Autoregressive-model-based missing value estimation for DNA microarray time series data

Authors:
Miew Keen Choong;Maurice Charbit;Hong Yan
Affiliations:
School of Electrical and Information Engineering, University of Sydney, Sydney, N.S.W., Australia;Department of Signal and Image Processing, Ecole Nationale Supérieure des Télécommunications, Paris, France;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong and School of Electrical and Information Engineering, University of Sydney, Sydney, N.S.W., Australia
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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Abstract

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimationmethod (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.