SpaMTP

https://genomicsmachinelearning.github.io/SpaMTP/
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SpaMTP is an R package designed for the integrative statistical analysis, metabolite annotation, and...
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### Cardinal installation error output

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Example error output when Cardinal fails to install due to missing EBImage dependency and fftw3.h header.

```text
ERROR: dependency ‘EBImage’ is not available for package ‘Cardinal’

BiocManager::install("EBImage")

fftwtools.c:28:9: fatal error: fftw3.h: No such file or directory
   28 | #include<fftw3.h>
      |         ^~~~~~~~~
```

--------------------------------

### Example call with transform = TRUE

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/metaspace_to_feature_matrix.html

Example call to metaspace_to_feature_matrix with transform = TRUE, as shown in the documentation examples section.

```R
# metaspace_to_feature_matrix(mtx, transform = TRUE)
```

--------------------------------

### Cairo installation error output

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Example error output when Cairo fails to install due to missing cairo.h. Use this to identify the issue.

```text
checking if R was compiled with the RConn patch... no
checking for cairo.h... no
configure: error: Cannot find cairo.h! Please install cairo (http://www.cairographics.org/) and/or set CAIRO_CFLAGS/LIBS correspondingly.
ERROR: configuration failed for package ‘Cairo’
```

--------------------------------

### MZRidgePlot example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/MZRidgePlot.html

Example call to MZRidgePlot, grouping by the 'sample' metadata column. This is the only example provided.

```r
# MZRidgePlot(SeuratObj, group.by = "sample")
```

--------------------------------

### pixelPlot example with SpatialFeaturPlot

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/pixelPlot.html

Example call to pixelPlot, commented out, converting the output of SpatialFeaturPlot to pixel layout. The example is not executed.

```r
#pixelPlot(SpatialFeaturPlot(SpaMTP.obj, features = "nFeature_Spatial"))
```

--------------------------------

### Example: Filtering and running proc_db

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/proc_db.html

Commented example showing how to load a database, filter by adduct (M+H) and natural elements, then run proc_db with ppm_threshold = 5. The example is commented out and uses placeholder objects.

```r
# HMDB_db <- load("data/HMDB_1_names.rds")
# mz_df <- SeuratObject[["Spatial"]][["mz"]]
# mz_df$row_id <- seq(1, length(mz_df$mz))

## 1) Filter DB by adduct.
# db_1 <- db_adduct_filter(HMDB_db, c("M+H"), polarity = "pos")

## 2) only select natural elements
# db_2 <- formula_filter(db_1)

## 3) search db against mz df return results
# db_3 <- proc_db(mz_df, db_2, ppm_threshold = 5)
```

--------------------------------

### DEMsHeatmap example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/DEMsHeatmap.html

Example calls to FindAllDEMs and DEMsHeatmap, commented out. The example shows how to generate a heatmap from edgeR output.

```R
# DEMs <- FindAllDEMs(SeuratObj, "sample")

# DEMsHeatmap(DEMs)
```

--------------------------------

### BinSpaMTP example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/BinSpaMTP.html

Example call to BinSpaMTP with a resolution of 10 ppm and return.only.mtx = TRUE, which returns only the binned intensity matrix. The example is commented out in the source.

```r
#BinSpaMTP(spamtp.obj, resolution = 10, units = "ppm", return.only.mtx = TRUE)
```

--------------------------------

### metaspace_client example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/metaspace_client.html

Example of creating a client, commented out. Use this to instantiate the client for querying METASPACE data.

```r
# ms <- metaspace_client()
```

--------------------------------

### Install rgoslin via mamba

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Alternative installation of rgoslin using mamba.

```bash
mamba install bioconductor-rgoslin
```

--------------------------------

### LoadSM example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/LoadSM.html

Example call to LoadSM, commented out, showing how to specify file name, folder, mass range, resolution, and assay. The example is provided in the documentation but is not executed.

```R
# data <-LoadSM(name = "run1", folder = "/Documents/SpaMTP_test_data/", mass.range = c(160,1500), resolution = 10, assay = "Spatial")
```

--------------------------------

### AddFMP10Annotations example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/AddFMP10Annotations.html

Example call to AddFMP10Annotations with only.fmp.adduct set to FALSE. This is the only example provided in the documentation.

```R
# AddFMP10Annotations(spamtp, only.fmp.adduct = FALSE)
```

--------------------------------

### run_DE example with run_pooling

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/run_DE.html

Example of running run_DE() on pooled data, with commented-out run_pooling() call. The example uses n=3, logFC_threshold=1.2, annotation.column='all_IsomerNames', and assay='Spatial'.

```r
# pooled_obj <- run_pooling(SeuratObj, "sample", n = 3)
# run_DE(pooled_obj, SeuratObj, "sample", "~/Documents/DE_output/", "run_1", n = 3, logFC_threshold = 1.2, annotation.column = "all_IsomerNames", assay = "Spatial")
```

--------------------------------

### Install SpaMTP and load libraries

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/Single_Cell_MultiOmics.html

Install SpaMTP from GitHub if not already installed, then load required libraries: SpaMTP, Cardinal, Seurat, dplyr, and ggplot2.

```R
## Install SpaMTP if not previously installed
if (!require("SpaMTP"))
    devtools::install_github("GenomicsMachineLearning/SpaMTP")

#General Libraries
library(SpaMTP)
library(Cardinal)
library(Seurat)
library(dplyr)
library(ggplot2)
```

--------------------------------

### Install SpaMTP from GitHub with devtools

Source: https://genomicsmachinelearning.github.io/SpaMTP/index.html

Installs the current version of SpaMTP from the GenomicsMachineLearning/SpaMTP repository using devtools. Requires devtools to be installed first.

```r
if (!require("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("GenomicsMachineLearning/SpaMTP")
```

--------------------------------

### SpatialMZPlot example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/SpatialMZPlot.html

Example calls to SpatialMZPlot, showing how to plot specific m/z values and optionally set a plusminus threshold. The examples are commented out.

```r
# SpatialMZPlot(SeuratObj, mzs = c(400.678, 300.1))
# SpatialMZPlot(SeuratObj, mzs = c(400.678, 300.1), plusminus = 0.05)
```

--------------------------------

### SpatialMZAnnotationPlot usage examples

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/SpatialMZAnnotationPlot.html

Two commented example calls showing how to plot metabolites with and without a plusminus threshold. The examples are commented out and use a placeholder SeuratObj.

```R
# SpatialMZAnnotationPlot(SeuratObj, mzs = c("Glucose", "Glutamine"))
# SpatialMZAnnotationPlot(SeuratObj, mzs = c("Glucose", "Glutamine"), plusminus = 0.05)
```

--------------------------------

### CalculateSingleAnnotationStatistics example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/CalculateSingleAnnotationStatistics.html

Example usage with commented-out code. The example shows how to create a pathway object and then calculate annotation statistics for a specific m/z value. The code is commented out, indicating it is not run directly.

```r
#data <- CreatePathwayObject(data,assay="SPT_pathway",slot = "scale.data")
#CalculateSingleAnnotationStatistics(mz = "mz-674.2805",data = data,mz.assay = "SPM",pathway.assay = "pathway",mz.slot = "scale.data")
```

--------------------------------

### Create conda environment and install SpaMTP dependencies

Source: https://genomicsmachinelearning.github.io/SpaMTP/index.html

Creates a conda environment with Python 3.10, R 4.4, and ImageMagick, then installs all required R and Bioconductor packages for SpaMTP. Finally runs dependencies.R to install SpaMTP.

```bash
conda create --prefix [some-directory]/conda/spamtp python=3.10 r-base=4.4 imagemagick --y
conda activate [some-directory]/conda/spamtp
conda install -c conda-forge jupyter libconfig fontconfig freetype libtiff r-irkernel  --y
conda install --no-update-deps -c conda-forge r-here r-matrix r-Seurat r-SeuratObject r-hdf5r r-ggplot2 r-reshape2 r-patchwork fftw r-RCurl r-jpeg r-locfit r-ontologyindex --y
conda install --no-update-deps -c conda-forge r-fields r-ggdendro r-ggnewscale r-magick r-naturalsort r-pheatmap r-sf r-shinyjs r-zeallot r-r.utils --y
conda install --no-update-deps -c bioconda bioconductor-BiocGenerics bioconductor-enhancedvolcano bioconductor-biocparallel --y
conda install --no-update-deps -c bioconda bioconductor-protgenerics bioconductor-s4vectors bioconductor-biobase --y
conda install --no-update-deps -c bioconda bioconductor-edger bioconductor-fgsea bioconductor-limma bioconductor-scater bioconductor-singlecellexperiment --y
conda install --no-update-deps -c bioconda bioconductor-delayedmatrixstats bioconductor-hdf5array bioconductor-rhdf5 bioconductor-rhdf5lib --y
conda install --no-update-deps -c bioconda bioconductor-msnbase bioconductor-ebimage==4.48.0-0 --y
Rscript dependencies.R
```

--------------------------------

### Example: save_pheatmap_as_pdf call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/save_pheatmap_as_pdf.html

Example call to save a pheatmap plot to a PDF file. The filename argument is a full filepath.

```r
# save_pheatmap_as_pdf(pheatmap, filename = "/Documents/plots/pheatmap1")
```

--------------------------------

### DensityMap example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/DensityMap.html

Example call to DensityMap with a SpaMTP Seurat object. The actual object is commented out.

```r
# DensityMap(SpaMTP.obj)
```

--------------------------------

### Install rgoslin via BiocManager

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Installs the rgoslin package using BiocManager.

```R
BiocManager::install("rgoslin")
```

--------------------------------

### Install and import R libraries for SpaMTP analysis

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/Annotation_Refinement.html

Installs SpaMTP if not already installed and loads required R libraries including SpaMTP, Cardinal, Seurat, dplyr, tidyr, ggplot2, EnhancedVolcano, and viridis. Run this before any SpaMTP analysis.

```R
## Install SpaMTP if not previously installed
if (!require("SpaMTP"))
    devtools::install_github("GenomicsMachineLearning/SpaMTP")

#General Libraries
library(SpaMTP)
library(Cardinal)
library(Seurat)
library(dplyr)
library(tidyr)

#For plotting + DE plots
library(ggplot2)
library(EnhancedVolcano)
library(viridis)
```

--------------------------------

### AddCustomMZAnnotations example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/AddCustomMZAnnotations.html

Example usage of AddCustomMZAnnotations, commented out. It shows passing a SpaMTP object and an annotation data frame.

```r
# annotated_data <- AddCustomMZAnnotations(SpaMTP.obj, annotation.df)
```

--------------------------------

### Install and load required R packages

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/human_brain_tissue_analysis.html

Loads SpaMTP, Seurat, dplyr, ggplot2, viridis, and patchwork for the tutorial. Must be run before any analysis steps.

```R
library(SpaMTP)
library(Seurat)
library(dplyr)
library(ggplot2)
library(viridis)
library(patchwork)
```

--------------------------------

### Install r-cairo via conda

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Installs the r-cairo package via conda-forge to resolve Cairo installation issues.

```bash
conda install conda-forge::r-cairo
```

--------------------------------

### bin.mz example (commented out)

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/bin.mz.html

Example usage of bin.mz, commented out. It shows how to call bin.mz with a Seurat object and a vector of m/z values.

```r
# mz_values <- plusminus(SeuratObj, 448.2, 0.05)
# bin.mz(SeuratObj, mz_values)
```

--------------------------------

### PlotSinglePathwaySpatially example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/PlotSinglePathwaySpatially.html

Example usage of PlotSinglePathwaySpatially with a glycolysis pathway list and a SpaMTP Seurat object. The example is commented out.

```R
#glycolysis_list <- list("Glycolysis" = c("RAMP_C_000218730","RAMP_G_000012583","RAMP_G_000001171","RAMP_G_000007564","RAMP_C_000218226","RAMP_C_000040403","RAMP_C_000001115","RAMP_G_000008859"))
#PlotSinglePathwaySpatially(glycolysis_list, spamtp_obj)
```

--------------------------------

### Build SpaMTP Apptainer image from definition file

Source: https://genomicsmachinelearning.github.io/SpaMTP/index.html

Builds a SpaMTP Apptainer image from scratch using the spamtp.def definition file. Requires environment-linux.yml, dependencies.R, and spamtp.def files.

```bash
apptainer build spamtp.sif spamtp.def
```

--------------------------------

### Plot3DFeature example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/Plot3DFeature.html

Example call to Plot3DFeature with two features, specifying assays and an image to display. The example is commented out.

```R
# Plot3DFeature(data = my_data, features = c("gene1", "gene2"), assays = c("SPT", "SPM"), show.image = "slice1")
```

--------------------------------

### Load_METASPACE example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/Load_METASPACE.html

Example call to Load_METASPACE with a dataset ID, FDR threshold, database, and relative normalization enabled. The code is commented out.

```r
# Load_METASPACE(dataset_id = "2020-12-07_03h16m14s", fdr = 0.1, database= c("HMDB", "v4"), relative = TRUE)
```

--------------------------------

### Example: CreateMergedModalityAssay usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/CreateMergedModalityAssay.html

Example call to CreateMergedModalityAssay, merging assays 'SPM' and 'SPT' into a new assay named 'merged'. The example is commented out.

```r
# merged_obj <- CreateMergedModalityAssay(SpaMTP = spamtp_obj, assays.to.merge = c("SPM", "SPT"),new.assay = "merged")
```

--------------------------------

### Example: Filter database to natural elements

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/formula_filter.html

Example workflow: first filter the database by adduct (commented out), then refine to only include natural elements using formula_filter.

```R
## 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)
```

--------------------------------

### FindRegionalPathways example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/FindRegionalPathways.html

Example call to FindRegionalPathways. Note: the example uses a 'polarity' argument that is not documented in the function signature; it may be outdated or incomplete.

```R
# SpaMTP = FindRegionalPathways(SpaMTP, polarity = "positive")
```

--------------------------------

### get_metaspace example call

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/get_metaspace.html

Example call to get_metaspace with a specific dataset ID, FDR threshold, database, and relative normalization. The call is commented out, indicating it is for demonstration.

```R
# get_metaspace(dataset_id = "2020-12-07_03h16m14s", fdr = 0.1, database= c("HMDB", "v4"), relative = TRUE)
```

--------------------------------

### FishersPathwayAnalysis example calls for mzs, metabolites, and combined modes

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/FishersPathwayAnalysis.html

Commented examples showing how to run the function in 'mzs' mode, 'metabolites' mode, and combined 'metabolites' and 'genes' mode. These are illustrative and not executable as-is.

```r
## Running in 'mzs' mode:
# FishersPathwayAnalysis(Analyte = list("mzs" = mz_values), ppm_error = 3)

## Running in 'metabolites' mode
# FishersPathwayAnalysis(Analyte = list("metabolites" = metabolite_ids))

## Running 'metabolites' and 'genes' combined
# FishersPathwayAnalysis(Analyte = list("metabolites" = metabolite_ids, "genes" = gene_names))
```

--------------------------------

### RunRAMPgeseca example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/RunRAMPgeseca.html

Example usage commented out. It shows how to run the function with minSize=15 and maxSize=500 on a feature loadings matrix from a SpaMTP object.

```r
# E <- SpaMTP@reductions$pca.rev@feature.loadings
# sig_pathways <- RunRAMPgeseca(E, minSize=15, maxSize=500)
```

--------------------------------

### PlotSinglePathway example usage

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/PlotSinglePathway.html

Commented example showing how to call PlotSinglePathway with a glycolysis pathway list and a SpaMTP Seurat object, using the 'umap' reduction. The pathway list is commented out, indicating it should be defined beforehand.

```r
#glycolysis_list <- list("Glycolysis" = c("RAMP_C_000218730","RAMP_G_000012583","RAMP_G_000001171","RAMP_G_000007564","RAMP_C_000218226","RAMP_C_000040403","RAMP_C_000001115","RAMP_G_000008859"))
#PlotSinglePathway(glycolysis_list, spamtp_obj, reduction = "umap")
```

--------------------------------

### MassIntensityPlot examples

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/MassIntensityPlot.html

Four commented examples showing different ways to call MassIntensityPlot: plotting the whole tissue, grouping by 'ssc', splitting by 'ssc' with m/z labels, and splitting with metabolite annotations. Note that the examples are commented out and the last example uses a 'plot.layout' argument not in the function signature.

```R
## Plot mean of whole tissue section
# MassIntensityPlot(SeuratObj)

## Plot ssc segmentation groups on the same plot
# MassIntensityPlot(SeuratObj, group.by = "ssc")

## Plot mean of each ssc segmentation of separate plot with mz annotations
# MassIntensityPlot(SeuratObj, split.by= "ssc", mz.labels = c(329.166), plot.layout = c(5,2))

## Plot mean of each ssc segmentation of separate plot with metabolite annotations
# MassIntensityPlot(SeuratObj, split.by= "ssc", mz.labels = c(329.166), label.annotations = TRUE)
```

--------------------------------

### ConvertSeuratToCardinal example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/ConvertSeuratToCardinal.html

Example call to ConvertSeuratToCardinal with feature.metadata = TRUE. The code is commented out.

```R
# cardinal.obj <- ConvertSeuratToCardinal(SeuratObject, feature.metadata = TRUE)
```

--------------------------------

### AnnotateBigData example with Cardinal data

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/AnnotateBigData.html

Example workflow: reads an imzML file with Cardinal, extracts m/z values, annotates them with HMDB_db at 3 ppm tolerance and negative polarity adducts, subsets the Cardinal object to annotated peaks, and converts to a SpaMTP Seurat object. The code is commented out.

```R
#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)
#SpaMTP_data <- CardinalToSeurat(cardinal_subset)
```

--------------------------------

### GetMZMetadata examples

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/GetMZMetadata.html

Two commented example calls. The first retrieves all isomer names for m/z 'mz-100'. The second retrieves metabolite annotation IDs for the same m/z value using the 'all_Isomers' column.

```r
# 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")
```

--------------------------------

### RefineLipids usage example

Source: https://genomicsmachinelearning.github.io/SpaMTP/reference/RefineLipids.html

Example call to RefineLipids with a data frame containing lipid annotations. The function simplifies annotations to general terms, groups, and classes. The example is commented out and uses default parameters.

```R
# RefineLipids(DEPs_df)
```

--------------------------------

### Install Cardinal directly via conda

Source: https://genomicsmachinelearning.github.io/SpaMTP/articles/SpaMTP.html

Installs Cardinal directly from bioconda if other methods fail.

```bash
conda install bioconda::bioconductor-cardinal  
```
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