Auditable gene mapping with SpaMTPdb and SpaMTPData
Reference ownership and reproducibility
SpaMTPdb provides RaMP annotation tables and a separate versioned HGNC gene reference. SpaMTPData supplies native experiment data and declared species. SpaMTP resolves identifiers, annotates row metadata and runs pathway analyses. Use SpaMTP >= 0.99.6, SpaMTPdb >= 0.99.4 and SpaMTPData >= 0.99.5 together.
library(SpaMTP)
SpaMTPdb::spaMTPdbGeneReference(metadata = TRUE)
## resource version organism taxonomy_id title
## 1 hgnc_gene_reference 2026.7.7 Homo sapiens 9606 SpaMTPdb_HGNC_2026.7.7
## file_name
## 1 hgnc_complete_set_2026-07-07.tsv
## location_prefix
## 1 https://storage.googleapis.com/public-download-files/hgnc/archive/archive/quarterly/tsv/
## rdata_path source_url
## 1 hgnc_complete_set_2026-07-07.tsv https://www.genenames.org/download/archive/
## bytes md5
## 1 16913890 cd41d33955722de9ac0e14a2557ef5fc
SpaMTPdb::spaMTPdbVersion()
## [1] "3.0.7"The HGNC reference uses a fixed quarterly archive, with its own
version and checksum. It does not change the existing RaMP 3.0.7 or
experiment 1.1.0 files. HGNC mapping is for Homo
sapiens; it does not convert mouse genes to human orthologues.
Native experiments loaded by SpaMTPData carry their registry species in
metadata(x)$SpaMTPData, including their nested experiments.
Species checks therefore still apply after extracting RNA with
altExp().
Build once, inspect before testing
The following examples run during vignette builds, using a bundled
240-location subset of the published human-brain dataset. The locations
nearest the coordinate-wise median were selected without consulting
expression or mapping results. All 339 measured RNA features and their
paired MS features are retained; see help("vignetteData")
for the recipe and source checksums. Pinned annotation references are
downloaded if not already available locally or in a verified cache.
Local files and verified caches can also be used with
offline = TRUE.
database <- loadSpaMTPDatabase(
c("source_df", "analyte", "analytehaspathway", "pathway"),
version = "3.0.7"
)
reference <- SpaMTPdb::spaMTPdbGeneReference(
version = "2026.7.7"
)
index <- buildGeneMappingIndex(database$source_df, gene_reference = reference)
mapping <- mapGeneIdentifiers(
c("gene_symbol:AATK", "entrez:9625", "gene_symbol:BANK1"), index = index
)
mapping
## input gene_id symbol canonical_ramp_id
## 1 gene_symbol:AATK HGNC:21 AATK RAMP_G_000021175
## 2 entrez:9625 HGNC:21 AATK RAMP_G_000021175
## 3 gene_symbol:BANK1 HGNC:18233 BANK1 RAMP_G_000031335
## ramp_ids status method candidate_gene_ids
## 1 RAMP_G_000021175;RAMP_G_000030253 mapped approved_symbol HGNC:21
## 2 RAMP_G_000021175;RAMP_G_000030253 mapped entrez_id HGNC:21
## 3 RAMP_G_000031335 mapped approved_symbol HGNC:18233
## candidate_ramp_ids
## 1
## 2
## 3The index records a fingerprint of the source table’s gene
identifiers and common names. Pathway workflows reject a cached index
built from different identity assignments, even if the RaMP ID set is
unchanged. Rebuild older saved indices with
buildGeneMappingIndex(); changing row order or duplicating
identical source rows does not invalidate the fingerprint.
Symbols, previous symbols and aliases are resolved to an HGNC gene. An approved symbol takes priority over other genes’ aliases. Entrez, HGNC, UniProt and versioned Ensembl gene IDs are supported. A shared protein ID can be resolved by a compatible gene-specific ID in the same RaMP record. Conflicting specific IDs quarantine the record rather than joining different genes.
gene_id is the stable HGNC identity.
ramp_ids lists its non-conflicting RaMP records, and
canonical_ramp_id is one representative used to count the
gene once. For example, AATK’s UniProt and Entrez records in RaMP 3.0.7
belong to one HGNC gene, so their pathway memberships are united before
enrichment.
Inspect status, method,
candidate_gene_ids and candidate_ramp_ids. The
latter exposes raw RaMP candidates for direct IDs and crossreferences. A
shared crossreference with unresolved or conflicting competitors remains
ambiguous even when only one candidate has a known HGNC identity.
not_in_ramp means HGNC recognizes the gene but no usable
RaMP record was found; ambiguous and
conflicting_ramp_records require review.
mapped_ramp_only denotes a raw RaMP record whose HGNC
identity was not resolved. A warning is not evidence that the gene has
no biological function or pathway membership.
Annotate experiment features without altering expression
spe <- readRDS(system.file("extdata", "human_demo.rds", package = "SpaMTP"))
SingleCellExperiment::altExpNames(spe)
## [1] "SPT"
# Select the actual transcriptome altExp shown above. If identifiers live in
# a rowData column, set id_column; otherwise row names are used.
spe <- annotateGeneIdentifiers(
spe, assay = "SPT", index = index, organism = "Homo sapiens"
)
## Warning: 10 gene input(s) unresolved; inspect mapping status and
## candidate_gene_ids.
rna <- SingleCellExperiment::altExp(spe, "SPT")
head(SummarizedExperiment::rowData(rna))
## DataFrame with 6 rows and 6 columns
## spamtp_gene_id spamtp_gene_symbol spamtp_gene_canonical_ramp_id
## <character> <character> <character>
## AATK HGNC:21 AATK RAMP_G_000021175
## ABCA2 HGNC:32 ABCA2 RAMP_G_000012783
## ABCC3 HGNC:54 ABCC3 RAMP_G_000000236
## AIF1 HGNC:352 AIF1 RAMP_G_000014049
## AKAP12 HGNC:370 AKAP12 RAMP_G_000009757
## ALOX5AP HGNC:436 ALOX5AP RAMP_G_000008688
## spamtp_gene_ramp_ids spamtp_gene_status spamtp_gene_method
## <character> <character> <character>
## AATK RAMP_G_000021175;RAM.. mapped approved_symbol
## ABCA2 RAMP_G_000012783 mapped approved_symbol
## ABCC3 RAMP_G_000000236 mapped approved_symbol
## AIF1 RAMP_G_000014049 mapped approved_symbol
## AKAP12 RAMP_G_000009757 mapped approved_symbol
## ALOX5AP RAMP_G_000008688 mapped approved_symbol
table(SummarizedExperiment::rowData(rna)$spamtp_gene_status)
##
## conflicting_ramp_records mapped not_in_ramp
## 2 329 8
unresolved <- SummarizedExperiment::rowData(rna)$spamtp_gene_status != "mapped"
SummarizedExperiment::rowData(rna)[unresolved,
c("spamtp_gene_symbol", "spamtp_gene_status", "spamtp_gene_ramp_ids")]
## DataFrame with 10 rows and 3 columns
## spamtp_gene_symbol spamtp_gene_status spamtp_gene_ramp_ids
## <character> <character> <character>
## BRINP3 BRINP3 not_in_ramp
## C1orf162 C1orf162 not_in_ramp
## C1orf194 CFAP276 not_in_ramp
## C9orf24 SPMIP6 not_in_ramp
## FBXO36 FBXO36 not_in_ramp
## HEPN1 HEPN1 not_in_ramp
## HLA-DQA1 HLA-DQA1 conflicting_ramp_rec..
## SULT1A3 SULT1A3 conflicting_ramp_rec..
## TMIGD3 TMIGD3 not_in_ramp
## TRBC2 TRBC2 not_in_ramp
stopifnot(ncol(rna) == ncol(spe), nrow(rna) == 339L)Mapping columns remain aligned with original features. Counts, feature names, coordinates and paired modalities are preserved. If multiple expression rows map to the same gene, annotation records that fact without summing expression. Resolve those features before differential analysis. Regional pathway and network workflows reject different differential statistics for the same gene within a cluster rather than selecting an arbitrary row.
For these pinned inputs, 329 genes map and 10 produce an intentional unresolved identifier warning: two have conflicting RaMP records and eight are absent from RaMP. Their original expression remains in the object, but they are excluded from pathway testing. The table above exposes those exclusions rather than silently treating the entire 339-gene panel as mapped.
Use the whole measured panel as the universe
For a runnable foreground, we declare the measured genes AATK and BANK1 as genes of interest. This is an illustrative, predeclared query, not a list of statistically significant genes inferred from this single section.
genes_of_interest <- c("AATK", "BANK1")
measured_panel_genes <- rownames(rna)
stopifnot(all(genes_of_interest %in% measured_panel_genes))
result <- fishersPathwayAnalysis(
Analyte = list(genes = genes_of_interest),
universe = list(genes = measured_panel_genes),
database = database, gene_index = index,
min_path_size = 5, pval_cutoff = NULL
)
## Warning: universe$genes: 10 unmapped value(s) excluded; see attr(result,
## 'enrichment')$unmapped.
## Testing 1004 pathways; foreground = 2, universe = 329.
head(attr(result, "enrichment")$gene_mapping$inputs)
## input gene_id symbol canonical_ramp_id ramp_ids
## 1 AATK HGNC:21 AATK RAMP_G_000021175 RAMP_G_000021175;RAMP_G_000030253
## 2 BANK1 HGNC:18233 BANK1 RAMP_G_000031335 RAMP_G_000031335
## 3 ABCA2 HGNC:32 ABCA2 RAMP_G_000012783 RAMP_G_000012783
## 4 ABCC3 HGNC:54 ABCC3 RAMP_G_000000236 RAMP_G_000000236
## 5 AIF1 HGNC:352 AIF1 RAMP_G_000014049 RAMP_G_000014049
## 6 AKAP12 HGNC:370 AKAP12 RAMP_G_000009757 RAMP_G_000009757
## status method candidate_gene_ids candidate_ramp_ids
## 1 mapped approved_symbol HGNC:21
## 2 mapped approved_symbol HGNC:18233
## 3 mapped approved_symbol HGNC:32
## 4 mapped approved_symbol HGNC:54
## 5 mapped approved_symbol HGNC:352
## 6 mapped approved_symbol HGNC:370
head(result)
## pathway_name
## 1 Aberrant B Cell Signaling in Autoimmune Diseases
## 2 Pathomechanisms of LN outside the kidney
## 3 Identification of key genes and pathway related to chemoresistance of small cell lung cancer through an integrative bioinformatics analysis
## 4 GWAS of fibrosing disorders
## 5 ZIKV infection downregulates host mRNA expression in the placenta, including genes associated with cellular function and development
## 6 Pervasive translational regulation of the cell signalling circuitry underlies mammalian development
## pathway_id type pathwayCategory p_val fdr ratio
## 1 PMC9654300__F2 pfocr <NA> 0.03020980 1 0.20000000
## 2 PMC3752952__F1 pfocr <NA> 0.03020980 1 0.20000000
## 3 PMC9577795__F4 pfocr <NA> 0.03020980 1 0.20000000
## 4 PMC7698326__F1 pfocr <NA> 0.03619616 1 0.16666667
## 5 PMC9104602__F5 pfocr <NA> 0.04216399 1 0.14285714
## 6 PMC5316868__F2 pfocr <NA> 0.06584995 1 0.09090909
## analytes_in_pathways total_in_pathways pathwayRampId
## 1 1 5 RAMP_P_000082114
## 2 1 5 RAMP_P_000100894
## 3 1 5 RAMP_P_000103072
## 4 1 6 RAMP_P_000094730
## 5 1 7 RAMP_P_000077931
## 6 1 11 RAMP_P_000087198
## foreground_analytes_number background_analytes_number database_size
## 1 2 329 21
## 2 2 329 32
## 3 2 329 130
## 4 2 329 54
## 5 2 329 74
## 6 2 329 85
## database_raw_size measured_size coverage_fraction used_size
## 1 21 5 0.23809524 5
## 2 33 5 0.15625000 5
## 3 130 5 0.03846154 5
## 4 54 6 0.11111111 6
## 5 75 7 0.09459459 7
## 6 85 11 0.12941176 11
## excluded_conflict_count
## 1 0
## 2 1
## 3 0
## 4 0
## 5 1
## 6 0Foreground and universe use the same complete identity index. All
RaMP records of a resolved gene contribute to its union of pathway
memberships, but the gene counts once in K, M, U and overlap. The
universe is fixed before size filtering; zero-overlap eligible pathways
remain in the BH family. pval_cutoff filters FDR after
correction. Missing identifiers are excluded with an explicit audit; do
not set U to the raw panel length to hide mapping failures.
Official SpaMTPdb workflows harmonize human genes automatically. A
custom bundle can provide gene_reference, or an index can
be supplied explicitly. Custom bundles without a reference retain
RaMP-only behavior without a network request.
gene_mapping = "ramp" explicitly reproduces historical
mapping or uses a separately curated species-specific database; it is
not an orthology conversion switch. Previously saved gene-ID mappings
and p-values may change after identity reconciliation even though Fisher
arithmetic is unchanged.
Use the same gene_index in
findRegionalPathways() and
pathwayNetworkPlots() to keep annotation identity and
provenance consistent.
One membership index for enrichment, expression and plots
buildPathwayIndex() joins the gene identity index to
RaMP memberships once. A pathway’s database_size counts
unique identities after reconciliation and conflict exclusion;
database_raw_size counts original RaMP records.
measured_size and coverage_fraction describe
the selected assay or measured universe. used_size and
used_members record what actually contributes to a score or
test. In regional analysis these can be smaller than the measured set
because of the requested ranking filters. A pathway score is an
expression summary, not evidence that the whole pathway is
activated.
This small example runs offline. Its fictional gene has two RaMP records with complementary pathway memberships; both contribute while the gene counts once.
reference_example <- data.frame(
hgnc_id = c("HGNC:1", "HGNC:2", "HGNC:3"),
symbol = c("GENEA", "GENEB", "GENEC"), entrez_id = c("101", "102", "103"),
uniprot_ids = c("U1", "U2", "U3")
)
database_example <- list(
source_df = data.frame(rampId = paste0("RAMP_G_", c(1, 2, 3, 4)),
sourceId = c("entrez:101", "uniprot:U1", "entrez:102", "entrez:103")),
analytehaspathway = data.frame(rampId = paste0("RAMP_G_", c(1, 3, 2, 4)),
pathwayRampId = c("P1", "P1", "P2", "P2")),
pathway = data.frame(pathwayRampId = c("P1", "P2"), pathwayName = c("First", "Second")),
gene_reference = reference_example
)
pathway_index_example <- buildPathwayIndex(database_example)
example <- SpaMTPData::spaMTPExampleData()
E_example <- matrix(seq_len(36), 3, dimnames = list(c("GENEA", "GENEB", "GENEC"), colnames(example)))
SingleCellExperiment::altExp(example, "transcriptome") <-
SingleCellExperiment::SingleCellExperiment(assays = list(logcounts = log1p(E_example)))
example <- createPathwayAssay(example, analyte_type = "genes", assay = "transcriptome",
slot = "logcounts", new_assay = "gene_ids", pathway_index = pathway_index_example)
example <- createPathwayObject(example, assay = "gene_ids", new.assay = "gene_scores",
pathway_index = pathway_index_example)
SummarizedExperiment::rowData(SingleCellExperiment::altExp(example, "gene_scores"))[
, c("pathwayRampId", "database_size", "measured_size", "coverage_fraction", "used_members")]
## DataFrame with 2 rows and 5 columns
## pathwayRampId database_size measured_size coverage_fraction
## <character> <integer> <integer> <numeric>
## P1 P1 2 2 1
## P2 P2 2 2 1
## used_members
## <AsIs>
## P1 RAMP_G_1,RAMP_G_3
## P2 RAMP_G_1,RAMP_G_4For the real human subset loaded above, use the full measured RNA panel and the same index for every entry point. This chunk runs during package builds on all 339 RNA features and 240 retained observations. The end-to-end guide also uses these data for descriptive regional rankings. Neither example treats locations as biological replicates.
pathway_index <- buildPathwayIndex(database = database, gene_index = index)
spe <- createPathwayAssay(spe, analyte_type = "genes", assay = "SPT",
slot = "counts", new_assay = "gene_ids", pathway_index = pathway_index)
## Warning: 10 gene input(s) unresolved; inspect mapping status and
## candidate_gene_ids.
spe <- createPathwayObject(spe, assay = "gene_ids", new.assay = "gene_pathways",
pathway_index = pathway_index, min_path_size = 5, max_path_size = 500)
pathways <- SingleCellExperiment::altExp(spe, "gene_pathways")
coverage <- S4Vectors::metadata(pathways)$pathway_mapping$coverage
head(coverage[, c("pathwayRampId", "database_size", "database_raw_size",
"measured_size", "coverage_fraction", "excluded_conflict_count")])
## pathwayRampId database_size database_raw_size measured_size
## 1 RAMP_P_000000001 0 0 0
## 2 RAMP_P_000000002 18 18 1
## 3 RAMP_P_000000003 33 33 1
## 4 RAMP_P_000000004 15 15 1
## 5 RAMP_P_000000005 28 29 1
## 6 RAMP_P_000000006 9 9 1
## coverage_fraction excluded_conflict_count
## 1 NA 0
## 2 0.05555556 0
## 3 0.03030303 0
## 4 0.06666667 0
## 5 0.03571429 1
## 6 0.11111111 0
head(coverage$used_members)
## [[1]]
## character(0)
##
## [[2]]
## [1] "RAMP_G_000000001"
##
## [[3]]
## [1] "RAMP_G_000000001"
##
## [[4]]
## [1] "RAMP_G_000000001"
##
## [[5]]
## [1] "RAMP_G_000000001"
##
## [[6]]
## [1] "RAMP_G_000000001"
head(coverage$excluded_conflict_records)
## [[1]]
## character(0)
##
## [[2]]
## character(0)
##
## [[3]]
## character(0)
##
## [[4]]
## character(0)
##
## [[5]]
## [1] "RAMP_G_000000095"
##
## [[6]]
## character(0)
selected <- head(coverage$pathwayRampId[coverage$eligible], 2)
plots <- plotPathwaysSpatially(selected, spe, assay = "SPT", slot = "counts",
pathway_index = pathway_index)
## Warning: 10 gene input(s) unresolved; inspect mapping status and
## candidate_gene_ids.
attr(plots, "pathway_coverage")
## pathwayRampId sourceId type pathwayCategory
## 115 RAMP_P_000000115 map00500 kegg kegg
## 173 RAMP_P_000000173 map00010 kegg kegg
## pathwayName database_size database_raw_size measured_size
## 115 Starch and sucrose metabolism 102 105 8
## 173 Glycolysis / Gluconeogenesis 262 280 6
## coverage_fraction used_size eligible database_members measured_members
## 115 0.07843137 8 TRUE RAMP_G_0.... RAMP_G_0....
## 173 0.02290076 6 TRUE RAMP_G_0.... RAMP_G_0....
## used_members foreground_members excluded_conflict_records
## 115 RAMP_G_0.... RAMP_G_0....
## 173 RAMP_G_0....
## excluded_conflict_count
## 115 2
## 173 0
for (plot in plots) print(plot)
The original assays, feature names, coordinates and paired modalities
are preserved. A derived gene assay keeps its selected layer name:
logcounts do not become counts. Different expression rows mapping to one
gene error by default; choose duplicate_genes = "mean" or
"sum" only after considering the measurement scale (summing
log-expression is generally inappropriate). Identical repeated rows are
deduplicated. Named pathway plots accept exact
pathwayRampId values; ambiguous display names error instead
of choosing the first match. Gene and pathway reference fingerprints are
retained in outputs.
Fisher’s complete coverage table is
attr(result, "pathway_coverage"); its per-row size columns
also appear in the result. Pathway score objects put coverage in rowData
and the complete audit in metadata. Named plots return coverage
attributes and the exact plotted score vector. Unmeasured and
conflict-only pathways stay in the complete audit even when no score is
drawn.
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
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] 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
## [3] filelock_1.0.3 tibble_3.3.1
## [5] lifecycle_1.0.5 httr2_1.3.0
## [7] sf_1.1-3 edgeR_4.99.7
## [9] globals_0.20.0 lattice_0.23-1
## [11] crosstalk_1.2.2 magrittr_2.0.5
## [13] SpaMTPdb_0.99.4 limma_3.99.2
## [15] plotly_4.12.1 sass_0.4.10
## [17] rmarkdown_2.32 jquerylib_0.1.4
## [19] yaml_2.3.12 metapod_1.21.0
## [21] httpuv_1.6.17 otel_0.2.0
## [23] cowplot_1.2.0 DBI_1.3.0
## [25] buildtools_1.0.0 RColorBrewer_1.1-3
## [27] abind_1.4-8 GenomicRanges_1.65.4
## [29] purrr_1.2.2 BiocGenerics_0.59.12
## [31] rappdirs_0.3.4 SpaMTPData_0.99.5
## [33] IRanges_2.47.5 S4Vectors_0.51.10
## [35] ggrepel_0.9.8 irlba_2.4.1
## [37] listenv_1.1.0 maketools_1.3.2
## [39] pheatmap_1.0.13 units_1.0-1
## [41] RSpectra_0.16-2 matter_2.15.0
## [43] dqrng_0.4.1 parallelly_1.48.0
## [45] codetools_0.2-20 DelayedArray_0.39.8
## [47] scuttle_1.23.2 tidyselect_1.2.1
## [49] farver_2.1.2 ScaledMatrix_1.21.0
## [51] viridis_0.6.5 matrixStats_1.5.0
## [53] stats4_4.6.1 BiocFileCache_3.3.0
## [55] Seqinfo_1.3.2 jsonlite_2.0.0
## [57] CardinalIO_1.11.0 BiocNeighbors_2.7.3
## [59] e1071_1.7-17 scater_1.41.2
## [61] tools_4.6.1 Rcpp_1.1.2
## [63] glue_1.8.1 gridExtra_2.3.1
## [65] SparseArray_1.13.4 BiocBaseUtils_1.15.1
## [67] xfun_0.61 MatrixGenerics_1.25.0
## [69] dplyr_1.2.1 withr_3.0.3
## [71] BiocManager_1.30.27 fastmap_1.2.0
## [73] bluster_1.23.1 digest_0.6.39
## [75] rsvd_1.0.5 R6_2.6.1
## [77] mime_0.13 RSQLite_3.53.3
## [79] tidyr_1.3.2 generics_0.1.4
## [81] data.table_1.18.6.1 FNN_1.1.4.1
## [83] class_7.3-24 httr_1.4.9
## [85] htmlwidgets_1.6.4 S4Arrays_1.13.2
## [87] ontologyIndex_2.12 pkgconfig_2.0.3
## [89] gtable_0.3.6 blob_1.3.0
## [91] S7_0.2.2 SingleCellExperiment_1.35.2
## [93] XVector_0.53.0 sys_3.4.3
## [95] htmltools_0.5.9 fgsea_1.39.2
## [97] ProtGenerics_1.45.0 scales_1.4.0
## [99] Biobase_2.73.2 png_0.1-9
## [101] SpatialExperiment_1.23.0 scran_1.41.1
## [103] knitr_1.52 rjson_0.2.23
## [105] nlme_3.1-171 curl_8.0.0
## [107] proxy_0.4-29 cachem_1.1.0
## [109] stringr_1.6.0 Cardinal_3.15.0
## [111] BiocVersion_3.24.0 KernSmooth_2.23-27
## [113] parallel_4.6.1 vipor_0.4.7
## [115] AnnotationDbi_1.75.2 pillar_1.11.1
## [117] grid_4.6.1 vctrs_0.7.3
## [119] promises_1.5.0 BiocSingular_1.29.2
## [121] dbplyr_2.6.0 beachmat_2.29.3
## [123] xtable_1.8-8 cluster_2.1.8.3
## [125] beeswarm_0.4.0 evaluate_1.0.5
## [127] magick_2.9.1 cli_3.6.6
## [129] locfit_1.5-9.12 compiler_4.6.1
## [131] rlang_1.3.0 crayon_1.5.3
## [133] future.apply_1.20.2 labeling_0.4.3
## [135] mclust_6.1.3 classInt_0.4-11
## [137] ggbeeswarm_0.7.3 stringi_1.8.9
## [139] viridisLite_0.4.3 BiocParallel_1.47.0
## [141] Biostrings_2.81.9 Matrix_1.7-6
## [143] ExperimentHub_3.3.2 bit64_4.8.6
## [145] future_1.76.0 ggplot2_4.0.3
## [147] KEGGREST_1.53.6 statmod_1.5.2
## [149] shiny_1.14.0 SummarizedExperiment_1.43.0
## [151] AnnotationHub_4.3.2 igraph_2.3.4
## [153] memoise_2.0.1 bslib_0.12.0
## [155] fastmatch_1.1-8 bit_4.6.0