Trendy

Trendy identifies and characterizes dynamic changes in gene expression from high-throughput ordered-condition experiments such as time-course and spatial-course studies.

Key Features:

• Segmented Regression Models: Employs segmented (breakpoint) regression models to estimate breakpoints that indicate changes in gene expression levels.
• Gene-Specific Expression Patterns: Selects an optimal segmented regression model for each gene to locate breakpoint positions and determine the direction of expression changes.
• Global Dynamic Activity Summarization: Summarizes dynamic activity across the transcriptome to highlight critical time points or spatial locations with coordinated expression changes.
• Data Type Support: Applies to high-throughput expression data including microarray and RNA-seq datasets.
• Ordered-Condition Analysis: Targets experiments with ordered conditions (e.g., time-course, spatial-course) to detect temporal or spatial expression dynamics.

Scientific Applications:

• Differentiation and developmental studies: Identifies timing and sequence of gene expression changes during differentiation processes.
• Spatial patterning analysis: Detects location-specific expression dynamics underlying spatial patterning.
• Identification of critical time points and pathways: Highlights critical time points and coordinated transcriptome changes relevant to biological pathways and cellular behaviors.

Methodology:

Uses segmented (breakpoint) regression models to estimate breakpoints, selects optimal segmented regression models per gene, and summarizes global dynamic activity across the transcriptome.