Spatial Transcriptomics: Methods and Applications in R

Dates

• From Tuesday 6/10/2026 to Thursday 8/10/2026

Speakers

Aurélien Barré, Benjamin Dartigues

Training link

Objectives

• Analyze and manipulate spatially resolved transcriptomics datasets (sequencing and imaging data)
• Apply methodological concepts specific to spatial data
  (spatial statistics, niche enrichment analysis)
• Integrate spatial approaches with single-cell RNA-seq analyses for cell annotation and identification

Program

This training course will introduce the essential tools for spatial transcriptomic analysis in R, with a focus on the Seurat package, which enables the manipulation, analysis, and visualization of spatial data. Several other R packages specialized in spatial data analysis will also be presented.

• Introduction to the concepts of spatially resolved RNA sequencing (sequencing-based and image-based)
• Importing spatial data into R
• Data quality control and preprocessing
• Spatial visualization of gene expression
• Data normalization
• Marker identification
• Data deconvolution for cell identification

Prerequisite

Proficiency in the R language
Must have completed the “R Language: Introduction” course or have equivalent knowledge

Public

Researchers, engineers, and technicians in biology and bioinformatics
Professionals interested in analyzing spatial transcriptomics data or replicating methodologies described in scientific publications