An integrative clustering and modeling algorithm for dynamical gene expression data


Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge.


Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response.


Availability: The code to our method is freely available