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
