STModule

STModule identifies tissue modules from spatially resolved transcriptomics data using a Bayesian method that reveals spatial components and characteristics of tissues.

Key Features:

• Bayesian tissue module identification: STModule identifies tissue modules from spatially resolved transcriptomics using a Bayesian statistical framework.
• Diverse expression signal detection: STModule uncovers diverse expression signals in transcriptomic landscapes including cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features, and various cell types.
• Robust and transferable modules: Tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies.
• Broad biological signal capture: STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components.

Scientific Applications:

• Tumor microenvironment analysis: STModule provides insights into tumor microenvironments through spatial characterization of transcriptomic landscapes.
• Disease mechanism investigation: STModule facilitates understanding of disease mechanisms via identification of spatially resolved tissue modules.
• Treatment development: STModule supports treatment development by revealing spatial components and characteristics relevant to therapeutic targeting.
• Histological organization study: STModule enables analysis of histological organization of tissues through spatial module identification.
• Downstream analysis facilitation: STModule facilitates downstream analysis of spatially resolved transcriptomics data through modular decomposition.

Methodology:

STModule uses a Bayesian method to identify tissue modules from spatially resolved transcriptomics data.