SpaGFT : Graph Fourier transform for spatial omics representation and analyses of complex organs

Main

• About
  • SpaGFT’s functions
  • SpaGFT’s advantages
• System Requirements
  • Hardware Requirements
  • OS requirements
  • Python Dependencies
• Installation
  • Create a virtual environment
  • Install SpaGFT
    • Approach 1: Install from PyPI
    • Approach 2: Install from source
  • Install jupyter (optional)
• Data preparation
  • Spatial transcriptomics data
• References
• Release
• Contact
• SpaGFT Tutorial：Visium Human Lymph Node
  • Outline
  • 1. Import packages
  • 2. Load data
  • 3. QC and preprocessing
  • 4. Function: identify spatially variable genes
    • 4.1 Determine the Fourier modes and identify SVGs
    • 4.2 Visualize the identified SVGs
    • 4.3 Plot frequency signals
    • 4.4 UMAP visualization of gene frequency signals
  • 5 Function: gene expression enhancement
  • 6. Function: identify functional tissue units
    • 6.1 Group SVGs based on frequency signals
    • 6.2 Visualize clustering results
    • 6.3 Extract functional tissue unit information
    • 6.4 Visualize interested FTUs
    • 6.5 Enrichment analysis of biological process
• SpaGFT Tutorial：Visium Mouse Brain Coronal
  • Outline
  • 1. Import packages
  • 2. Load data
  • 3. QC and preprocessing
  • 4. Function: identify spatially variable genes
    • 4.1 Determine the Fourier modes and identify SVGs
    • 4.2 Visualize the identified SVGs
    • 4.3 Plot frequency signals
    • 4.4 UMAP visualization of gene frequency signals
  • 5 Function: gene expression enhancement
  • 6. Function: identify functional tissue unit
    • 6.1 Group SVGs based on frequency signals
    • 6.2 Visualize clustering results
    • 6.3 Extract functional tissue unit information
    • 6.4 Visualize interested FTUs
    • 6.5 Enrichment analysis of biological process
• SpaGFT Tutorial: Codex A6
  • Outline
  • 1. Import packages
  • 2. Load data
  • 3. QC
  • 4. Function: identify spatially variable genes
    • 4.1 Determine the Fourier modes and identify SVGs
    • 4.2 Visualize the identified SVPs
    • 4.3 Plot frequency signals
  • 5. Function: identify functional tissue units
    • 5.1 Group SVGs based on frequency signals
    • 5.2 Visualize clustering results
    • 5.3 Extract functional tissue units information
    • 5.4 Visualize interested FTUs
• Machine Learning Model Improvement
  • Outline
  • 1. SpaGCN Improvement
  • 2. TACCO Improvement
  • 3. Tangram Improvement
  • 4. CAMPA Improvement
• Spg script
  • Usage
  • Example 1 - mouse_brain_coronal
  • Example 2 - human_lymph_node
  • Example 3 - codex_lymph_node_A6
  • Example 4 - e95_e1s1_mosta
  • Example 5 - dorsal_midbrain_cell