Giotto

Giotto enables integrative analysis and visualization of spatial transcriptomic and proteomic expression data to characterize tissue composition, spatial expression patterns, and cellular interactions.

Key Features:

• Integrative analysis: Performs integrative analysis of spatial transcriptomic and proteomic datasets and incorporates single-cell RNA-seq (scRNAseq) data for spatial enrichment.
• Spatial cell-type enrichment: Implements spatial cell-type enrichment analysis by integrating scRNAseq to enhance resolution of cellular heterogeneity within native spatial contexts.
• Analysis algorithms: Provides an end-to-end analysis module that implements a diverse array of algorithms tailored to characterize complex biological spatial data.
• Visualization of spatial and imaging features: Produces visualizations of spatial expression patterns and associated imaging features for interpretation of spatial dynamics.
• spatialDWLS integration: Integrates the spatialDWLS method for quantitative estimation of cell-type composition at each spatial location and deconvolution.
• Cross-technology support: Applied to datasets derived from diverse technologies and platforms, including analyses on a human developmental heart dataset.
• Benchmarking: Includes benchmarking evidence that spatialDWLS outperforms existing deconvolution methods in accuracy and speed.

Scientific Applications:

• Tissue composition analysis: Characterizing tissue composition and cellular interactions from spatial expression data.
• Resolving cellular heterogeneity: Resolving adjacent cell types and cellular heterogeneity in their native spatial contexts via scRNAseq integration.
• Quantitative cell-type deconvolution: Estimating cell-type composition at single spatial locations using spatialDWLS.
• Developmental dynamics: Investigating spatial-temporal changes in cell-type composition during development, exemplified by a human developmental heart dataset.
• Cross-platform analyses: Comparing and integrating results across spatial transcriptomic and proteomic technologies.

Methodology:

Integration of scRNAseq for spatial cell-type enrichment and application of spatialDWLS for quantitative estimation of cell-type composition; an analysis module implements a diverse array of algorithms for spatial data characterization.