SRT-Server

SRT-Server performs integrated analyses of spatially resolved transcriptomics (SRT) data to detect spatially variable genes, deconvolve mixed expression into cell types, identify spatial domains, infer pseudo-time trajectories, analyze cell–cell communication, and perform differential expression and gene set enrichment within tissue spatial context.

Key Features:

• Quality Control (QC): Performs data integrity and reliability checks on SRT datasets.
• Spatially Variable Gene Detection (SVG): Identifies genes with spatially patterned expression across tissue coordinates.
• Deconvolution (DECON and DECON_PY): Separates mixed gene expression signals into constituent cell types or states using DECON and DECON_PY modules.
• Cell Typing (CT): Classifies spots or cells by their gene expression profiles.
• Spatial Domain Detection (SDD): Detects spatial domains or regions within tissue based on expression and spatial information.
• Differential Gene Expression Analysis (DEG): Compares gene expression between conditions or spatial regions.
• Gene Set Enrichment Analysis (GSEA): Assesses enrichment of predefined gene sets in selected comparisons or domains.
• Cell-Cell Communication Identification (CCC): Infers potential interactions between cell types based on molecular profiles.
• Pseudo-Time Trajectory Inference (TRAJ): Infers developmental or temporal trajectories within the tissue.
• Automated Execution and Figure Generation: Executes analytic pipelines automatically and generates figures and result outputs.
• Case Study Demonstrations: Applied to SRT data from two common platforms to demonstrate applicability across datasets.

Scientific Applications:

• Tissue architecture analysis: Elucidates spatial organization of cell types and domains within tissues.
• Cellular interaction mapping: Reveals potential cell–cell communication and neighborhood relationships.
• Developmental trajectory analysis: Reconstructs pseudo-time dynamics and developmental processes in spatial context.
• Disease pathology and therapeutic research: Supports comparison of diseased versus normal regions and investigation of therapeutic targets.

Methodology:

Automated execution of analytic pipelines that perform QC, SVG detection, deconvolution (DECON, DECON_PY), cell typing (CT), spatial domain detection (SDD), differential expression (DEG), gene set enrichment (GSEA), cell-cell communication (CCC), and pseudo-time trajectory inference (TRAJ), producing figures and result outputs.