Overview

SpaMTP’s current annotation pipeline separates candidate generation from downstream filtering. It generates chemically valid adduct hypotheses once, sorts their expected mass-to-charge ratios, queries that index within a strict mass tolerance, and ranks the remaining candidates using optional MS1 context.

The workflow is:

1. Decompose database SMILES into functional groups and plausible ionisation or metal-binding sites.
2. Select ion-mode and matrix rules, then apply molecule-specific structural bounds and priors.
3. Build a reusable sorted annotation index and query observed m/z values.
4. Score candidates using structure, mass accuracy, chemical heuristics, isotope evidence, and adduct-family support.
5. Store the retained candidates and choose a score threshold at analysis time.

This article describes upstream candidate annotation using native Bioconductor containers. See the native workflow for downstream spatial plotting and pathway-based candidate ranking, and modern data access for container accessors.

library(SpaMTP)

RaMP snapshot and identifiers

SpaMTP uses a deliberately pruned, versioned RaMP snapshot. SpaMTPdb stores the annotation and pathway resources independently of the analysis code: the chemical table keeps structures, formulae, monoisotopic masses, names, source identifiers, and current RAMP_C_* identifiers, while pathway tables retain only fields required by SpaMTP’s enrichment and network functions. The source = "auto" setting resolves these versioned resources through SpaMTPdb; a local staged resource directory can be used offline. The registry itself is package metadata, so it can be inspected without downloading the large chemical table.

ramp_registry <- spaMTPDatabaseInfo()
chem_props_registry <- ramp_registry[
  ramp_registry$resource == "chem_props",
  ,
  drop = FALSE
]

ramp_summary <- data.frame(
  property = c(
    "RaMP version",
    "Chemical-property records",
    "Serialized resource size",
    "Resource checksum"
  ),
  value = c(
    chem_props_registry$version,
    format(chem_props_registry$rows, big.mark = ","),
    format(chem_props_registry$bytes, big.mark = ","),
    chem_props_registry$md5
  )
)
knitr::kable(ramp_summary, align = c("l", "r"))

The complete table is retrieved only when it is needed. Its versioned schema is listed in the companion package registry and documented by SpaMTPdb:

chem_props_registry[, c(
  "resource", "version", "rows", "columns", "rdata_path"
)] |>
  knitr::kable(align = c("l", "r", "r", "r", "l"))

A small 2-hydroxyglutarate example

The examples below use the RaMP entry for D-2-hydroxyglutaric acid (2HG), with neutral monoisotopic mass approximately 148.0372 Da. Rather than manually declaring its chemistry, SpaMTP decomposes the RaMP iso_smiles graph into recognisable functional groups and ionisation sites.

# This one-row subset is embedded in the article so every example is runnable
# without downloading the 289,754-row resource. The identifiers and structure
# are copied verbatim from SpaMTPdb 3.0.7.
target_db <- data.frame(
  ramp_id = "RAMP_C_000219574",
  chem_data_source = "hmdb",
  chem_source_id = "hmdb:HMDB0000606",
  inchi_key = "HWXBTNAVRSUOJR-GSVOUGTGSA-N",
  monoisotop_mass = 148.037173366,
  common_name = "D-2-Hydroxyglutaric acid",
  mol_formula = "C5H8O5",
  iso_smiles = "O[C@H](CCC(O)=O)C(O)=O",
  stringsAsFactors = FALSE
)
target_db <- annotateSMILESStructure(target_db)

knitr::kable(
  target_db[, c(
    "ramp_id", "chem_source_id", "common_name", "mol_formula",
    "monoisotop_mass", "iso_smiles", "carboxyl_sites",
    "alcohol_hydroxyl_sites", "ketone_sites",
    "max_exchangeable_protons"
  )],
  digits = 7
)

The native parser works on molecular connectivity rather than elemental formula: for example, it separates a carboxylic acid from an ester and a ketone from the carbonyl in a carboxyl group. Existing curated columns are preserved by default; missing values are filled from SMILES.

From functional groups to ion-mode behaviour

deconvolveSMILES() also derives mode-specific, bounded scores. They are chemical priors, not calibrated probabilities or substitutes for experimental evidence.

structure_summary <- target_db[, c(
  "carboxyl_sites", "hydroxyl_sites", "carbonyl_sites",
  "acidic_sites", "basic_sites", "proton_acceptor_sites",
  "alkali_binding_sites", "alkali_chelation_motifs",
  "positive_mode_score", "negative_mode_score",
  "alkali_affinity_score", "neutral_mass_score"
)]
knitr::kable(structure_summary, digits = 2)

SpaMTP interprets these fields differently for each search mode:

• In positive mode, amines and other basic nitrogens support protonation; carbonyl, ether, and related heteroatoms provide weaker proton-acceptor evidence.
• For alkali/metal adducts, oxygen, nitrogen, and sulfur donor atoms are combined with carboxylate, catechol, phosphate, and nearby donor-pair motifs. A molecule with several donors can therefore favour [M+Na]+ or [M+K]+ even when it is not strongly basic.
• In negative mode, carboxylic, phosphoric, sulfonic, phenolic, and thiol sites support deprotonation. Ordinary alcohol groups contribute weaker evidence rather than an unconditional exclusion.
• neutral is a neutral-mass matching mode, not a claim that a mass spectrometer directly detected an uncharged molecule. It applies no protonation or metal-binding requirement.

For 2HG, two carboxyl groups support negative-mode deprotonation, while its five oxygen donors and multiple local donor motifs give stronger support to an alkali adduct than to simple positive-mode protonation. The complete audit text is retained in structure_evidence:

target_db[, c(
  "positive_site_atoms", "negative_site_atoms", "alkali_site_atoms",
  "structure_evidence"
)]

##   positive_site_atoms negative_site_atoms alkali_site_atoms
## 1              O7,O10               O6,O9   O1,O6,O7,O9,O10
##                                                                                                                                                                                               structure_evidence
## 1 carboxyl=2; alcohol-OH=1; phenolic-OH=0; ketone=0; amine=0; acidic-sites=2; proton-acceptors=2; alkali-donors=5; chelation-motifs=2; positive-atoms=O7,O10; negative-atoms=O6,O9; alkali-atoms=O1,O6,O7,O9,O10

Atom labels such as O1 refer to the atom order in the supplied SMILES graph; they identify the proposed local sites and are not canonical atom-map numbers.

The structure-only adduct search space can be inspected before any peak is queried. No adducts argument is required:

predicted_spaces <- lapply(
  c("positive", "negative", "neutral"),
  function(mode) {
    x <- predictAdductsFromSMILES(target_db$iso_smiles, polarity = mode)
    x <- x[x$retained, ]
    utils::head(x, 4L)
  }
)
predicted_spaces <- do.call(rbind, predicted_spaces)
knitr::kable(
  predicted_spaces[, c(
    "polarity", "adduct", "structure_score", "proton_bound_status",
    "combined_prior", "retained"
  )],
  digits = 3,
  row.names = FALSE
)

This prediction selects plausible families; observed mass, isotope, co-occurring adducts, spatial evidence, and pseudo-MS/MS still decide whether a specific peak supports one of them.

Validated adduct rules

adductRules() returns the ion masses, molecular multiplier, charge, proton-loss count, chemical class, and prior used by the engine. Bracketed and unbracketed forms such as [M+H]+ and M+H are both accepted.

selected_adducts <- c("M+H", "M+Na", "M+2Na-H", "2M+H")
positive_rules <- adductRules("positive")
rule_summary <- positive_rules[
  positive_rules$name %in% selected_adducts,
  c(
    "name", "n_molecules", "mass_shift", "charge", "loss_h",
    "complexity", "base_adduct", "prior"
  )
]
rule_summary <- rule_summary[match(selected_adducts, rule_summary$name), ]
knitr::kable(rule_summary, digits = 7, row.names = FALSE)

For each rule, SpaMTP calculates

\[ m/z_{expected} = \frac{nM + \Delta m_{adduct} - \Delta m_{loss}}{|z|}. \]

The stored mass_shift already contains the signed sum of additions and losses. Custom rule tables must satisfy charge balance before candidate generation.

Build the reusable index

buildMZAnnotationIndex() performs the expensive expansion once. Before mass expansion it applies SMILES-derived, rule-specific priors and chemical bounds; expected ions that remain are sorted for binary interval search. Thus the engine does not treat every adduct as equally plausible and only rank by m/z afterwards.

positive_index <- buildMZAnnotationIndex(
  target_db,
  polarity = "positive",
  adducts = selected_adducts
)
positive_index

## SpaMTP m/z annotation index
##   polarity: positive
##   MALDI matrix profile: unspecified
##   metabolites: 1
##   SMILES structures parsed: 1
##   ion/reaction rules: 4
##   expected ions: 4

ion_table <- data.frame(
  adduct = positive_index$rules$name[positive_index$rule_idx],
  expected_mz = positive_index$expected_mz
)
knitr::kable(ion_table, digits = 9, row.names = FALSE)

The structure contribution for each retained hypothesis is directly inspectable:

structure_rank <- queryMZAnnotationIndex(
  positive_index$expected_mz,
  positive_index,
  ppm = 0,
  check_isotopes = FALSE,
  check_adduct_network = FALSE
)
knitr::kable(
  structure_rank[, c(
    "adduct", "expected_mz", "structure_score", "structure_status",
    "structure_rule_evidence"
  )],
  digits = 3,
  row.names = FALSE
)

For large datasets, build one index for each database, polarity, and adduct set, then reuse it for every spectrum or batch of observed peaks.

negative_index <- buildMZAnnotationIndex(
  target_db,
  polarity = "negative",
  adducts = c("M-H", "M-2H", "M-3H")
)
indexed_negative_rules <- unique(
  negative_index$rules$name[negative_index$rule_idx]
)
data.frame(indexed_rule = indexed_negative_rules) |>
  knitr::kable(align = "l")

Query and rank candidates

The following observed peaks have small controlled mass offsets. The contextual MS1 spectrum also contains a carbon-13 peak for [M+H]+ at +1.00335 Da. In real data, ms1_spectrum should represent the same retention-time window, spatial pixel, or biologically coherent region as the queried peaks.

ppm_offsets <- c(1.2, -1.4, 0.6, -0.8)
observed_mz <- ion_table$expected_mz * (1 + ppm_offsets * 1e-6)
names(observed_mz) <- ion_table$adduct

carbon13_spacing <- 1.00335483507
ms1_spectrum <- data.frame(
  mz = c(
    observed_mz,
    ion_table$expected_mz[ion_table$adduct == "M+H"] + carbon13_spacing
  ),
  intensity = c(1000, 700, 300, 200, 53.5)
)

candidates <- queryMZAnnotationIndex(
  observed_mz,
  positive_index,
  ppm = 5,
  ms1_spectrum = ms1_spectrum,
  min_score = 0
)

candidate_view <- candidates[, c(
  "observed_mz", "expected_mz", "adduct", "ppm_error", "mass_score",
  "rule_prior", "chemical_score", "structure_score", "structure_status",
  "isotope_score",
  "adduct_network_score", "score", "ramp_ids"
)]
knitr::kable(candidate_view, digits = 4, row.names = FALSE)

Candidates outside the requested tolerance are removed before scoring:

outside_five_ppm <-
  ion_table$expected_mz[ion_table$adduct == "M+H"] * (1 + 5.1e-6)

data.frame(
  query = "5.1 ppm outside expected m/z",
  retained_candidates = nrow(queryMZAnnotationIndex(
    outside_five_ppm,
    positive_index,
    ppm = 5
  ))
) |>
  knitr::kable(row.names = FALSE)

The final score is multiplicative:

\[ S = S_{mass} \times P_{rule} \times S_{chemical} \times S_{structure} \times S_{reaction\ site} \times S_{isotope} \times S_{adduct\ family}. \]

Missing optional MS1 context is neutral rather than evidence of absence. When a contextual spectrum is supplied, missing expected isotope or base-adduct peaks can reduce the score.

dimer_mz <- ion_table$expected_mz[ion_table$adduct == "2M+H"]
monomer_mz <- ion_table$expected_mz[ion_table$adduct == "M+H"]

isolated_dimer <- queryMZAnnotationIndex(
  dimer_mz,
  positive_index,
  ppm = 5,
  ms1_spectrum = data.frame(mz = dimer_mz, intensity = 100),
  check_isotopes = FALSE
)
supported_dimer <- queryMZAnnotationIndex(
  dimer_mz,
  positive_index,
  ppm = 5,
  ms1_spectrum = data.frame(
    mz = c(dimer_mz, monomer_mz),
    intensity = c(100, 500)
  ),
  check_isotopes = FALSE
)

data.frame(
  context = c("isolated dimer", "base monomer present"),
  adduct_network_score = c(
    isolated_dimer$adduct_network_score,
    supported_dimer$adduct_network_score
  ),
  final_score = c(isolated_dimer$score, supported_dimer$score)
) |>
  knitr::kable(digits = 3, row.names = FALSE)

background_like_db <- data.frame(
  formula = "C2H2O2",
  exactmass = 99.942723533379,
  id = "synthetic-background-like-candidate",
  name = "Synthetic background-like candidate"
)
background_index <- buildMZAnnotationIndex(
  background_like_db,
  polarity = "positive",
  adducts = "M+H"
)
background_mz <- background_index$expected_mz[[1L]]

with_penalty <- queryMZAnnotationIndex(
  background_mz,
  background_index,
  ppm = 0,
  use_mass_defect = TRUE,
  check_isotopes = FALSE,
  check_adduct_network = FALSE
)
without_penalty <- queryMZAnnotationIndex(
  background_mz,
  background_index,
  ppm = 0,
  use_mass_defect = FALSE,
  check_isotopes = FALSE,
  check_adduct_network = FALSE
)

data.frame(
  setting = c("mass-defect heuristic on", "mass-defect heuristic off"),
  signed_mass_defect = background_mz - round(background_mz),
  chemical_score = c(
    with_penalty$chemical_score,
    without_penalty$chemical_score
  ),
  retained = TRUE
) |>
  knitr::kable(digits = 3, row.names = FALSE)

Annotate individual values or a SpaMTP object

annotateMZ() is a convenience wrapper for individual or small batches of observed values. Supply a pre-built index to avoid rebuilding it.

single_result <- annotateMZ(
  observed_mz[["M+H"]],
  index = positive_index,
  polarity = "positive",
  ppm = 5,
  ms1_spectrum = ms1_spectrum,
  min_score = 0
)
knitr::kable(
  single_result[, c(
    "observed_mz", "metabolite_names", "ramp_ids", "adduct",
    "ppm_error", "score"
  )],
  digits = 4,
  row.names = FALSE
)

annotateSM() applies the same engine to m/z feature metadata and preserves the ranked candidate table in metadata()$mz_annotation. The small object below is only for demonstrating storage and provenance.

demo_counts <- Matrix::Matrix(
  matrix(
    seq_len(length(observed_mz) * 4L),
    nrow = length(observed_mz),
    ncol = 4L
  ),
  sparse = TRUE
)
rownames(demo_counts) <- paste0("mz-", observed_mz)
colnames(demo_counts) <- paste0("pixel_", seq_len(ncol(demo_counts)))

demo_object <- SingleCellExperiment::SingleCellExperiment(
  assays = list(counts = demo_counts),
  rowData = S4Vectors::DataFrame(
    raw_mz = unname(observed_mz),
    row.names = rownames(demo_counts)
  )
)

annotated_object <- annotateSM(
  demo_object,
  db = NULL,
  assay = "main",
  raw.mz.column = "raw_mz",
  ppm_error = 5,
  adducts = selected_adducts,
  polarity = "positive",
  return.only.annotated = FALSE,
  save.intermediate = TRUE,
  min_score = 0,
  verbose = FALSE,
  index = positive_index,
  ms1_spectrum = ms1_spectrum
)

annotation_columns <- as.data.frame(SummarizedExperiment::rowData(annotated_object))[, c(
  "raw_mz", "all_IsomerNames", "all_Adducts", "all_Scores",
  "all_Ramp_IDs"
)]
knitr::kable(annotation_columns, digits = 4)

annotationInfo() records which engine and database provenance downstream functions will consume.

annotation_info <- annotationInfo(annotated_object)
annotation_info_table <- data.frame(
  field = names(annotation_info),
  value = vapply(annotation_info, paste, collapse = "; ", character(1)),
  row.names = NULL
)
knitr::kable(annotation_info_table, row.names = FALSE)

Choose thresholds without re-annotation

The recommended production pattern is to retain all ppm-valid candidates using min_score = 0, then select a threshold appropriate to the analysis. This makes coverage and specificity tunable without repeating index construction.

stored_candidates <- S4Vectors::metadata(annotated_object)$mz_annotation$results
score_thresholds <- c(0, 0.2, 0.5)
threshold_summary <- data.frame(
  score_threshold = score_thresholds,
  retained_candidates = vapply(
    score_thresholds,
    function(threshold) sum(stored_candidates$Score >= threshold),
    integer(1)
  )
)
knitr::kable(threshold_summary, row.names = FALSE)

Production use with the complete RaMP table

For one call, annotateSM(db = NULL) automatically resolves the current RaMP chemical-property table through SpaMTPdb. For repeated calls, explicitly load the table, then build and reuse an index. adducts is optional: when it is omitted, SpaMTP selects the full rule space for the mode or MALDI matrix and applies molecule-specific SMILES priors. An explicit list remains useful when the acquisition protocol rules out known ion families.

The smiles_features resource retrieved with SpaMTPdb::spaMTPdbResource() stores these fields separately from the pruned chem_props table and is joined by iso_smiles on demand. For a custom database of up to 5,000 unique structures, infer_structure = "auto" parses missing features at runtime. Larger un-precomputed inputs emit a warning instead of silently adding a long preprocessing delay; use infer_structure = "always", structure_workers, or precompute with annotateSMILESStructure() when that cost is intentional.

This section runs on all 2,075 measured mass features and 160 paired observations in the bundled DHB mouse-brain demonstrator, with the complete RaMP chemical table. The subset retains 80 locations per lesion side around the archived ACB coordinate centroids, selected without using expression or annotation results; see help("vignetteData") and the workflow guide. It is evaluated by default and downloads pinned annotation references if they are not available locally or in a verified cache. The average MS1 spectrum provides subset-level context, not pixel-specific validation of identities.

database <- loadSpaMTPDatabase(c("chem_props", "source_df", "analytehaspathway",
  "pathway", "ramp_db_metadata", "ramp_wikipathway", "ramp_reactome",
  "ramp_kegg", "ramp_hmdb"), version = "3.0.7")
chem_props <- database$chem_props
object <- readRDS(system.file("extdata", "dhb_demo.rds", package = "SpaMTP"))
original_counts <- SummarizedExperiment::assay(object, "counts")
ms1 <- data.frame(mz = SummarizedExperiment::rowData(object)$mz,
                  intensity = Matrix::rowMeans(original_counts))
ramp_positive_index <- buildMZAnnotationIndex(
  chem_props,
  polarity = "positive", maldi_matrix = "DHB"
)

object <- annotateSM(
  object,
  db = NULL,
  index = ramp_positive_index,
  assay = "main",
  raw.mz.column = "mz",
  polarity = "positive", maldi_matrix = "DHB", ms1_spectrum = ms1,
  ppm_error = 5,
  min_score = 0,
  return.only.annotated = FALSE
)

## Searching candidate index at 5 ppm ...

## Adding annotations to rowData ....

stopifnot(identical(SummarizedExperiment::assay(object, "counts"), original_counts),
          identical(dim(object), c(2075L, 160L)), nrow(chem_props) == 289754L)
annotationInfo(object)

## $schema_version
## [1] 2
## 
## $engine
## [1] "indexed-chemical-v2"
## 
## $ramp_version
## [1] "3.0.7"
## 
## $generated_at
## [1] "2026-10-14 02:14:44 UTC"
## 
## $candidates
## [1] 29949
## 
## $assay
## [1] "main"
## 
## $raw_mz_column
## [1] "mz"
## 
## $polarity
## [1] "positive"
## 
## $maldi_matrix
## [1] "dhb"
## 
## $ppm
## [1] 5
## 
## $min_score
## [1] 0
## 
## $adducts
## [1] "complete pre-built index rule space"
## 
## $database
## [1] "pre-built annotation index"

Carry the annotation threshold into real pathway analysis

Chemical eligibility and region characterization answer different questions. Select candidates at a declared score threshold, exclude ambiguous compound identities, and rank all retained identities by descriptive regional effects. No pixel-level DE P values are used for this single specimen. Mouse RNA remains in altExp() but is not converted to human HGNC genes. Network topology comes from human pathway resources; a compound match does not validate mouse-specific regulatory interactions.

pathway_index <- buildPathwayIndex(database, gene_mapping = "ramp",
                                 organism = "Mus musculus")
object <- normalizeSMData(object, "LogNormalize", verbose = FALSE)
object <- createPathwayAssay(object, assay = "main", slot = "logcounts",
  new_assay = "compound", pathway_index = pathway_index, database = database,
  organism = "Mus musculus", annotation_source = "current",
  annotation_score_threshold = 0.05, metabolite_ambiguity = "exclude")
markers <- findAllDEMs(object, ident = "RegionLoupe", assay = "compound",
  slot = "logcounts", method = "markers", spatial_blocks = 0)
ranks <- markers$expression
for (region in colnames(ranks)) {
  tab <- markers$DEMs[markers$DEMs$cluster == region, ]
  ranks[, region] <- tab$logFC[match(rownames(ranks), tab$gene)]
}
set.seed(1234)
regional_pathways <- findRegionalPathways(object, ident = "RegionLoupe",
  ranks = list(metabolites = ranks), pathway_index = pathway_index,
  min_path_size = 3, max_path_size = 100, organism = "Mus musculus")

##   |                                                                                                  |                                                                                          |   0%  |                                                                                                  |==========================================================================================| 100%

stopifnot(nrow(regional_pathways) > 0L)
head(regional_pathways[, c("pathwayName", "Cluster_id", "NES", "padj")])

##                pathwayName Cluster_id      NES      padj
## 1               Metabolism        ACB 1.327169 0.2442027
## 2     Metabolism of lipids        ACB 1.254681 0.2712566
## 3      Signal Transduction        ACB 1.112735 0.3826087
## 4                  Disease        ACB 1.490907 0.2052009
## 5       Infectious disease        ACB 1.320296 0.2442027
## 6 Viral Infection Pathways        ACB 1.281108 0.2442027

network_directory <- tempfile("dhb-annotation-networks-")
dir.create(network_directory)
network_file <- pathwayNetworkPlots(object, ident = "RegionLoupe",
  regpathway = regional_pathways, DE.list = list(metabolites = markers$DEMs),
  SM_assay = "compound", SM_slot = "logcounts", analyte_types = "metabolites",
  database = database, pathway_index = pathway_index, organism = "Mus musculus",
  annotation_score_threshold = 0.05, metabolite_detection = "annotated",
  path = network_directory, image = NULL, top_n_pathways = 4, max_nodes = 350)

## Selecting enriched pathways ...

## No stored topology was found for: Baseline metabolomic profiles: AC have prominent sugar metabolism whereas EC exhibit strong purine and amino acid metabolism; DHODH and cancer: promising prospects to be explored

## Preparing pathway edges once per topology ...

## Indexing differential results ...

## Extracting only plotted assay features ...

## Pathway network written to /tmp/Rtmp4y4zp6/dhb-annotation-networks-268020641637/RegionLoupe_2026_10_14_02_15_30.html

stopifnot(file.exists(network_file))

Interpretation and limitations

• An annotation is a ranked chemical hypothesis, not a confirmed identity, probability, or false-discovery rate.
• Accurate mass alone generally cannot resolve structural isomers.
• Isotope and adduct-family checks require a spectrum from an appropriate retention-time, pixel, or regional context.
• The mass-defect rule is a score penalty, not a universal exclusion rule.
• SMILES priors recognise common explicit functional groups but do not replace pKa, gas-phase proton-affinity, solvent, salt concentration, matrix, or instrument-specific models. Exotic bonding and organometallic SMILES may be returned as unparsed and retain backward-compatible mass-rule scoring.
• Tautomers and protonation states can change inferred sites. Store a standardised neutral SMILES in a production database and inspect structure_evidence for important assignments.
• Orthogonal evidence such as authentic standards, retention time, ion mobility, or MS/MS remains necessary for high-confidence identification.

The native workflow illustrates spatial plotting and pathway-based prioritisation of stored candidates. These ranks remain hypotheses, not substitutes for orthogonal identification evidence.

sessionInfo()

## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] SpaMTP_0.99.11   BiocStyle_2.41.0
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.6.1               later_1.4.8                 filelock_1.0.3             
##   [4] tibble_3.3.1                lifecycle_1.0.5             httr2_1.3.0                
##   [7] sf_1.1-3                    edgeR_4.99.7                globals_0.20.0             
##  [10] lattice_0.23-1              crosstalk_1.2.2             magrittr_2.0.5             
##  [13] SpaMTPdb_0.99.4             limma_3.99.2                plotly_4.12.1              
##  [16] sass_0.4.10                 rmarkdown_2.32              jquerylib_0.1.4            
##  [19] yaml_2.3.12                 metapod_1.21.0              httpuv_1.6.17              
##  [22] otel_0.2.0                  cowplot_1.2.0               DBI_1.3.0                  
##  [25] buildtools_1.0.0            RColorBrewer_1.1-3          abind_1.4-8                
##  [28] GenomicRanges_1.65.4        purrr_1.2.2                 BiocGenerics_0.59.12       
##  [31] rappdirs_0.3.4              SpaMTPData_0.99.5           IRanges_2.47.5             
##  [34] S4Vectors_0.51.10           ggrepel_0.9.8               irlba_2.4.1                
##  [37] listenv_1.1.0               maketools_1.3.2             pheatmap_1.0.13            
##  [40] units_1.0-1                 RSpectra_0.16-2             matter_2.15.0              
##  [43] dqrng_0.4.1                 parallelly_1.48.0           codetools_0.2-20           
##  [46] DelayedArray_0.39.8         scuttle_1.23.2              tidyselect_1.2.1           
##  [49] farver_2.1.2                ScaledMatrix_1.21.0         viridis_0.6.5              
##  [52] matrixStats_1.5.0           stats4_4.6.1                BiocFileCache_3.3.0        
##  [55] Seqinfo_1.3.2               jsonlite_2.0.0              CardinalIO_1.11.0          
##  [58] BiocNeighbors_2.7.3         e1071_1.7-17                scater_1.41.2              
##  [61] tools_4.6.1                 Rcpp_1.1.2                  glue_1.8.1                 
##  [64] gridExtra_2.3.1             SparseArray_1.13.4          BiocBaseUtils_1.15.1       
##  [67] xfun_0.61                   MatrixGenerics_1.25.0       dplyr_1.2.1                
##  [70] withr_3.0.3                 BiocManager_1.30.27         fastmap_1.2.0              
##  [73] bluster_1.23.1              digest_0.6.39               rsvd_1.0.5                 
##  [76] R6_2.6.1                    mime_0.13                   RSQLite_3.53.3             
##  [79] tidyr_1.3.2                 generics_0.1.4              data.table_1.18.6.1        
##  [82] FNN_1.1.4.1                 class_7.3-24                httr_1.4.9                 
##  [85] htmlwidgets_1.6.4           S4Arrays_1.13.2             ontologyIndex_2.12         
##  [88] pkgconfig_2.0.3             gtable_0.3.6                blob_1.3.0                 
##  [91] S7_0.2.2                    SingleCellExperiment_1.35.2 XVector_0.53.0             
##  [94] sys_3.4.3                   htmltools_0.5.9             fgsea_1.39.2               
##  [97] ProtGenerics_1.45.0         scales_1.4.0                Biobase_2.73.2             
## [100] png_0.1-9                   SpatialExperiment_1.23.0    scran_1.41.1               
## [103] knitr_1.52                  rjson_0.2.23                nlme_3.1-171               
## [106] curl_8.0.0                  proxy_0.4-29                cachem_1.1.0               
## [109] stringr_1.6.0               Cardinal_3.15.0             BiocVersion_3.24.0         
## [112] KernSmooth_2.23-27          parallel_4.6.1              vipor_0.4.7                
## [115] AnnotationDbi_1.75.2        pillar_1.11.1               grid_4.6.1                 
## [118] vctrs_0.7.3                 promises_1.5.0              BiocSingular_1.29.2        
## [121] dbplyr_2.6.0                beachmat_2.29.3             xtable_1.8-8               
## [124] cluster_2.1.8.3             beeswarm_0.4.0              evaluate_1.0.5             
## [127] magick_2.9.1                cli_3.6.6                   locfit_1.5-9.12            
## [130] compiler_4.6.1              rlang_1.3.0                 crayon_1.5.3               
## [133] future.apply_1.20.2         labeling_0.4.3              mclust_6.1.3               
## [136] classInt_0.4-11             naturalsort_0.1.3           ggbeeswarm_0.7.3           
## [139] stringi_1.8.9               viridisLite_0.4.3           BiocParallel_1.47.0        
## [142] Biostrings_2.81.9           Matrix_1.7-6                ExperimentHub_3.3.2        
## [145] bit64_4.8.6                 future_1.76.0               ggplot2_4.0.3              
## [148] KEGGREST_1.53.6             statmod_1.5.2               shiny_1.14.0               
## [151] SummarizedExperiment_1.43.0 AnnotationHub_4.3.2         igraph_2.3.4               
## [154] memoise_2.0.1               bslib_0.12.0                fastmatch_1.1-8            
## [157] bit_4.6.0