📚 Syllabus & Course Curriculum
Bioinformatics & Computational Biology
Module-by-module breakdown of Spatial Transcriptomics: Mapping Gene Expression in 3D Tissue Space, from foundations to a certified capstone project.
Platforms
• Sequencing-based Visium and imaging-based Xenium, MERFISH and CosMx
• The resolution against gene-panel-breadth trade-off that defines the choice
• Spot-level capture is not single cell — the point most analyses get wrong
Tissue
• FFPE and fresh-frozen workflows and the RNA integrity each demands
• Sectioning, orientation and permeabilisation optimisation
• Quality metrics that determine whether a section is worth sequencing at all
Processing
• Space Ranger output and building objects in Seurat, Squidpy or Scanpy
• Normalisation for spatial data and the confound of tissue density
• Registering expression to histology and reading the two together
Analysis
• Deconvolution with cell2location or RCTD against a single-cell reference
• Spatially variable gene detection and spatial domain identification
• Neighbourhood and ligand-receptor analysis, and the co-location fallacy
Biology
• Tumour microenvironment structure and immune exclusion patterns
• Serial section alignment for pseudo-3D reconstruction and its distortions
• Validation with immunofluorescence or in situ hybridisation before claiming a finding
Earn government-registered certification in Spatial Transcriptomics: Mapping Gene Expression in 3D Tissue Space
e-Certificate and e-Marksheet issued on successful completion.
