Package index • SpaMTP

Loading Spatial Metabolic Data into a SpaMTP Seurat Object

Functions that allow the user to load in SM data in different formats

LoadSM(): Loads spatial metabolic data into a SpaMTP Seurat Object
ReadSM_mtx(): Read Spatial Metabolomics matrix file (.csv format)
Load_METASPACE(): Load METASPACE Data and Create Seurat Object

Converting Between Data Objects

Functions that allow the user to convert between SpaMTP Seurat Objects and Cardinal Objects

CardinalToSeurat(): Converts a Cardinal Object into a SpaMTP Seurat Object
ConvertSeuratToCardinal(): Converts a SpaMTP Seurat object to a Cardinal object, including annotations and metadata

Loading Versioned Annotation Databases

Functions for inspecting and loading bundled RaMP annotation resources

LoadSpaMTPDatabase(): Load versioned SpaMTP annotation resources
SpaMTPDatabaseInfo(): Inspect SpaMTP database resources
BundledDatabases chem_props source_df analyte analytehaspathway pathway ramp_db_metadata smiles_features RAMP_hmdb RAMP_kegg RAMP_Reactome RAMP_wikipathway HMDB_db Chebi_db Lipidmaps_db GNPS_db filtered_fmp10: Bundled annotation and pathway databases

Binning Spatial Metabolomic Data

Functions that bin m/z values into a lower resolution/wider peak.

BinSpaMTP(): Bin SpaMTP Object

Annotating m/z Masses

Functions required for performing and handling m/z annotation using a reference metabolic database

AnnotateSM(): Annotates m/z values stored in a SpaMTP Object
AnnotateMZ(): Annotate one or more observed m/z values
DeconvolveSMILES(): Decompose explicit functional groups in SMILES strings
AnnotateSMILESStructure(): Add SMILES-derived structural features to a metabolite database
PredictAdductsFromSMILES(): Predict a structure-aware adduct search space from SMILES
AdductRules(): Return the validated SpaMTP adduct rule table
MALDIMatrixProfiles(): List MALDI matrix and on-tissue derivatization profiles
MALDIMatrixRules(): Select annotation rules from the MALDI matrix/reagent profile
BuildMZAnnotationIndex(): Build a reusable indexed metabolite annotation search space
QueryMZAnnotationIndex(): Query an indexed metabolite annotation search space
AnnotationInfo(): Inspect the metabolite annotation provenance stored in a SpaMTP object
print(<spamtp_mz_index>): Print an indexed metabolite annotation search space
AddCustomMZAnnotations(): Assign custom annotations to m/z values
AddFMP10Annotations(): Annotates FMP10 matrix data
SearchAnnotations(): Find Annotation
GetMZMetadata(): Gets values from a single metadata column for a respective m/z value.
FindDuplicateAnnotations(): Finds if any metabolite is duplicated across multiple m/z values.
SubsetMZFeatures(): Subset a SpaMTP Seurat Spatial Metabolomic object by a list of m/z's
AnnotateBigData(): Annotates vector of m/z values
getRefinedAnnotations(): Refines and reduces m/z annotations

Simplify Annotations

Functions required for simplifying m/z annotation results

CalculateAnnotationStatistics(): Calculate annotation statistics for all m/z value suggesting the most likely metabolite based on correlated pathway expression.
CalculateSingleAnnotationStatistics(): Calculate annotation statistics for a single m/z value suggesting the most likely metabolite based on correlated pathway expression.
Pseudo_msms(): Find all library spectra10 above a cosine threshold for each imaging peak, restricting to a global precursor-mz range.
CreatePathwayObject(): Create a SpaMTP Seurat Object containing expression values for all present pathways

Simplifying Lipid Nomenclature

Function used to simplify lipid names into general lipid categories, classes, and more

RefineLipids(): Uses common lipid nomenclature to simplify lipid annotations

Analysis of Differentially Expressed Peaks

Functions required for performing pseudo-bulking differential expression analysis

FindAllDEMs(): Finds differentially expressed m/z values/metabolites between all comparison groups.

Visualising DEPs Analysis

Functions used to generate a heatmap from DEP results

DEMsHeatmap(): Heatmap of Differentially Expressed Metabolites

Metabolic and Transcriptomic Pathway Analysis

Functions used to perform pathway analysis, both PCA and metabolite/gene set-based (GSEA)

FishersPathwayAnalysis(): Calculates Significant Metabolic Pathways using a Fisher Exact Test
FindRegionalPathways(): Regional Pathway Enrichment
RunRAMPgeseca(): Runs multilevel Monte-Carlo variant for performing gene sets co-regulation analysis using the RAMP_DB metabolite/gene database.
CreatePathwayAssay(): Create a Pathway Assay from Gene or Metabolite Data

Metabolic and Transcriptomic Pathway Visualisation

Functions used to visualise pathway analysis results

VisualisePathways(): Visualise Significant Pathways
PlotRegionalPathways(): Plot significantly enriched pathways per region
PlotPathways(): Plots the expression profile of a feature set corresponding to specified pathways onto a 2D scatter plot based on a dimensionality reduction technique.
PlotPathwaysSpatially(): Plots the spatial expression profile of a feature set corresponding to specified pathways

Pathway Network Plotting

Functions used to generate network plots for specified patheays

PathwayNetworkPlots(): Construct an interactive pathway network visualization

Dimentionality Reduction Analysis

Functions that are used for calculating PCA embeddings and projections based on SM and/or ST data

RunMetabolicPCA(): Generates PCA analysis results for a SpaMTP Seurat Object
RunSpatialGraphPCA(): Perform Dimensionality Reduction using Graph-Regularised PCA on Spatial Data
GetKmeanClusters(): Perform K-means clustering on a specified reduction

SpaMTP Metabolic Data Visualisation

Functions that can be used to visualize data from SpaMTP object

ImageMZPlot(): Plot expression of m/z values spatially
ImageMZAnnotationPlot(): Plot expression of annotated metabolites spatially
SpatialMZPlot(): Plot expression of m/z values spatially for a Spatial SpaMTP Seurat Objects.
SpatialMZAnnotationPlot(): Plot expression of metabolites in spatially from a Spatial SpaMTP Objects.
Plot3DFeature(): Generates a 3D spatial feature plot from a SpaMTP object
MassIntensityPlot(): Plot mass intensity spectra
DensityMap(): Generates interactive 3D spatial density plot for m/z values
CheckAlignment(): Check multi-modal coordinate alignment

Interactive Spatial Binning Visualisation

Interactive plot that displays spatial changes to m/z intensity values based on changes to bin size.

InteractiveSpatialPlot(): Interactive Spatial Plot for visualising different m/z bin sizes

Additional SpaMTP Functions

Functions that can be used to find the closest metabolite and bin the expression of multiple metabolites into one

FindNearestMZ(): Finds the nearest m/z peak to a given value in a SpaMTP Object
BinMetabolites(): Sums the intensity values of multiple m/z values into one

Spatial Analysis of Metabolomic Data

Functions used to identify spatially correlated features (metabolites/genes)

FindCorrelatedFeatures(): Find top features and metabolites that are strongly correlated with a given feature
FindSpatiallyVariableMetabolites(): Find Spatially Variable Metabolites
GetSpatiallyVariableMetabolites(): Get top spatially variable metabolites
RowVar(): Compute the row variances for each m/z value

Multi-Omic Data Integration

Functions used to Align, Map and Integrate Spatial Metabolomic and Transcriptomic data

MapSpatialOmics(): Maps Spatial Metabolomic (MALDI) data to corresponding Spatial Transcriptomics data and coordinates.
ApplySpatialAlignment(): Apply automatic or precomputed spatial alignment
AlignSpatialOmics(): Interactive app for SM and ST coordinate alignment
MultiOmicIntegration(): Mult-Omic data integration
CreateMergedModalityAssay(): Create a singular multiomics assay by merging data from multiple assays.

Adding Image Data

Functions used to align and add an image to a spatial metabolic SpaMTP object.

AddSMImage(): Manually align an image (e.g. H&E, Immuno) to a SM SpaMTP dataset

ROI Selection

Functions used subset or select specific spatial regions of interest.

SelectROIs(): Launch an Interactive ROI Annotation App for Seurat Spatial Data

Pre-Processing SpaMTP Metabolic Data

Functions for normalising and visualising the pre-processing of SpaMTP datasets

NormalizeSMData(): Normalizes m/z intensity data stored in a SpaMTP Seurat Object
TMMNormalize(): Performs TMM normalization between categories based on a specified ident
MZRidgePlot(): Generates a ridge plot of spatial metabolic intensity data
MZVlnPlot(): Generates a violin plot of spatial metabolic intensity data
MZBoxPlot(): Generates a Boxplot of spatial metabolic intensity data

Exporting SpaMTP Data

Function to export SpaMTP data in .mtx, barcodes.csv, features.csv, metadata.csv, and feature.metadata.csv files

SaveSpaMTPData(): Saves SpaMTP Object

Bundled Annotation and Network Constants

Small compatibility tables used by annotation and pathway visualisation

adduct_file: Common adduct constants
reaction_type: Pathway-network reaction styles

Cardinal Wrapper Functions

Functions used to alter Cardinal Objects.

add_ssc_annotation(): Adds Cardinal ssc segmentation annotation to m/z count data object

Additional Worker Functions

Various helper functions used by SpaMTP

subset_SPM(): Subsets SpaMTP Seurat Object containing FOVs
check_cardinal_version(): Check Cardinal Version

Utility Functions

Binning Helper Functions

Helper functions for binning m/z intensity spectra for SpaMTP objects

BinnedCardinalToSeurat(): Converts a SpaMTP binned Cardinal Object into a SpaMTP Seurat Object
spectral_binning(): Spectral binning of intensity values stored in a Matrix object, converted from matter.

Plotting Helper Functions

Helper functions used for SpaMTP data visualisation

bin.mz(): Bins multiple m/z values into one.
plusminus(): Identifies all mz peaks within a plus-minus range of the target_mz
plot_plus_minus(): Helper Function to generate merged counts within the plus minus range provided
check_column_type(): Helper function to determine if a column contains categorical or continuous Data
pixelPlot(): Helper function for converting Seurat Class ggplots from spot to pixel layout

Annotation Helper Functions

Helper functions required for running ‘AnnotateSM()’

annotateTable(): Annotates m/z values stored in a data.frame based on reference metabolite dataset
labels_to_show(): Filters the annotation list to only include the first n number of annotations per m/z
add_backslashes_to_specialfeatures(): Adds in backslashes required to take into account special using grepl such as brackets and +
check_and_truncate_adduct_vector(): Checks if the complete adduct is in the data base, else returns a truncated adduct
db_adduct_filter(): Filters the provided metabolomic database by polarity and adducts
formula_filter(): Filters reference Database to only select natural elements
calculate_bounds(): Calculates the mz range of the observed_df
ppm_error(): Calculates the ppm error as a valve
ppm_range_match(): Check whether m/z values match within a ppm tolerance

METASPACE Loader helper functions

Helper functions required for running ‘Load_METASPACE()’

metaspace_client(): Metaspace R Client Constructor
get_metaspace(): Core METASPACE Data Retrieval
metaspace_to_feature_matrix(): Convert METASPACE Images to Feature Matrix

Differential Abundance Helper Functions

Helper functions for calculating and plotting differentially expressed metabolites

run_pooling(): Pools SpaMTP Seurat object into random pools for pseudo-bulking.
run_DE(): Runs EdgeR analysis for pooled data
save_pheatmap_as_pdf(): Saves a DEMsHeatmap as a PDF

Pathway Analysis Helper Functions

Helper functions used for running pathway analysis

PlotSinglePathway(): Plots the expression profile of a feature set corresponding to specified pathways onto a 2D scatter plot based on a dimensionality reduction technique.
PlotSinglePathwaySpatially(): Plot expression profile of a single RAMP pathway spatially
addGesecaScores(): Add GESECA Scores to SpaMTP Object
get_analytes_db(): Helper function for building a pathway db based on detected
list_to_pprcomp(): Creates a pprcomp object based on an input list

Multi-Omic Helper Functions

Helper functions used in multi-omic analysis functions

kneighbors_graph(): Construct a k-Nearest Neighbour Graph from Spatial Coordinates.
get_square_coordinates(): This function computes the coordinates of a square's four corners based on a given center point and width.
lowresMapping(): Maps SM pixels to low resolution ST data
hiresMapping(): Maps SM pixels to high resolution ST data
combine.tr(): Combines rigid tranformation matrices

Pre-Processing Helper Functions

Helper functions for generating pre-processing plots

statPlot(): Helper function for QC plots by generating intensity count data