Introduction to Spatial omic analyses

Stefano Mangiola, South Australian immunoGENomics Cancer Institute1, Walter and Eliza Hall Institute2

Luciano Martellotto, Adelaide Centre for Epigenetics, South Australian immunoGENomics Cancer Institute3

Source: vignettes/Introduction.Rmd

Instructors

Dr. Stefano Mangiola is leading the Computational Cancer immunology group at the South Australian immunoGENomics Cancer Institute (SAiGENCI). He uses single-cell and spatial technologies to investigate the tumor microenvironment and the immune system. Beyong data production, his focus in on the integration and modelling of large-scale single-cell data resources. He is the author of tidytranscriptiomics and co-leads the tidyomics endevour.

Dr. Luciano Martelotto is a key figure in the field of spatial omics technology. He demonstrated his extensive expertise and significant contributions to the fields of single cell and spatial omics technology. Currently, he heads the Martelotto Lab located at the Adelaide Centre for Epigenetics and the South Australian immunoGENomics Cancer Institute (SAiGENCI). His lab is dedicated to the development and evaluation of new tools and methodologies for single cell and spatial omics.

Workshop partner: Physalia 

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Workshop goals and objectives

What you will learn

  • The basics of spatial profiling technologies
  • Analysis and manipulation of sequencing-based spatial data.
  • The basics of tidy R analyses of biological data with tidyomics
  • How to interface SpatialExperiment with tidy R manipulation and visualisation
  • Analysis and manipulation of imaging-based spatial data.

Getting started

Local

You can view the material at the workshop webpage

here.

Workshop package installation

If you want to install the packages and material post-workshop, the instructions are below. The workshop is designed for R 4.4 and Bioconductor 3.19.

# Install workshop package
#install.packages('BiocManager')
BiocManager::install("tidyomics/tidySpatialWorkshop", dependencies = TRUE)
    
# Then build the vignettes
BiocManager::install("tidyomics/tidySpatialWorkshop", build_vignettes = TRUE, force=TRUE)

# To view vignette
library(tidySpatialWorkshop)
vignette("Introduction")

Interactive execution of the vignettes

From command line, and enter the tidySpatialWorkshop directory.

# Open the command line
git clone git@github.com:tidyomics/tidySpatialWorkshop.git

Alternatively download the git zipped package. Uncompress it. And enter the directory.

Announcements

Tidyomics is now published in (Nature Methods)[https://www.nature.com/articles/s41592-024-02299-2]. And availabel for (free) here[https://www.biorxiv.org/content/10.1101/2023.09.10.557072v3].

Introduction to Spatial Omics

Objective

Provide a foundational understanding of spatial omics, covering different technologies and the distinctions between imaging and sequencing in experimental and analytical contexts.

Workshop Structure

Day 1
1. Welcome and Introduction
  • Introduction of the instructor
  • Introduction of the crowd
  • Overview and goals of the workshop.
2. What is Spatial Omics?
  • Definition and significance in modern biology.
  • Key applications and impact.
  • Overview of different spatial omics technologies.
  • Comparison of imaging-based vs sequencing-based approaches.
3. Sequencing Spatial Omics
  • Detailed comparison of methodologies.
  • Experimental design considerations.
  • Data analysis challenges and solutions.
5. Analysis of sequencing based spatial data
  • Getting Started with SpatialExperiment.
  • Data Visualisation and Manipulation.
  • Quality control and filtering.
  • Dimensionality reduction.
  • Spatial Clustering.
  • Deconvolution of pixel-based spatial data.
Day 2
1. Introduction to tidyomics
  • Use tidyverse on spatial, single-cell, pseudobulk and bulk genomic data
2. Working with tidySpatialExperiment
  • tidySpatialExperiment package
  • Tidyverse commands
  • Advanced filtering/gating and pseudobulk
  • Work with features
  • Summarisation/aggregation
  • tidyfying your workflow
  • Visualisation
Day 3
1. Imaging Spatial Omics
  • Detailed comparison of methodologies.
  • Experimental design considerations.
  • Data analysis challenges and solutions.
2. Spatial analyses of imaging data
  • Working with imaging-based data in Bioconductor with MoleculeExperiment
  • Aggregation and analysis
  • Clustering
  • Neighborhood analyses

  1. ↩︎

  2. <mangiola.s at wehi.edu.au>↩︎

  3. ↩︎