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)

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

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

AddCustomMZAnnotations(): Assign custom annotations to m/z values

AddFMP10Annotations(): Annotates FMP10 matrix data

SearchAnnotations(): Find Annotation

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

getRefinedAnnotations(): Refines and reduces m/z annotations

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

Metabolic and Transcriptomic Pathway Visualisation

Functions used to visualise pathway analysis results

VisualisePathways(): Visualise Significant Pathways

PlotRegionalPathways(): Plot significantly enriched pathways per region

Pathway Network Plotting

Functions used to generate network plots for specified patheays

PathwayNetworkPlots(): Constructs an interactive network for exploring spatial metabolomics and transcriptomics data.

PCA Metabolite Analysis

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

RunMetabolicPCA(): Generates PCA analysis results for a SpaMTP Seurat Object

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.

AlignSpatialOmics(): Interactive app for SM and ST coordinate alignment

MultiOmicIntegration(): Mult-Omic data integration

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

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

Reference Metabolite Datasets

Metabolite datasets used for annotating m/z masses

HMDB_db: HMDB_db: A cleaned version of the reference metabolomics dataset from the Human Metabolome Database (HMDB)

Lipidmaps_db: Lipidmaps_db: A cleaned version of the lipid database from LIPID MAPS

Chebi_db: Chebi_db: Cleaned ChEBI (Chemical entities of biological interest) reference dataset

GNPS_db: GNPS_db: A cleaned database of metabolites from GNPS

filtered_fmp10: filtered_fmp10: data.frame containing FMP10+ metabolite mappings

Metabolic Pathway Datasets

Various datasets required for pathway analysis

adduct_file: adduct_file: A dataframe containing possible adducts used for pathway analysis

analyte: analyte: A dataframe containing ID's of possible RAMP analytes

analytehaspathway: analytehaspathway: A dataframe containing RAMP_pathway ID's

chem_props: chem_props: A database containing the chemical properties and metadata of each RAMP_DB analyte

pathway: pathway: A dataframe containing RAMP_DB pathways and their relative metadata

source_df: source_df: A dataframe containing source information about RAMP_ID analyte used for analysis

Pathway Network Datasets

Various datasets required for generated pathway network plots

RAMP_hmdb: RAMP_hmdb: A list containing network plot information about pathways from the HMDB database

RAMP_Reactome: RAMP_Reactome: A list containing network plot information about pathways from the Reactome database

RAMP_kegg: RAMP_kegg: A list containing network plot information about pathways from the KEGG database

RAMP_wikipathway: RAMP_wikipathway: A list containing network plot information about pathways from the Wiki database

reaction_type: reaction_type: data.frame containing reaction type mappings

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

verbose_message(): Helper function for suppressing function progress messages

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.

bin_SpectralImagingExperiment(): Generate binned intensity matrix based on reference peaks generated via Cardinal

bin_cardinal(): Bins spectral data generated by Cardinal from a matter object to a Matrix object

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 sotred 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

is_formula_valid(): Checks if a formula contains only the allowed elements

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()

Calculates the ppm range and check if mz values are within the range

Returns TRUE if match is found and false if no match.

proc_db(): Searches observed mz values against the data base list and returns matching annotations

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

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

rescale(): Helper function to rescale a sampled matrix

ResizeMat(): Helper function to resize a matrix back to its original layout after sampling

Multi-Omic Helper Functions

Helper functions used in multi-omic analysis functions

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

translate(): Creates a transformation matrix that translates an object in 2D

mirror(): Creates a transformation matrix that mirrors an object in 2D along either the x axis or y axis around its center of mass

stretch(): Stretch along angle

rigid.rot(): Creates a transformation matrix for rotation

rigid.transf(): Creates a transformation matrix for rotation and translation

rigid.transl(): Creates a transformation matrix for translation with an offset of (h, k)

rigid.refl(): Creates a transformation matrix for reflection

rigid.stretch(): Creates a transformation matrix for stretching

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