HTA

HTA quantifies heterogeneity in spatially resolved molecular and imaging data to assess tumor and tissue heterogeneity for applications in spatial transcriptomics, digital pathology, and medical imaging.

Key Features:

• Statistical Robustness: Produces an approximately normal distribution of index values, facilitating statistical assessment and inference.
• Multivariate Capability: Handles the multivariate data typical of spatial transcriptomics.
• Versatility Across Domains: Applies to spatial transcriptomics, digital pathology (including H&E staining), Geographic Information Systems (GIS), and medical imaging such as brain MRI.
• Multiresolution Immune Detection: Captures immune-cell infiltration at multiple spatial resolutions.
• Validation by Simulation: Validated through simulations demonstrating accuracy in reflecting heterogeneity levels.

Scientific Applications:

• Cancer Research: Analyzes spatial transcriptomics datasets to quantify tumor heterogeneity in 2D and 3D data.
• Digital Pathology: Supports survival analysis by linking heterogeneity levels in H&E and other pathology data with patient outcomes.
• Medical Imaging: Distinguishes normal aging, Alzheimer's disease, and tumors in brain MRI data.
• Geographic Information Systems: Characterizes spatial heterogeneity in GIS datasets.

Methodology:

Computes an index from spatially resolved data (e.g., 10x Genomics spatial RNA-seq and H&E digital pathology), yields approximately normally distributed values enabling statistical inference, has been validated via simulations, and aligns with known molecular traits while capturing immune-cell infiltration at multiple resolutions.