1 Getting started

1.1 Summary

This workshop will cover Spatial Transcriptomics Analysis

Conducted by:
- Dr. Shani Amarasinghe (Jarde lab, Monash BDI & Monash Genomics and Bioinformatics Platform)
- Dr. Adrew Pattinson (Jarde lab)
- Dr. Sean Byars (Monash Genomics and Bioinformatics Platform)
- Dr. Eamon Coughlan

In short: Spatial transcriptomics helps connect molecular function with physical structure, enabling a deeper understanding of biological systems. These insights are not easily captured by bulk or single-cell RNA-seq alone. Spatial methods provide the missing layer: where things are happening.

Goal: Equip you with the foundational knowledge and hands-on skills to start working with spatial transcriptomics data across multiple platforms.

1.2 Prerequisites and dependencies

Programs, R and Python packages

1.2.1 Data

Follow this code to obtain all data we will work with

Data sources: Xenium Data

Explorers: Xenium Explorer Visium Explorer

1.3 Download or link data

Within Bio1 Server

cd path/where/you/want/data
ln -s /home/lper0012/tasks/training/SpatialSelfTrainingData_Jul2025/data

from URL

curl -O https://cf.10xgenomics.com/samples/xenium/2.0.0/Xenium_V1_Human_Colon_Cancer_P2_CRC_Add_on_FFPE/Xenium_V1_Human_Colon_Cancer_P2_CRC_Add_on_FFPE_outs.zip
tar -xvf cell_feature_matrix.tar.gz

Within R



## Download and untar the data
options(timeout=3600)
download.file(
    "https://bioinformatics.erc.monash.edu/home/lper0012/SpatialSelfTrainingData_Jul2025/data.tar","data.tar")

untar("data.tar")

1.3.1 Packages required in R

# Install BiocManager if not already installed
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# CRAN packages
install.packages(c(
  "Seurat",
  "patchwork",
  "SeuratObject",
  "SeuratData",
  "tidyverse",        # Includes dplyr, ggplot2, tibble, tidyr, stringr
  "qs",
  "RColorBrewer",
  "FNN",
  "future",
  "ggpubr",
  "DT",
  "dbscan",
  "Matrix"
))

# Bioconductor packages
BiocManager::install(c(
  "AUCell",
  "scider",
  "SpatialExperiment",
  "Glimma",
  "SingleR",
  "limma",
  "edgeR",
  "SingleCellExperiment"
))

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

devtools::install_github("immunogenomics/harmony")
devtools::install_github("satijalab/seurat-wrappers")  # optional extras for Seurat
devtools::install_github("YosefLab/spacexr")           # for spacexr

# Optional: install archived GSA
install.packages("https://cran.r-project.org/src/contrib/Archive/GSA/GSA_1.03.tar.gz", 
                 repos = NULL, type = "source")

1.3.2 Packages required in Python

Create the following conda spatial_training_conda_env

wget https://bioinformatics.erc.monash.edu/home/lper0012/SpatialSelfTrainingData_Jul2025/python/spatial_training_conda_env.yaml

2 Introduction