End-to-End Spatial Transcriptomics Data Analysis Workshop FHCC2026

Introduction

This hands-on workshop provides a structured, end-to-end workflow for spatial transcriptomics data analysis. Participants will learn how to process raw spatial datasets, perform quality control, visualize spatial gene expression patterns, integrate single-cell references, and conduct automated cell-type annotation and deconvolution.

Using real-world human tissue datasets, the workshop bridges computational methodology with biological interpretation, with applications in cancer and multi-tissue systems. The sessions will include guided demonstrations in R using Seurat-based workflows and reference-guided annotation approaches.

Designed for researchers seeking practical skills in spatial omics analysis, this workshop emphasizes reproducibility, interpretability, and translational relevance.

Learning Outcomes

By the end of this workshop, participants will be able to:

• Understand the core principles and workflow of spatial transcriptomics analysis, including Visium and Xenium platforms.
• Perform basic preprocessing and quality control of spatial transcriptomics datasets.
• Conduct normalization, dimensionality reduction, clustering, and spatial visualization.
• Integrate spatial data with single-cell RNA sequencing references for cell-type annotation.
• Apply reference-guided annotation and introductory spatial deconvolution approaches.
• Interpret spatial gene expression patterns in healthy and disease contexts, including cancer.

Target Audience

This workshop is designed for:

• Biologists and biomedical scientists.
• Medical professionals and clinician-researchers.
• Bioinformaticians and data scientists.
• Biotechnology and life sciences students.
• Graduate students and postdoctoral researchers working with omics data.