A New Statistical Method for Time Course Microarray Experiments

Monitoring the behavior of gene expression over time is important and will be a common experimental design in the future. We developed a general statistical significance method for detecting temporal differential expression.

Impact/Significance

• First rigorous statistical method for analysis of time course microarray data
• Enabling the identification of different temporal expression patterns in different groups

			Personnel SGTC Wenzhong Xiao Ron Davis U Washington John Storey and group Glue Grant Consortium

Accomplishments

We developed a general method for statistical inference of time course microarray data. This method is applicable to data of both independent and longitudinal samplings, and automatically handles missing data and difference of sampling time points.