P-value` < 0.05 ~ paste0(Signature, "**"),
            # `P-value` < 0.5 ~ paste0(Signature, "**"),
            TRUE ~ paste0(Signature, "*")
        )
    ) |> 
    select(-PermP, - FDR)
sum_tbl2 |> 
    {\(y) myDataTable(y, page_len = nrow(y))}()

sum_tbl2 |> 
    count(`Body site`, Condition, Rank) |> 
    knitr::kable()

Body site	Condition	Rank	n
feces	Obesity	genus	1
feces	Obesity	species	7
feces	covid	genus	3
feces	covid	species	7
feces	crc	genus	3
feces	crc	species	6
feces	ibs	species	3
feces	pd	species	4
mouth	smoking	genus	2
mouth	smoking	species	3
vagina	hpv	genus	1

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     2024-06-28
#>  pandoc   3.2 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#>  Biobase              * 2.64.0  2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
#>  BiocFileCache          2.12.0  2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
#>  BiocGenerics         * 0.50.0  2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
#>  bit                    4.0.5   2022-11-15 [1] RSPM (R 4.4.0)
#>  bit64                  4.0.5   2020-08-30 [1] RSPM (R 4.4.0)
#>  blob                   1.2.4   2023-03-17 [1] RSPM (R 4.4.0)
#>  bslib                  0.7.0   2024-03-29 [1] RSPM (R 4.4.0)
#>  bugphyzz             * 0.99.3  2024-06-28 [1] Github (waldronlab/bugphyzz@8fc1c6d)
#>  bugphyzzAnalyses     * 0.1.19  2024-06-28 [1] local
#>  bugsigdbr            * 1.10.0  2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
#>  cachem                 1.1.0   2024-05-16 [1] RSPM (R 4.4.0)
#>  Cairo                  1.6-2   2023-11-28 [1] RSPM (R 4.4.0)
#>  circlize               0.4.16  2024-02-20 [1] RSPM (R 4.4.0)
#>  cli                    3.6.3   2024-06-21 [1] RSPM (R 4.4.0)
#>  clue                   0.3-65  2023-09-23 [1] RSPM (R 4.4.0)
#>  cluster                2.1.6   2023-12-01 [2] CRAN (R 4.4.1)
#>  codetools              0.2-20  2024-03-31 [2] CRAN (R 4.4.1)
#>  colorspace             2.1-0   2023-01-23 [1] RSPM (R 4.4.0)
#>  ComplexHeatmap       * 2.20.0  2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
#>  crayon                 1.5.3   2024-06-20 [1] RSPM (R 4.4.0)
#>  crosstalk              1.2.1   2023-11-23 [1] RSPM (R 4.4.0)
#>  curl                   5.2.1   2024-03-01 [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)
#>  DelayedArray           0.30.1  2024-05-07 [1] Bioconductor 3.19 (R 4.4.1)
#>  desc                   1.4.3   2023-12-10 [1] RSPM (R 4.4.0)
#>  digest                 0.6.36  2024-06-23 [1] RSPM (R 4.4.0)
#>  doParallel             1.0.17  2022-02-07 [1] RSPM (R 4.4.0)
#>  dplyr                * 1.1.4   2023-11-17 [1] RSPM (R 4.4.0)
#>  DT                     0.33    2024-04-04 [1] RSPM (R 4.4.0)
#>  evaluate               0.24.0  2024-06-10 [1] RSPM (R 4.4.0)
#>  fansi                  1.0.6   2023-12-08 [1] RSPM (R 4.4.0)
#>  farver                 2.1.2   2024-05-13 [1] RSPM (R 4.4.0)
#>  fastmap                1.2.0   2024-05-15 [1] RSPM (R 4.4.0)
#>  filelock               1.0.3   2023-12-11 [1] RSPM (R 4.4.0)
#>  foreach                1.5.2   2022-02-02 [1] RSPM (R 4.4.0)
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#>  hoardr                 0.5.4   2024-01-23 [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)
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#>  pkgconfig              2.0.3   2019-09-22 [1] RSPM (R 4.4.0)
#>  pkgdown                2.0.9   2024-04-18 [1] RSPM (R 4.4.0)
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#>  purrr                * 1.0.2   2023-08-10 [1] RSPM (R 4.4.0)
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#>  ragg                   1.3.2   2024-05-15 [1] RSPM (R 4.4.0)
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#>  rjson                  0.2.21  2022-01-09 [1] RSPM (R 4.4.0)
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#>  vroom                  1.6.5   2023-12-05 [1] RSPM (R 4.4.0)
#>  withr                  3.0.0   2024-01-16 [1] RSPM (R 4.4.0)
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#> 
#>  [1] /usr/local/lib/R/site-library
#>  [2] /usr/local/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────