Package 'SpaMTP'

Description

Adds in backslashes required to take into account special using grepl such as brackets and +

Usage

add_backslashes_to_specialfeatures(input_string)

Arguments

Value

Character string containing double backslashes around special features

Description

Adds custom metabolite annotations to respective m/z values (ideal for specific matrices such as FMP10).

Usage

addCustomMZAnnotations(
  data,
  annotations,
  assay = "main",
  return.only.annotated = FALSE,
  mass.threshold = 0.05,
  annotation.column = "all_IsomerNames"
)

Arguments

Value

Bioconductor experiment containing the custom annotations stored in the feature metadata dataframe.

Examples

utils::str(formals(addCustomMZAnnotations))
# annotated_data <- addCustomMZAnnotations(SpaMTP.obj, annotation.df)

Description

Adds metabolite annotations to respective m/z values generated using an FMP10 matrix. This is done based on a curated FMP10 matrix database.

Usage

addFMP10Annotations(
  obj,
  only.fmp.adduct = FALSE,
  add.custom.annotation = NULL,
  assay = "main",
  return.only.annotated = FALSE,
  mass.threshold = 0.05,
  annotation.column = "all_IsomerNames",
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL
)

Arguments

Value

Bioconductor experiment containing the relative metabolite annotations stored in the feature metadata dataframe.

Examples

utils::str(formals(addFMP10Annotations))
# addFMP10Annotations(spamtp, only.fmp.adduct = FALSE)

Description

Attach and optionally align an optical image to MSI

Usage

addSMImage(
  image_path,
  SpaMTP,
  imageId = "optical",
  sampleId = NULL,
  scaleFactor = 1,
  transformation = NULL,
  interactive = TRUE,
  ...
)

Arguments

Value

A SpatialExperiment with an imgData entry and optionally transformed spatialCoords. Coordinate alignment requires one tissue sample.

Description

This is a small SpaMTP generic around SpatialExperiment::addImg(). Images are stored in imgData() and may remain path-backed by setting load = FALSE.

Usage

addSpatialImage(
  x,
  imageSource,
  scaleFactor = 1,
  sampleId = unique(x$sample_id)[[1L]],
  imageId = tools::file_path_sans_ext(basename(imageSource)),
  load = FALSE,
  ...
)

## S4 method for signature 'SpatialExperiment'
addSpatialImage(
  x,
  imageSource,
  scaleFactor = 1,
  sampleId = unique(x$sample_id)[[1L]],
  imageId = tools::file_path_sans_ext(basename(imageSource)),
  load = FALSE,
  ...
)

Arguments

Value

The updated object.

Examples

methods::showMethods("addSpatialImage")

Description

Adds spatial shrunken centroid (SSC) labels to Cardinal::pixelData(data)[["ssc"]] through its public replacement accessor. This function accepts Cardinal containers only; it does not modify S4 slots directly or require Seurat.

Usage

addSSCAnnotation(data, data_ssc, resolution)

Arguments

Value

A Cardinal Object containing the raw m/z counts and the relative ssc segmentation annotations per pixel.

Examples

utils::str(formals(addSSCAnnotation))
# ssc_data <- Cardinal::spatialShrunkenCentroids(CardinalObj, ...)
# new_CardinalObj <- addSSCAnnotation(CardinalObj, ssc_data, resolution ="r=2,k=8,s=32")

Description

The transcriptome must contain the same spatial pixels as the metabolomics object. It is stored with SingleCellExperiment::altExp() rather than as a second Seurat assay.

Usage

addTranscriptome(
  x,
  transcriptome,
  name = "transcriptome",
  assayName = "counts",
  ...
)

## S4 method for signature 'SpatialExperiment'
addTranscriptome(
  x,
  transcriptome,
  name = "transcriptome",
  assayName = "counts",
  ...
)

Arguments

Value

The updated SpatialExperiment.

Examples

methods::showMethods("addTranscriptome")

Description

A small table of legacy adduct definitions retained for compatibility with earlier SpaMTP workflows. New annotation code should use adductRules(), which includes charge, stoichiometry, ion-mode, and chemical-validity fields.

Usage

data(adduct_file)

Format

A data frame with 47 rows and 6 variables:

adduct_name

Adduct notation.

ion.mass

Legacy ion-mass expression.

charge

Ion charge.

mult

Analyte stoichiometric multiplier.

add_mass

Exact mass shift.

pol

Ion polarity.

Value

A data frame of legacy adduct definitions.

See Also

adductRules(), maldiMatrixRules()

Description

The rules use neutral monoisotopic mass and explicit ion masses. Each row stores the molecular multiplier, total numerator mass shift, charge, number of protons removed from the analyte, and the charge contributed by added species. This allows rules to be checked before candidate generation.

Usage

adductRules(
  polarity = c("both", "positive", "negative", "neutral"),
  include_complex = TRUE
)

Arguments

Value

A data frame containing validated adduct rules.

Examples

utils::str(formals(adductRules))

Description

A supplied homogeneous matrix is applied reproducibly without a GUI. With transformation = NULL, an interactive preview allows translation, rotation, scaling and reflection. Coordinates are always stored in their full spatial units; an imgData scaleFactor affects only display of the reference image.

Usage

alignSpatialOmics(
  sm.data,
  st.data,
  transformation = NULL,
  image.slice = NULL,
  msi.pixel.multiplier = 1,
  shiny.host = "127.0.0.1",
  shiny.port = NULL,
  verbose = FALSE
)

Arguments

Value

The source SpatialExperiment with updated spatialCoords and alignment provenance recorded by applySpatialAlignment.

Description

Adds descriptive region-versus-rest effects, detection fractions, region means and eligibility audits. Existing biological-replicate contrasts remain unchanged. Optional native spatial modules call findSpatiallyVariableMetabolites and runSpatialGraphPCA on an explicitly bounded, recorded feature/point screen. Works with any modality names, species and observation metadata, including results saved by earlier workflow versions.

Usage

analyzeSpaMTPRegions(
  result,
  regions = NULL,
  min_region_size = 3,
  native = list(),
  markers = TRUE,
  spatial_blocks = 6
)

Arguments

Details

Region effects use all labelled retained observations. Blank/NA labels are excluded and counted in the audit. No P values are inferred from pixels or random pools. Moran's I statistics are exploratory and use BH within the selected feature family per modality and sample; this screening is not a genome-wide or biological-replicate test. Marker reference means equally weight other eligible regions, following scran pairwise summaries.

Value

The result with region_analysis, native_analysis and updated provenance.

Examples

utils::str(formals(analyzeSpaMTPRegions))

Description

This function assigns each valid m/z peak with one/multiple metabolite names based on the mass difference between the observed value and the theoretical value documented in the reference database. This function is to be used when dealing with large datasets as a preprocessing step. Users can annotate m/z values first and then subset their data accordinly before loading it into a Bioconductor experiment.

Usage

annotateBigData(
  mzs,
  db = NULL,
  ppm_error = NULL,
  adducts = NULL,
  polarity = NULL,
  tof_resolution = 30000,
  verbose = TRUE,
  maldi_matrix = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  ...
)

Arguments

Value

A data.frame containing all successfully annotated m/z values, with their corresponding annotation.

Examples

utils::str(formals(annotateBigData))
#cardinal <- readImzML("./Test_Data/Spotted/test_data1")
#mzs <- data.frame(Cardinal::featureData(cardinal))$mz
#results <- annotateBigData(mzs, db = HMDB_db, ppm_error = 3, adducts = c("M-H", "M+Cl"), polarity = "negative")
#cardinal_subset <- Cardinal::subset(cardinal, mz %in% results$observed_mz)

Description

Adds aligned mapping columns to the selected experiment's rowData() and provenance to its metadata(). Expression values, feature names, row order, spatial coordinates and paired experiments are preserved. Multiple assay rows that map to one gene are annotated, not silently summed; pathway enrichment performs gene-level deduplication separately.

Usage

annotateGeneIdentifiers(
  object,
  assay = "main",
  id_column = NULL,
  id_type = c("auto", "symbol", "entrez", "ensembl", "uniprot", "hgnc"),
  ...
)

Arguments

Value

The input object with ⁠spamtp_gene_*⁠ mapping columns and metadata(experiment)$gene_mapping provenance and input audit.

Examples

utils::str(formals(annotateGeneIdentifiers))

Description

Convenience wrapper that builds an index when one is not supplied. Reuse a pre-built index for repeated or large searches.

Usage

annotateMZ(
  observed_mz,
  db = NULL,
  index = NULL,
  polarity = NULL,
  adducts = NULL,
  rules = NULL,
  maldi_matrix = NULL,
  ppm = 5,
  ms1_spectrum = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  infer_structure = c("auto", "never", "always"),
  structure_backend = c("auto", "native"),
  structure_workers = getOption("SpaMTP.smiles_workers", 1L),
  min_structure_score = 0.05,
  ...
)

Arguments

Value

A ranked candidate data frame.

Examples

utils::str(formals(annotateMZ))

Description

This function assigns each valid m/z peak with one/multiple metabolite names based on the mass difference between the observed value and the theoretical value documented in the reference database. Versioned RaMP, HMDB, LIPID MAPS, ChEBI, and GNPS resources are provided by SpaMTPdb and can be combined with user-supplied reference tables.

Usage

annotateSM(
  data,
  db = NULL,
  assay = "main",
  raw.mz.column = "raw_mz",
  ppm_error = NULL,
  adducts = NULL,
  polarity = NULL,
  tof_resolution = 30000,
  filepath = NULL,
  return.only.annotated = TRUE,
  save.intermediate = TRUE,
  min_score = 0,
  verbose = TRUE,
  maldi_matrix = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  ...
)

Arguments

Value

A Bioconductor experiment with m/z values annotated. These annotations are stored in the selected experiment's rowData().

Examples

utils::str(formals(annotateSM))
# HMDB_db <- load("data/HMDB_1_names.rds")

Description

Add SMILES-derived structural features to a metabolite database

Usage

annotateSMILESStructure(
  db,
  smiles_column = NULL,
  backend = c("auto", "native"),
  overwrite = FALSE,
  strict = FALSE,
  workers = getOption("SpaMTP.smiles_workers", 1L)
)

Arguments

Value

db with structure-derived columns appended or completed.

Examples

utils::str(formals(annotateSMILESStructure))

Description

Helper function for annotateSM() and fishersPathwayAnalysis().

Usage

annotateTable(
  mz_df,
  db = NULL,
  ppm_error = NULL,
  adducts = NULL,
  polarity = NULL,
  tof_resolution = 30000,
  verbose = TRUE,
  index = NULL,
  rules = NULL,
  ms1_spectrum = NULL,
  use_mass_defect = TRUE,
  check_isotopes = TRUE,
  check_adduct_network = TRUE,
  min_score = 0,
  maldi_matrix = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  infer_structure = c("auto", "never", "always"),
  structure_backend = c("auto", "native"),
  structure_workers = getOption("SpaMTP.smiles_workers", 1L),
  min_structure_score = 0.05
)

Arguments

Value

Generates an intermediate annotated m/z dataframe

Examples

### HelperFunction

Description

Reports whether downstream pathway functions will use the current indexed, scored RaMP annotation pipeline or a legacy mass-match result.

Usage

annotationInfo(SpaMTP)

Arguments

Value

A named list containing annotation schema, engine, RaMP version, provenance, and candidate count where available.

Examples

utils::str(formals(annotationInfo))

Description

Aligns a moving Spatial Metabolomics (SM) SpatialExperiment to a fixed SpatialExperiment. The function can apply coordinates exported by the SMINT workflow, apply a homogeneous transformation matrix, estimate an affine transform from paired landmarks, or run the STalign LDDMM backend used by SMINT through reticulate.

Usage

applySpatialAlignment(
  SM.data,
  ST.data = NULL,
  alignment = NULL,
  method = c("lddmm", "affine"),
  SM.fov = NULL,
  ST.image = NULL,
  SM.boundary = "centroids",
  SM.landmarks = NULL,
  ST.landmarks = NULL,
  landmark.order = c("xy", "yx"),
  SM.landmark.space = c("original", "preprocessed"),
  coordinate.columns = NULL,
  cell.column = NULL,
  ST.scale.factor = NULL,
  source.scale = 1,
  source.rotate = 0,
  source.flip = c(FALSE, FALSE),
  source.translate = c(0, 0),
  source.origin = NULL,
  dx = 30,
  niter = 1000,
  epV = 200,
  device = c("auto", "cpu", "cuda"),
  seed = 1,
  lddmm.args = list(),
  python = NULL,
  alignment.name = "SMINT",
  store = TRUE,
  return = c("object", "result"),
  diagnostics.max.points = 10000,
  verbose = TRUE
)

Arguments

Details

Python is only required when method = "lddmm" and alignment = NULL. Precomputed SMINT coordinates and affine landmark alignment are handled entirely in R. The backend summary records STalign's affine component as affine_yx; it is not the complete nonlinear LDDMM transformation.

Value

The updated spatial container when return = "object"; otherwise an object of class spamtp_spatial_alignment containing the aligned object and alignment details.

Examples

utils::str(formals(applySpatialAlignment))
# Apply coordinates already exported by the SMINT/STalign notebook.
aligned_sm <- applySpatialAlignment(
  SM.data = sm,
  ST.data = st,
  alignment = "Ven5_z2_transformed_metabolites_coordinates.csv",
  coordinate.columns = c("x_transformed", "y_transformed")
)

# Run the notebook's landmark-guided LDDMM workflow from R.
fit <- applySpatialAlignment(
  SM.data = sm,
  ST.data = st,
  method = "lddmm",
  SM.landmarks = sm_landmarks,
  ST.landmarks = st_landmarks,
  landmark.order = "yx",             # point-annotator row/column order
  SM.landmark.space = "preprocessed", # landmarks selected after rasterization
  source.scale = 10,
  source.rotate = 90,
  source.origin = c(0, 0),
  python = "/path/to/STalign_env/bin/python",
  return = "result"
)
fit$diagnostics

Description

Convert a SpatialExperiment to a Cardinal experiment

Usage

asCardinal(x, ...)

## S4 method for signature 'SpatialExperiment'
asCardinal(x, ...)

## S4 method for signature 'MSImagingExperiment'
asCardinal(x, ...)

## S4 method for signature 'MSImagingArrays'
asCardinal(x, ...)

Arguments

Value

A Cardinal MSImagingExperiment, or unchanged MSImagingArrays input.

Examples

methods::showMethods("asCardinal")

Description

asSpatialExperiment() is the Bioconductor-native conversion entry point used by SpaMTP. Continuous or unaligned raw spectra should remain in a Cardinal MSImagingArrays or MSImagingExperiment; conversion is intended for data with a common feature-by-pixel matrix, typically after peak alignment or binning.

Usage

asSpatialExperiment(x, ...)

## S4 method for signature 'MSImagingExperiment'
asSpatialExperiment(x, ...)

## S4 method for signature 'MSImagingArrays'
asSpatialExperiment(x, ...)

## S4 method for signature 'SpatialExperiment'
asSpatialExperiment(x, ...)

## S4 method for signature 'SummarizedExperiment'
asSpatialExperiment(x, coordinates = c("x", "y"), ...)

Arguments

Value

A SpatialExperiment.

Examples

methods::showMethods("asSpatialExperiment")

Description

The sum is stored in colData without modifying the expression assays.

Usage

binMetabolites(
  data,
  mzs,
  assay = "main",
  slot = "counts",
  bin_name = "Binned_Metabolites"
)

Arguments

Value

The updated SpatialExperiment.

Examples

utils::str(formals(binMetabolites))

Description

Convert a binned Cardinal matrix to an optional Seurat object

Usage

binnedCardinalToSeurat(
  data,
  mtx,
  multi.run = FALSE,
  assay = "Spatial",
  verbose = TRUE
)

Arguments

Value

A Seurat object.

Examples

utils::str(formals(binnedCardinalToSeurat))

Description

MSImagingArrays or MSImagingExperiment input delegates binning to Cardinal and is converted to SpatialExperiment only after a common feature axis exists. SpatialExperiment input returns a new binned SpatialExperiment. For aligned matrices, bins start at the minimum observed m/z and use linear (mz) or log-mass (ppm) spacing. Each feature is assigned once to its nearest bin on that axis (midpoints go up); empty bins are omitted. Thus sum binning conserves each pixel's total intensity. Cardinal input uses Cardinal's reference-window binning convention instead.

Usage

binSpaMTP(
  data,
  resolution,
  units = "ppm",
  assay = "main",
  slot = "counts",
  method = c("sum", "mean", "max", "min"),
  return.only.mtx = FALSE
)

## S4 method for signature 'SpatialExperiment'
binSpaMTP(
  data,
  resolution,
  units = "ppm",
  assay = "main",
  slot = "counts",
  method = c("sum", "mean", "max", "min"),
  return.only.mtx = FALSE
)

## S4 method for signature 'MSImagingExperiment'
binSpaMTP(
  data,
  resolution,
  units = "ppm",
  assay = "main",
  slot = "counts",
  method = c("sum", "mean", "max", "min"),
  return.only.mtx = FALSE
)

## S4 method for signature 'MSImagingArrays'
binSpaMTP(
  data,
  resolution,
  units = "ppm",
  assay = "main",
  slot = "counts",
  method = c("sum", "mean", "max", "min"),
  return.only.mtx = FALSE
)

## S4 method for signature 'ANY'
binSpaMTP(
  data,
  resolution,
  units = "ppm",
  assay = "main",
  slot = "counts",
  method = c("sum", "mean", "max", "min"),
  return.only.mtx = FALSE
)

Arguments

Value

A binned SpatialExperiment or matrix.

Examples

methods::showMethods("binSpaMTP")

Description

Connects RaMP gene records to HGNC using explicit stable identifiers, then approved and previous symbols. Shared aliases are never used to merge RaMP records. Conflicting stable identifiers quarantine the affected RaMP record. Multiple non-conflicting RaMP records for one HGNC gene share one canonical RaMP ID (lexicographically first); their pathway memberships can then be united and counted once. No transitive merging across different HGNC genes or orthology conversion is performed. A fingerprint of normalized RaMP identifiers and common names guards index reuse. Reordering rows, duplicating evidence or changing unrelated columns does not invalidate it. Rebuild saved indices from older schema versions with this function before using them in a new workflow.

Usage

buildGeneMappingIndex(
  source_df,
  gene_reference = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  offline = FALSE
)

Arguments

Value

A spamtp_gene_index list containing reference lookup, gene groups, per-RaMP identity decisions, precomputed conflict membership, evidence and resource provenance including source_fingerprint.

Examples

utils::str(formals(buildGeneMappingIndex))

Description

Expected adduct m/z values are generated once, sorted, and queried by binary interval search. This is the one-dimensional equivalent of an interval tree and avoids scanning every database row for every observed peak.

Usage

buildMZAnnotationIndex(
  db,
  polarity = NULL,
  adducts = NULL,
  rules = NULL,
  maldi_matrix = NULL,
  collapse_isomers = TRUE,
  infer_structure = c("auto", "never", "always"),
  structure_backend = c("auto", "native"),
  structure_workers = getOption("SpaMTP.smiles_workers", 1L),
  min_structure_score = 0.05
)

Arguments

Value

An object of class spamtp_mz_index.

Examples

utils::str(formals(buildMZAnnotationIndex))

Description

Unites pathway memberships of non-conflicting RaMP records belonging to one HGNC gene. Retains raw memberships and excluded conflicting records. Reuse this index in enrichment, expression aggregation, scoring and named pathway plots to obtain the same identities and membership sets. Pathways are keyed by pathwayRampId; identical display names never merge distinct pathways.

Usage

buildPathwayIndex(
  database = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL
)

Arguments

Value

A spamtp_pathway_index containing canonical and raw member sets, conflicting members, metadata, gene_index and resource fingerprints. Database size means unique analyte identities after reconciliation and conflict exclusion, before intersecting with measured features.

Examples

db <- list(analytehaspathway = data.frame(rampId = "RAMP_C_1",
           pathwayRampId = "P1"), pathway = data.frame(pathwayRampId = "P1"))
buildPathwayIndex(db)$members

Description

Calculates the mz range of the observed_df

Usage

calculate_bounds(input_df)

Arguments

Value

A list containing the lower and upper mz range for the provided sample

Examples

# mz_df$row_id <- seq(1, length(mz_df$mz))

# mass_range <- calculate_bounds(mz_df)
# lower_bound <- mass_range$lower_bound
# upper_bound <- mass_range$upper_bound

Description

When pathway.assay contains RaMP analytes rather than pathway scores, createPathwayObject is reused to calculate scores in a temporary altExp. Set pathway.scores=TRUE to use an existing pathway-level assay directly. Input containers are never modified.

Usage

calculateAnnotationStatistics(
  data,
  mz.assay = "main",
  pathway.assay = "pathway",
  mz.slot = "counts",
  pathway.slot = "counts",
  return.top = TRUE,
  corr_theshold = 0,
  corr_weight = 1,
  n_weight = 1,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  pathway.scores = FALSE,
  pathway_index = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  duplicate_genes = c("error", "mean", "sum")
)

Arguments

Details

Uses the score definition and missing-data rules documented in calculateSingleAnnotationStatistics(). The reported pval and pval_adj columns are heuristic ranking measures, not calibrated identification p-values or biological-replicate inference.

Value

A data.frame preserving feature order and rowData, with ranked annotation columns; or a named list when return.top=FALSE.

Examples

utils::str(formals(calculateAnnotationStatistics))

Description

Pearson correlations are calculated directly between a selected m/z assay and pathway expression in altExp, aligned by pixel IDs. This replaces the former temporary Seurat assay and synthetic-mass Cardinal conversion.

Usage

calculateSingleAnnotationStatistics(
  mz,
  data,
  mz.assay = "main",
  pathway.assay = "pathway",
  mz.slot = "counts",
  pathway.slot = "pathwayScores",
  corr_theshold = 0,
  corr_weight = 1,
  n_weight = 1,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL
)

Arguments

Details

For each candidate, max_cor is the signed correlation of the pathway with largest absolute correlation; ties use pathway ID order. n_sig_path counts finite correlations strictly greater than corr_theshold (zero therefore counts positive correlations, not every pathway). The score is corr_weight * scale(abs(max_cor)) + n_weight * scale(n_sig_path), across candidates for one feature. A constant component contributes zero. Constant expression and unmeasured pathways have undefined correlations. Scores require two evaluable candidates; otherwise scores and probabilities are NA.

pval is the standard-normal upper tail of this weighted score; pval_adj applies BH within each feature. These are legacy heuristic ranking measures, not calibrated identification p-values: the weighted components are not independent, the combined score need not be standard normal, and pixels are not biological replicates. Correlation is across all supplied pixels; subset samples/conditions beforehand when appropriate.

Current scored RaMP annotations take precedence over rowData RaMP IDs. Older all_Isomers_IDs columns can be mapped via source_df$sourceId. Duplicate candidate IDs are counted once.

Value

A tibble ordered by decreasing score, containing metabolite, ramp_id, n_sig_path, max_cor, z_score, pval and pval_adj.

Examples

utils::str(formals(calculateSingleAnnotationStatistics))

Description

Convert a Cardinal object to an optional Seurat object

Usage

cardinalToSeurat(
  data,
  multi.run = FALSE,
  seurat.coord = NULL,
  assay = "Spatial",
  verbose = TRUE
)

Arguments

Value

A Seurat object.

Examples

utils::str(formals(cardinalToSeurat))

Description

The intensity matrix is stored as an assay, Cardinal featureData() becomes rowData(), pixelData() becomes colData(), and coord() becomes spatialCoords(). This conversion materialises a matter-backed intensity matrix; raw continuous data should be binned with Cardinal before conversion.

Usage

cardinalToSpatialExperiment(x, assayName = "counts", materialize = TRUE)

Arguments

Value

A SpatialExperiment.

Examples

utils::str(formals(cardinalToSpatialExperiment))

Description

Checks if the complete adduct is in the data base, else returns a truncated adduct

Usage

check_and_truncate_adduct_vector(adduct, db, verbose = TRUE)

Arguments

Value

Character string of the complete or truncated adduct

Examples

# HMDB_db <- load("data/HMDB_1_names.rds")
# check_and_truncate_adduct_vector(c("M+H"), HMDB_db)

Description

Checks if the Cardinal Package installed is >= version 3.6.0

Usage

check_cardinal_version()

Value

Boolean indicating if the version of Cardinal is >= 3.6.0

Description

Inspect the alignment of two spatial experiments

Usage

checkAlignment(
  SM.data,
  ST.data,
  names = c("SM", "ST"),
  cols = NULL,
  image.slice = NULL,
  size = 0.5
)

Arguments

Value

A ggplot; multiple samples are shown in separate panels.

Description

Combines rigid tranformation matrices in the following order: translation of points to origin (0, 0) -> reflection of points -> rotation by alpha degrees and translation of points to new center

Usage

combineTransformations(
  center.cur,
  center.new,
  alpha,
  mirror.x = FALSE,
  mirror.y = FALSE
)

Arguments

Value

A 3-by-3 homogeneous affine-transformation matrix.

Examples

utils::str(formals(combineTransformations))
transformation <- combineTransformations(
  center.cur = c(0, 0),
  center.new = c(10, 20),
  alpha = 90
)
stopifnot(identical(dim(transformation), c(3L, 3L)))

Description

Convert an optional Seurat object to Cardinal

Usage

convertSeuratToCardinal(
  data,
  assay = "Spatial",
  slot = "counts",
  run_col = NULL,
  feature.metadata = FALSE,
  verbose = TRUE
)

Arguments

Value

A Cardinal MSImagingExperiment.

Examples

utils::str(formals(convertSeuratToCardinal))

Description

Combines selected modalities as a scaled alternative experiment. Useful for integrating multiple modalities (e.g. transcriptomics, proteomics, metabolomics) that have already been scaled.

Usage

createMergedModalityAssay(
  SpaMTP,
  assays.to.merge,
  new.assay = "merged",
  return.original = TRUE,
  verbose = FALSE
)

Arguments

Details

Prefers the native scaled assay, then the legacy scale.data assay; otherwise centres and scales logcounts, normcounts or counts across pixels. Constant features become zero. The merged values are stored as scaled, never relabelled as counts.

Value

If return.original = TRUE, the input SpatialExperiment with altExp(SpaMTP, new.assay) containing a scaled assay. Otherwise a new SpatialExperiment with merged rows, the same pixels and a scaled assay.

Examples

x <- SpatialExperiment::SpatialExperiment(
    assays = list(counts = rbind(a = 1:4, b = 4:1)),
    spatialCoords = cbind(x = 1:4, y = 0),
    colData = S4Vectors::DataFrame(row.names = paste0("p", 1:4)))
rna <- matrix(c(1, 3, 2, 4), nrow = 1,
    dimnames = list("gene1", colnames(x)))
x <- addTranscriptome(x, rna)
x <- createMergedModalityAssay(x, c("main", "transcriptome"))
merged <- SingleCellExperiment::altExp(x, "merged")
SummarizedExperiment::assay(merged, "scaled")

Description

Maps genes or mass features to RaMP identities in a new altExp(). Rows are gene/compound identities, not pathway scores. Use this identity expression for enrichment or co-regulation analysis, or call createPathwayObject() to construct pathway-level scores.

Usage

createPathwayAssay(
  SpaMTP,
  analyte_type = "metabolites",
  assay = "Spatial",
  slot = "counts",
  new_assay = "pathway",
  annotation_score_threshold = 0.05,
  annotation_source = c("current", "auto", "legacy"),
  verbose = TRUE,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  pathway_index = NULL,
  duplicate_genes = c("error", "mean", "sum"),
  id_column = NULL,
  metabolite_ambiguity = c("expand", "exclude")
)

Arguments

Details

Gene expression uses the shared HGNC identity index. Non-conflicting RaMP records of one gene count once. Different expression rows for a gene require an explicit aggregation choice. In custom RaMP-only compatibility mode the historical default is mean. The selected expression assay name is preserved for genes, including logcounts. Inputs, member coverage, duplicate handling and index provenance are stored in metadata(altExp(x, new_assay))$pathway_mapping.

Value

The input container with a new SingleCellExperiment::altExp(SpaMTP, new_assay). Its expression assay retains the name supplied in slot, and its rowData() describes the RaMP identities and original measured features.

Examples

utils::str(formals(createPathwayAssay))
## Create a pathway assay from metabolite data
#spamtp_obj <- createPathwayAssay(spamtp_obj, analyte_type = "metabolites", assay = "SPM", new_assay = "pathway")

## Create a pathway assay from gene data with verbose output
#spamtp_obj <- createPathwayAssay(spamtp_obj, analyte_type = "genes", assay = "SPT", new_assay = "gene_pathway", verbose = TRUE)

Description

This function computes pathway-level scores from analyte-level expression data and stores them in altExp for SingleCellExperiment-derived objects. Each score is the sum of matched expression divided by the square root of the matched feature count, then centred and scaled across pixels. Constant scores are zero; unmatched pathways are removed or stored as NA.

Usage

createPathwayObject(
  object,
  assay = NULL,
  slot = "logcounts",
  new.assay = "pathway",
  remove.nans = TRUE,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  pathway_index = NULL,
  duplicate_genes = c("error", "mean", "sum"),
  min_path_size = 1,
  max_path_size = Inf
)

Arguments

Details

Accepts gene identifiers directly or a harmonized RaMP assay. min_path_size and max_path_size are applied to measured unique members. rowData contains database_size (after identity reconciliation), database_raw_size, measured_size, coverage_fraction, used_size, database_members, measured_members, used_members and excluded_conflict_records. The complete coverage table, including excluded and unmeasured pathways, is retained in metadata(altExp(x, new.assay))$pathway_mapping$coverage. Scores summarize observed expression; they are not pathway activity tests.

Value

The input with pathwayScores in a new alternative experiment. Pathway identifiers are preserved.

Examples

utils::str(formals(createPathwayObject))

Description

Filters the provided metabolomic database by polarity and adducts

Usage

db_adduct_filter(db, adduct, polarity = "neg", verbose = TRUE)

Arguments

Value

A filtered reference metabolomic database DataFrame

Examples

# HMDB_db <- load("data/HMDB_1_names.rds")
# db_adduct_filter(HMDB_db,c("M+H"), polarity = "pos")

Description

A lightweight, dependency-free SMILES graph parser identifies common metabolite functional groups and derives interpretable positive-ion, negative-ion, alkali-binding, and neutral-mass priors. The parser is intended for candidate prioritisation rather than pKa, proton-affinity, or quantum chemistry calculations. Stereochemistry is accepted but does not change the counts.

Usage

deconvolveSMILES(
  smiles,
  backend = c("auto", "native"),
  strict = FALSE,
  workers = getOption("SpaMTP.smiles_workers", 1L)
)

Arguments

Value

A data frame with functional-group counts, ion-mode scores, and a human-readable structure_evidence field.

Examples

utils::str(formals(deconvolveSMILES))

Description

Generates a heatmap of DEMs generated from edgeR analysis run using findAllDEMs(). This function uses pheatmap to plot data.

Usage

demsHeatmap(
  edgeR_output,
  n = 5,
  only.pos = FALSE,
  FDR.threshold = 0.05,
  logfc.threshold = 0.5,
  order.by = "FDR",
  scale = "row",
  color = (grDevices::colorRampPalette(c("navy", "white", "red")))(50),
  cluster_cols = FALSE,
  cluster_rows = TRUE,
  fontsize_row = 15,
  fontsize_col = 15,
  cutree_cols = 9,
  silent = TRUE,
  plot_annotations_column = NULL,
  save_to_path = NULL,
  plot.save.width = 20,
  plot.save.height = 20,
  nlabels.to.show = NULL,
  annotation_colors = NULL
)

Arguments

Value

A heatmap plot of significantly differentially expressed metabolites defined in the edgeR ouput object.

Examples

utils::str(formals(demsHeatmap))

# demsHeatmap(DEMs)

Description

The spatial kernel uses coordinates of nonzero pixels, not intensity weights. The mass-spectrum overlay uses an intensity-weighted density without replicating each mass according to its intensity. This exploratory viewer is not a spatial significance test. Its HTML requires internet access to load the existing Plotly and JavaScript KDE libraries.

Usage

densityMap(
  object,
  assay = "main",
  slot = "counts",
  folder = getwd(),
  sampleId = NULL,
  ...
)

Arguments

Value

Invisibly, the path to mzs_density_map.html.

Examples

utils::str(formals(densityMap))

Description

SpaMTP uses public S4 accessors, not internal slots. A SpatialExperiment is a SingleCellExperiment and a SummarizedExperiment. The required container class is stated on each function's help page; spatial operations need coordinates, while paired modalities and reductions need SingleCellExperiment infrastructure. Raw, unaligned MSI spectra remain in Cardinal until binned or aligned to a common feature-by-pixel matrix.

Value

This is a documentation topic, not a callable function. The accessors described here return the selected data: assay() returns a matrix-like object, altExp() returns an alternative experiment, rowData() and colData() return DataFrame objects, and spatialCoords() returns a coordinate matrix. Replacement accessors update the corresponding data in the experiment. See each accessor's help page for its full return contract.

Experiments and expression assays

In analysis functions, assay selects the primary experiment ("main") or a named SingleCellExperiment::altExp(). The compatibility arguments slot, slots, SM.slot, ST.slot, SM_slot, ST_slot, mz.slot and pathway.slot select expression assay names within those experiments. They do not name S4 slots. Supply exact, non-empty character names, not numeric matrix positions. For example, assay = "transcriptome" and slot = "logcounts" read SummarizedExperiment::assay(SingleCellExperiment::altExp(x, "transcriptome"), "logcounts").

The expression assays within one experiment share feature and pixel axes; counts, normcounts, logcounts and scaled represent different value scales, not different modalities. Each alternative experiment has its own feature rows and the same paired pixel columns. Inspect assayNames() and altExpNames() before selecting data. No automatic alias maps data to logcounts or scale.data to scaled during matrix access.

For older analysis calls, NULL, "primary", "Spatial", "SPM", the primary experiment's mainExpName(), and its expression assay names can also select the primary experiment. An exact alternative-experiment name takes precedence over these aliases. Prefer "main" and explicit altExpNames() in new code. An expression name supplied as assay does not replace the separate slot selection. Constructors and explicit converters have their own documented naming arguments.

Metadata and spatial infrastructure

Use SummarizedExperiment::rowData() for feature annotations and SummarizedExperiment::colData() for pixel metadata. A primary experiment and each altExp() have separate rowData() and S4Vectors::metadata(). Parent pixel metadata are not automatically copied into an extracted alternative experiment. Use SingleCellExperiment::colLabels() for groups, reducedDim() for embeddings and colPair() for pixel-pair graphs. SpatialExperiment coordinates and images are accessed with spatialCoords(), imgData() and imgRaster().

Accessors have replacement forms. Standard column subsetting keeps paired alternative experiments and spatial coordinates aligned. Row subsetting changes only the selected experiment's feature space. SpaMTP does not require users to define additional S4 slots or inspect internal storage.

Optional Seurat conversion

Core analysis does not use Seurat. SeuratObject is an optional dependency for seuratToSingleCellExperiment(), seuratToSpatialExperiment() and spatialExperimentToSeurat(). Convert once at the boundary, then use the native Bioconductor workflow. Seurat assay names become experiment names; selected Seurat layers become expression assays as documented by the converter.

See Also

asSpatialExperiment(), addTranscriptome(), normalizeSMData(), scaleSMData(), runMetabolicPCA()

Examples

x <- SpatialExperiment::SpatialExperiment(
    assays = list(counts = rbind(a = 1:4, b = 4:1)),
    spatialCoords = cbind(x = 1:4, y = 0),
    colData = S4Vectors::DataFrame(row.names = paste0("p", 1:4)))
rna <- matrix(c(1, 3, 2, 4), nrow = 1,
    dimnames = list("gene1", colnames(x)))
x <- addTranscriptome(x, rna)
x <- normalizeSMData(x, "LogNormalize", assay = "transcriptome",
    slot = "counts", verbose = FALSE)
rna <- SingleCellExperiment::altExp(x, "transcriptome")
SummarizedExperiment::assayNames(rna)
SummarizedExperiment::assay(rna, "logcounts")
SummarizedExperiment::rowData(rna)$feature_label <- "example gene"
SingleCellExperiment::altExp(x, "transcriptome") <- rna
SummarizedExperiment::colData(x)$region <- c("A", "A", "B", "B")
selected <- x[, x$region == "A"]
stopifnot(identical(colnames(SingleCellExperiment::altExp(selected,
    "transcriptome")), colnames(selected)))

Description

Finds differentially expressed m/z values/metabolites between all comparison groups.

Usage

findAllDEMs(
  data,
  ident,
  n = 3,
  logFC_threshold = 1.2,
  DE_output_dir = NULL,
  run_name = "findAllDEMs",
  annotation.column = NULL,
  assay = "Spatial",
  slot = "counts",
  return.individual = FALSE,
  verbose = TRUE,
  seed = 1234,
  method = c("technical", "markers", "replicate"),
  replicate = NULL,
  contrasts = list(),
  min_region_size = 3,
  spatial_blocks = 0
)

Arguments

Value

A result list, not a modified experiment. Technical mode returns pooled values in counts and differential results in DEMs (or a list per group when return.individual = TRUE). Marker mode returns DEMs, scores, expression, group eligibility and provenance. Replicate mode returns group_means and named tests with status and, when eligible, limma tables and the biological-replicate design. Marker mode may return status = "skipped" with a reason when too few groups are eligible.

Examples

utils::str(formals(findAllDEMs))

Description

Computes Pearson correlations across matched pixels. Genes are read from altExp without assigning artificial m/z values. Constant features return NA.

Usage

findCorrelatedFeatures(
  data,
  mz = NULL,
  gene = NULL,
  ident = NULL,
  SM.assay = "main",
  ST.assay = NULL,
  SM.slot = "counts",
  ST.slot = "counts",
  nfeatures = 10,
  covariates = NULL,
  features = NULL
)

Arguments

Value

A data frame with features, correlation, modality, mz, rank and, for multiple reference groups, ident.

Examples

x <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = rbind(a = 1:4, b = 4:1)),
  rowData = S4Vectors::DataFrame(mz = c(100, 200)),
  spatialCoords = cbind(x = 1:4, y = 0),
  colData = S4Vectors::DataFrame(row.names = paste0("p", 1:4)))
findCorrelatedFeatures(x, mz = 100)

Description

Finds if any metabolite is duplicated across multiple m/z values.

Usage

findDuplicateAnnotations(data, assay = "main")

Arguments

Value

Vector of character strings describing metabolites that are assigned to multiple m/z values

Examples

utils::str(formals(findDuplicateAnnotations))

Description

Finds the nearest m/z peak to a given value in a SpaMTP Object

Usage

findNearestMZ(data, target_mz, assay = NULL)

Arguments

Value

The exact character feature identifier for the nearest measured m/z, not a numeric mass. Ties select the first feature in the stored order.

Examples

utils::str(formals(findNearestMZ))
# findNearestMZ(spe, target_mz = 400.01)

Description

This the function used to compute the gene/metabolites set enrichment for multi-omics spatial data

Usage

findRegionalPathways(
  SpaMTP,
  ident,
  DE.list = NULL,
  analyte_types = c("genes", "metabolites"),
  SM_assay = "main",
  ST_assay = "transcriptome",
  SM_slot = "counts",
  ST_slot = "counts",
  min_path_size = 5,
  max_path_size = 500,
  pval_cutoff_mets = 0.05,
  pval_cutoff_genes = 0.05,
  annotation_score_threshold = 0.05,
  annotation_source = c("current", "auto", "legacy"),
  verbose = TRUE,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  pathway_index = NULL,
  ranks = NULL,
  rank_scale = c("rms", "none"),
  nPermSimple = 1000
)

Arguments

Details

Gene mapping uses the same identity index as fishersPathwayAnalysis(). Multiple RaMP records of a gene have their memberships united. If multiple input features map to that gene with different differential statistics in one cluster, resolve the duplicate features before differential analysis; the function does not select an arbitrary statistic.

Value

A data frame of regional pathway enrichment results, not a modified experiment. The complete-ranks interface also attaches the ranks, pathway coverage, mapping and multiple-testing provenance as attributes.

Examples

utils::str(formals(findRegionalPathways))

Description

Calls ape::Moran.I with inverse squared Euclidean distance weights and zero diagonal, row-standardized by ape. Samples are analysed separately. Constant features return NA.

Usage

findSpatiallyVariableMetabolites(
  object,
  assay = "main",
  slot = "counts",
  image = NULL,
  nfeatures = 2000,
  max_spots = 5000,
  seed = 1,
  verbose = TRUE,
  sampleId = NULL
)

Arguments

Value

A SpatialExperiment with statistics, BH-adjusted p-values, flags and ranks in rowData of the selected experiment. Settings and sampled pixel IDs are stored in metadata(object)$moransi.

Examples

x <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = rbind(a = 1:6, b = c(1, 3, 5, 2, 4, 6))),
  rowData = S4Vectors::DataFrame(mz = c(100, 200)),
  spatialCoords = cbind(x = 1:6, y = 0),
  colData = S4Vectors::DataFrame(row.names = paste0("p", 1:6)))
x <- findSpatiallyVariableMetabolites(x)
getSpatiallyVariableMetabolites(x)

Description

Fisher Exact Tests for Pathway Enrichment

Usage

fishersPathwayAnalysis(
  Analyte,
  max_path_size = 500,
  min_path_size = 5,
  alternative = "greater",
  pathway_all_info = FALSE,
  pval_cutoff = NULL,
  verbose = TRUE,
  database = NULL,
  database_version = "latest",
  database_source = c("auto", "spamtpdb"),
  database_local_dir = NULL,
  universe = NULL,
  gene_mapping = c("auto", "hgnc", "ramp"),
  gene_reference = NULL,
  gene_index = NULL,
  gene_reference_version = "latest",
  gene_reference_local_dir = NULL,
  organism = "Homo sapiens",
  pathway_index = NULL,
  ...
)

Arguments

Details

With universe size U, foreground size K, pathway size M and overlap a, the test table is matrix(c(a, M - a, K - a, U - K - M + a), nrow = 2). Memberships and identifiers are deduplicated by RaMP ID. Pathway sizes are computed after intersecting with the universe; size filtering never changes that universe. All pathways passing the size limits are tested, including zero-overlap pathways. BH correction uses this complete family, before any output cutoff. Missing display metadata does not remove a test.

Unmapped input values are excluded with warnings and recorded in the enrichment attribute. An empty mapped universe is an error. An empty foreground gives p-values of 1 for all eligible pathways. If no pathways pass the size limits, a zero-row data frame is returned.

For a targeted panel, provide the measured panel as universe; the default database background is not a substitute for the detection background. m/z foreground and background values are annotated together with identical settings. All mapped candidate RaMP IDs are retained; ambiguous mass matches are not confirmed compound identifications. In a combined analysis, genes and compounds are counted as individual RaMP IDs in a single pooled test.

Value

A data frame sorted by p_val, retaining the columns pathway_name, pathway_id, type, pathwayCategory, p_val, fdr, ratio, analytes_in_pathways (overlap) and total_in_pathways (size within the universe). Also includes pathwayRampId, foreground_analytes_number and background_analytes_number. pathway_all_info = TRUE adds member information. The enrichment attribute records mapped foreground and universe IDs, unmapped inputs, excluded foreground IDs, universe source, alternative, size limits and the number of tests before output filtering. Its gene_mapping entry contains per-input mapping status, gene groups, excluded conflicting RaMP records and versioned reference provenance.

Examples

utils::str(formals(fishersPathwayAnalysis))
# For a targeted gene panel:
# fishersPathwayAnalysis(
#   Analyte = list(genes = paste0("gene_symbol:", significant_genes)),
#   universe = list(genes = paste0("gene_symbol:", measured_panel_genes))
# )

Description

Filters reference Database to only select natural elements

Usage

formula_filter(df, elements = NULL)

Arguments

Value

A refined DataFrame which only includes annotations containing the specified elements

Examples

## Get filtered DB by adduct
# db_1 <- db_adduct_filter(db, test_add_pos, polarity = "pos")

## Refine to only include natural elements
# db_2 <- formula_filter(db_1)

Description

Helper function for building a pathway db based on detected

Usage

get_analytes_db(input_id, analytehaspathway, chem_props, pathway)

Arguments

Value

A analyte database containing corresponding pathways associated with each detected metabolite

Examples

#HELPER FUNCTION

Description

Runs K-means on an embedding read through SingleCellExperiment::reducedDim() and writes pixel cluster labels to SummarizedExperiment::colData().

Usage

getKmeanClusters(
  data,
  reduction = "SpatialPCA",
  cluster.name = "spatial_clusters",
  clusters = 8,
  iter.max = 10,
  nstart = 1,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  trace = FALSE,
  seed = 888
)

Arguments

Value

The input container with cluster assignments in SummarizedExperiment::colData(data)[[cluster.name]].

Examples

utils::str(formals(getKmeanClusters))

Description

The main user-facing function to download annotations and ion images for a METASPACE dataset.

Usage

getMetaspace(
  dataset_id,
  fdr = 0.1,
  database = c("HMDB", "v4"),
  include_images = TRUE,
  api_key = NULL,
  isotope_idx = 1,
  relative = TRUE
)

Arguments

Value

A list with two elements: annotations (data.frame) and images (list of 2D matrices).

Examples

utils::str(formals(getMetaspace))
# getMetaspace(dataset_id = "2020-12-07_03h16m14s", fdr = 0.1, database= c("HMDB", "v4"), relative = TRUE)

Description

Gets values from a single metadata column for a respective m/z value.

Usage

getMZMetadata(
  obj,
  mz,
  assay = "main",
  metadata.column = "all_IsomerNames",
  separate = TRUE
)

Arguments

Value

Vector of character strings containing the metadata for a specific mz value.

Examples

utils::str(formals(getMZMetadata))
# getMZMetadata(SpaMTP, mz = "mz-100", metadata.column = "all_IsomerNames")

##### Example for getting metabolite annotation IDs for a m/z value
# getMZMetadata(SpaMTP, mz = "mz-100", metadata.column = "all_Isomers")

Description

Used to subset dataset to only include annotations that have n number of entries. For example some peaks can have multiple annotations. Peaks which have above n number of annotations assigned will be removed from Bioconductor experiment.

Usage

getRefinedAnnotations(obj, assay = "main", n = 1)

Arguments

Value

The input container with the selected experiment restricted to features having at most n annotations. Pixel columns are unchanged.

Examples

utils::str(formals(getRefinedAnnotations))
# HMDB_db <- load("data/HMDB_1_names.rds")

Description

Get the top spatially variable metabolites

Usage

getSpatiallyVariableMetabolites(object, assay = "main", n = 10)

Arguments

Value

Feature names in Moran's I rank order, excluding untested features.

Description

This function computes the coordinates of a square's four corners based on a given center point and width.

Usage

getSquareCoordinates(center_x, center_y, width, name)

Arguments

Value

A data frame with columns Selection, X, and Y representing the square's name and corner coordinates.

Examples

utils::str(formals(getSquareCoordinates))
getSquareCoordinates(center_x = 5, center_y = 5, width = 4, name = "MySquare")

Description

All matching peaks are summed per metabolite. A peak shared by overlapping mass-tolerance windows is counted once. No temporary metadata is written.

Usage

imageMZAnnotationPlot(
  object,
  metabolites,
  plusminus = NULL,
  fov = NULL,
  cols = c("lightgrey", "firebrick1"),
  size = 0.5,
  min.cutoff = NA,
  max.cutoff = NA,
  split.by = NULL,
  alpha = 1,
  border.color = "white",
  border.size = 0,
  dark.background = TRUE,
  crop = FALSE,
  cells = NULL,
  scale = c("feature", "all", "none"),
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE,
  assay = "main",
  slot = "counts",
  column.name = "all_IsomerNames",
  plot.exact = TRUE,
  plot.pixel = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

A ggplot or list of panels.

Examples

utils::str(formals(imageMZAnnotationPlot))

Description

These functions read the primary experiment or a named altExp without modifying the object. Numeric m/z queries select the nearest measured mass; character feature IDs select exact rows. A mass tolerance sums all peaks within that distance of the selected measured mass. Overlapping annotation windows count each peak once. Multiple samples are plotted separately.

Usage

imageMZPlot(
  object,
  mzs,
  plusminus = NULL,
  fov = NULL,
  cols = c("lightgrey", "firebrick1"),
  size = 0.5,
  min.cutoff = NA,
  max.cutoff = NA,
  split.by = NULL,
  alpha = 1,
  border.color = "white",
  border.size = 0,
  dark.background = TRUE,
  crop = FALSE,
  cells = NULL,
  scale = c("feature", "all", "none"),
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE,
  assay = "main",
  slot = "counts",
  plot.pixel = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

A ggplot, or a list of panels when combine is FALSE.

Examples

x <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = rbind(a = 1:4, b = 4:1)),
  rowData = S4Vectors::DataFrame(mz = c(100, 200)),
  colData = S4Vectors::DataFrame(row.names = paste0("p", 1:4)),
  spatialCoords = cbind(x = 1:4, y = c(0, 1, 0, 1)))
imageMZPlot(x, mzs = 100)

Description

The app sums every measured feature within the selected mass window without changing the input assays or annotations. The mass-axis controls use rowData masses, never mean intensities. No browser is launched by this function.

Usage

interactiveSpatialPlot(
  obj,
  assay = "main",
  slot = "counts",
  image = NULL,
  sampleId = NULL
)

Arguments

Value

A Shiny application object; use shiny::runApp to launch.

Examples

utils::str(formals(interactiveSpatialPlot))

Description

Builds a sparse adjacency matrix representing the k-nearest neighbour relationships between spots or cells, using Euclidean distance between spatial coordinates.

Usage

kNeighborsGraph(
  location,
  n_neighbors,
  platform,
  include_self = FALSE,
  alg = "kd_tree"
)

Arguments

Value

A sparse adjacency matrix (dgCMatrix) representing the k-NN graph.

Examples

utils::str(formals(kNeighborsGraph))
### HELPER FUNCTION

Description

Filters the annotation list to only include the first n number of annotations per m/z

Usage

labels_to_show(annotation_column, n = 3)

Arguments

Value

Vector containing the first n number of annotations

Description

Downloads METASPACE data, converts it to a feature matrix, and packages it in a Bioconductor SpatialExperiment. Use explicit conversion for other containers. Coordinates are stored in spatialCoords() without redundant copies in colData(); dataset and acquisition metadata remain in colData().

Usage

loadMetaspace(
  dataset_id,
  fdr = 0.1,
  database = c("HMDB", "v4"),
  api_key = NULL,
  relative = FALSE,
  transform = FALSE,
  verbose = TRUE,
  returnType = "SpatialExperiment"
)

Arguments

Value

A SpatialExperiment.

Examples

utils::str(formals(loadMetaspace))
# loadMetaspace(dataset_id = "2020-12-07_03h16m14s", fdr = 0.1, database= c("HMDB", "v4"), relative = TRUE)

Description

Raw spectra remain in Cardinal's file-backed MSImagingArrays, without asking the reader to infer a shared mass axis. Once a resolution is supplied and Cardinal has produced a common binned feature axis, the default return value is a SpatialExperiment. Seurat conversion is explicit and optional.

Usage

loadSM(
  file,
  mass.range = NULL,
  resolution = NA,
  units = "ppm",
  verbose = TRUE,
  assay = "Spatial",
  multi.run = FALSE,
  returnType = c("auto", "Cardinal", "SpatialExperiment")
)

Arguments

Value

A Cardinal MSImagingArrays/MSImagingExperiment, or a SpatialExperiment.

Examples

utils::str(formals(loadSM))
# data <-loadSM(name = "run1", folder = "/Documents/SpaMTP_test_data/", mass.range = c(160,1500), resolution = 10, assay = "Spatial")

Description

Loads a coherent group of annotation resources from SpaMTPdb. Retrieved resources are cached for the current R session. A named custom bundle can be supplied for offline, testing, or user-curated workflows.

Usage

loadSpaMTPDatabase(
  resources = c("chem_props", "source_df", "analyte", "analytehaspathway", "pathway"),
  version = "latest",
  source = c("auto", "spamtpdb"),
  database = NULL,
  local_dir = NULL,
  hub = NULL,
  offline = FALSE,
  refresh = FALSE,
  verify = TRUE
)