Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling

The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostatic dynamics of cell cycle systems to the response of key genes to the onset of cancer or infectious disease. However, microarray data frequently contain a significant number of missing values making the application of common multivariate analysis methods, all of which require complete expression matrices, difficult. In order to preserve the experimentally expensive non-missing data points in time series gene expression data, methods are needed to estimate the missing values in such a way that preserves the latent interdependencies among time points within individual expression profiles. Thus we propose modeling gene expression profiles as simple linear and Gaussian dynamical systems and apply the Kalman filter to estimate missing values. While other current advanced estimation methods are either sensitiv...

• Type: Conference
• Year: 2008
• Where: AINA
• Authors: Connie Phong, Raul Singh
• Keywords: AINA 2008, Computer Networks, Gene Expression, Missing Values, Time Series