Getting Started • SpaMTP

Listed below are a number of useful tutorials demonstrating how to use SpaMTP when analysing your spatial metabolomic datasets. For more documentation of each function used in these tutorials, please visit our Reference page.

Useful SpaMTP Vignettes

For a general introduction in importing data, annotating metabolites and running key analysis pipelines on your spatial metabolomic data, we suggest starting with the Spatial Metabolomic Analysis Tutorial. This vignette uses mouse bladder data and demonstrates how to perform general tasks such as plotting, subsetting, and manipulating SpaMTP Seurat objects. For those with paired multi-modal datasets, we suggest working through the Multi-Modal Integration Tutorial. Here, we provide methods to align your two datasets to the same coordinates, map spatial metabolomic pixels to spatial transcriptomic spots and then run various analysis using this integrated data.

Spatial Metabolomics Analysis using SpaMTP	Multi-Modal Integration of Spatial Data using SpaMTP	Additional SpaMTP Functionality

An introduction to analysing spatial metabolomic datasets using SpaMTP.	Align, map and integrate spatial metabolomic and transcriptomics datasets together using SpaMTP.	Examples of additional SpaMTP visualisation and analysis functions that are useful for SM analysis pipelines.
GO	GO	GO

Installation

You can install the current version of SpaMTP from GitHub with:

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

devtools::install_github("GenomicsMachineLearning/SpaMTP")

Installing with conda environment

Below is an example of how to set up SpaMTP using a conda environment:

conda create -n SpaMTP -c conda-forge r-base=4.3.3 r-essentials r-devtools r-cairo python=3.9 r-reticulate r-leiden r-imager r-magick r-hdf5r r-sf bioconda::bioconductor-ebimage bioconda::bioconductor-scater bioconda::bioconductor-dropletutils bioconda::bioconductor-fgsea bioconda::bioconductor-rgoslin bioconda::bioconductor-glmgampoi
conda activate SpaMTP

Within the SpaMTP environment open R and install SpaMTP.

#Install Seurat version 5.1.0
remotes::install_github("satijalab/seurat", "seurat5", quiet = FALSE)

#Install Cardinal
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("Cardinal")

#Install SpaMTP
devtools::install_github("GenomicsMachineLearning/SpaMTP")

Possible Installiation Errors

Cairo Failed to Install

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’

Check these libraries exist:

conda install conda-forge::r-cairo

This should resolve any issues. Rerun:

BiocManager::install("scater")

Cardinal Failed to Install

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>
      |         ^~~~~~~~~

If EBImage failed to installed, it is most likely due to an issue with the installation of fftwtools.

Try:

conda config --add channels conda-forge
conda config --set channel_priority strict

conda install r-fftwtools

Else try:

conda install bioconda::r-fftwtools
conda install conda-forge::fftw

Try in R:


if (!require("BiocManager", quietly = TRUE)) #Check if BiocManager is installed
    install.packages("BiocManager")

BiocManager::install("fftwtools")

If none of the above methods resolve instiallation issues, install Cardinal directly through conda:

conda install bioconda::bioconductor-cardinal

rgoslin Failed to Install

If rgoslin fails to install please head to their github. Alternative, you can try installing through BiocManager or mamba shown below:

BiocManager::install("rgoslin")

mamba install bioconductor-rgoslin

Matrix version incompatible with Seurat

If the Matrix package version installed is not compatible with Seurat then try installing a different version and re-installing Seurat

#Install Correct version of Matrix package
remotes::install_version("Matrix", version = "1.6-4", repos = "https://cran.r-project.org")

#Install Seurat version 5.1.0
install.packages("Seruat")

For other issue please flag on github under Issues.