Tutorial for Spatial Transcriptomics Data Analysis
Organizers:
Shinn-Ying Ho, National Yang Ming Chiao Tung University, Taiwan
Tzong-Yi Lee, National Yang Ming Chiao Tung University, Taiwan
Speakers:
Tzong-Yi Lee, National Yang Ming Chiao Tung University, Taiwan
Chia-Ru Chung, National Central University, Taiwan
Yun Tang, National Yang Ming Chiao Tung University
Yuxuan Pang, The University of Tokyo
Audience: Researchers and students interested in Gene Expression Data Analysis and Spatial Transcriptomics
Tutorial Abstract:
In the rapidly evolving field of high-throughput sequencing, single-cell and spatial transcriptomics have emerged as a transformative technology, offering unprecedented insights into the spatial organization of gene expression within tissues and organisms. This workshop aims to equip participants with the knowledge and tools necessary to harness the power of spatial transcriptomics data and extract meaningful biological insights. The tutorial will be held in interactive manner, after the introduction of principles, methodologies, and challenges associated with spatial transcriptomics data analysis. Through a combination of lectures, hands-on exercises, and interactive discussions, participants will learn how to preprocess spatial transcriptomics datasets, perform quality control assessments, detect spatially co-expressed genes, and visualize spatial gene expression patterns. By leveraging platforms such as iDEP, Cytoscape, Seurat, STForte, and Squidpy, this tutorial equips researchers with a comprehensive skill set to interpret and visualize spatial gene expression patterns for biological discovery.
Outline and Duration:
From understanding the fundamentals of gene expression profiling to exploring advanced computational techniques, participants will gain a comprehensive understanding of the analytical pipeline required to analyze transcriptomic data and interpret gene regulations spatially. Additionally, we will explore cutting-edge computational approaches for spatial data integration, trajectory inference, and spatial cell type mapping, providing participants with a toolkit to address diverse biological questions across a range of spatial scales and experimental designs. Whether you are a biologist seeking to leverage spatial transcriptomics to uncover the spatial dynamics of gene regulation or a computational scientist interested in developing novel analytical methods for spatial data analysis, this workshop offers a valuable opportunity to enhance your skills and expand your research horizons. Join us as we embark on a journey through the transcriptomic landscape, where spatially resolved insights await discovery.
In this tutorial on spatial transcriptomics, we will initially focus on conventional transcriptomic analysis using iDEP and Cytoscape, covering GEO dataset retrieval, preprocessing, quality control, and differential expression analysis. Participants will learn how to link enriched pathways to interaction networks and apply basic styling for effective visualization. Next, the workshop will transition to the effective processing, analysis, and visualization of spatial transcriptomics data using Seurat. Core topics include data import, spatial-specific quality control, normalization, clustering, and the identification of spatially variable features. Visual outputs will highlight the spatial structure of tissue sections, enabling biologically meaningful interpretations. Seurat is a comprehensive R package, extended to support spatial data with functionalities beyond basic single-cell analysis, including preprocessing, quality control, normalization, and identification of spatially relevant clusters and markers.
The final section will introduce Python-based workflows with STForte and Squidpy. Participants will explore spatial region detection, neighborhood analysis, and spatial cell-cell communication using COMMOT. We will also cover multi-slice data integration with PASTE2 and STForte to reconstruct multi-dimensional spatial context. Both STForte and Squidpy employ robust statistical and machine learning methods such as PCA, t-SNE, and UMAP, which are critical for uncovering complex data structures and understanding the spatial context of gene expression. This tutorial aims to provide participants with a comprehensive framework for conducting spatial transcriptomics research. The topics covered in this tutorial are provided as follows:
- High-throughput Sequencing Technology and Spatial Transcriptomics (20 – 30 mins)
- High-throughput Sequencing Technology
- Gene Expression Data Analysis
- Introduction of Spatial Transcriptomics (ST)
- Tools and Databases for ST data analysis
- Transcriptomic Data Analysis using iDEP and Cytoscape Toolkits (20 – 30 mins)
- Data Download from GEO and Overview of Input Requirements
- Data Preprocessing and Quality Control
- Differential Expression Analysis and Functional Enrichment
- Integrative Visualization and Connecting to Cytoscape
- Cytoscape Data Loading and Basic Network Styling
- ST Data Analysis Pipeline in Seurat (R language) (30 mins)
- Data Import & Setup: Prepare spatial data for analysis.
- Quality Control: Remove low-quality spots for accuracy.
- Normalization: Correct technical variation across spots.
- Clustering & Visualization: Group regions based on gene expression.
- Identify Spatial Features: Detect spatially variable genes.
- ST Data Analysis in STForte and Squidpy (Python) (20 mins)
- Spatial region identification and spatial enhancement using STForte
- Spatial neighborhood analysis using Squidpy
- Spatial cell-cell communication (CCI) using COMMOT
- Integrating multi-slices spatial transcriptomics using PASTE2 + STForte