TTCA: an R package for the identification of differentially expressed genes in time course microarray data

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics

Background:

• Microarray time series analysis offers insights into cellular responses to stimulation.
• Gene expression dynamics can be rapid and transient or slow and gradual.
• Existing methods struggle with heterogeneous, sparse, or irregularly sampled data.

Purpose of the Study:

• To develop a robust method for analyzing heterogeneous and sparse time-course gene expression data.
• To improve the detection sensitivity and transparency of significant expression dynamics.
• To provide a tool for understanding cellular responses to perturbations.

Main Methods:

• A novel method combining multiple scores to capture diverse expression dynamics (fast, transient, slow).
• Designed to perform well with low replicate numbers and irregular sampling times.
• Implemented as the R package TTCA (transcript time course analysis).

Main Results:

• The method successfully analyzes perturbation responses, identifying both fast and slow gene expression changes.
• Results are presented transparently with links to figures for easy interpretation.
• Demonstrated effectiveness on microarray data from lung cancer cells stimulated with EGF.
• An extension allows for the analysis of functional gene groups, providing an overview of cellular responses.

Conclusions:

• A new, efficient R package (TTCA) for analyzing sparse and heterogeneous time-course data is presented.
• The package offers high detection sensitivity and transparency.
• TTCA is available on CRAN, with source code provided.