trendsceek

trendsceek identifies genes with statistically significant spatial expression trends by analyzing spatial gene expression patterns at single-cell resolution.

Key Features:

• Marked Point Processes: Employs a mathematical framework based on marked point processes to detect and quantify spatial expression patterns.
• Spatial Transcriptomics Analysis: Analyzes spatial transcriptomic data to identify genes exhibiting significant spatial trends.
• Sequential FISH Data Analysis: Processes sequential fluorescence in situ hybridization (FISH) datasets to characterize spatial gene distributions.
• scRNA-seq Low-Dimensional Projections: Processes low-dimensional projections derived from dissociated single-cell RNA sequencing (scRNA-seq) data to reveal expression gradients and hot spots.
• Statistical Significance Testing: Identifies genes with statistically significant spatial expression trends using statistical tests on spatial patterns.

Scientific Applications:

• Gene Expression Gradients: Maps gene expression gradients across tissues to inform studies of developmental processes and tissue organization.
• Hot Spot Detection: Detects hot spots—regions of intense gene activity—within tissue contexts.
• Integration Across Data Types: Integrates spatial transcriptomics, sequential FISH, and projected scRNA-seq data for comprehensive spatial expression analysis.

Methodology:

Uses a marked point process framework to detect and quantify spatial expression patterns, applies statistical testing to identify genes with significant spatial trends, and processes low-dimensional projections from scRNA-seq as well as spatial transcriptomics and sequential FISH datasets.