A New Statistical Method for Analyzing Time Course Multifactor Expression Data

Time-course microarray experiment is capable of capturing the dynamic profile of genomic response to treatment factors. The profile contains valuable information for researchers to identify possible genetic factors that lead to different clinical outcomes, which can help directing future investigation. We developed a general statistical method to extract gene-specific temporal patterns to the interaction of multiple treatment factors.


  • First rigorous statistical method for analyzing time course multifactor data
  • Enabling the identification of both response types to multifactor treatment and response pattern during time course


We developed a statistical inference method for time course multifactor analysis. This method is applicable to both longitudinal and cross-sectional microarray data. It can handle both balanced and unbalanced experimental design, a frequent situation in observational studies.


Baiyu Zhou
Weihong Xu
Wenzhong Xiao
Ronald W. Davis
Wing Wong

Glue Grant Consortium