TrendCatcher

TrendCatcher identifies dynamic differentially expressed genes (DDEGs) in RNA-seq longitudinal studies using an R package implementation to characterize temporal gene expression changes during biological processes such as disease progression.

Key Features:

• Dynamic differential expression detection: Detects dynamic differentially expressed genes (DDEGs) in time-course RNA-seq data.
• Model integration: Integrates a smoothing spline ANOVA model with a break point searching strategy to capture temporal expression changes.
• Temporal pattern characterization: Captures distinct temporal patterns that differentiate adaptive versus maladaptive responses and subtle temporal shifts.
• Visualization of temporal signatures: Identifies and visualizes dynamic transcriptional gene signatures and biological processes over time.
• Data type support: Applies to bulk RNA-seq and single-cell RNA-seq (scRNA-seq) time course datasets.
• Biomarker and target identification: Enables identification of early biomarkers and potential pathogenic therapeutic targets from longitudinal transcriptomes.

Scientific Applications:

• COVID-19 peripheral blood time-course analysis: Applied to bulk RNA-seq and scRNA-seq peripheral blood transcriptomes to identify early and persistent activation of neutrophils and coagulation pathways and impaired type I interferon (IFN-I) signaling in patients progressing to severe COVID-19 compared with vaccinated individuals or mild cases.
• Disease progression mapping: Characterizes temporal gene expression changes during disease progression to identify early indicators and underlying biological mechanisms.
• Biomarker and therapeutic discovery: Supports discovery of temporal biomarkers and potential pathogenic therapeutic targets from longitudinal datasets.

Methodology:

TrendCatcher integrates a smoothing spline ANOVA model with a break point searching strategy.