Analysis of Mouse Periodic Gene Expression Data Based on Singular Value Decomposition and Autoregressive Modeling

Abstract

Each DNA microarray experiment generates a large amount of gene expression profiles and it remains a challenge for biologists to robustly identify periodic gene expression profiles with certain noise level in the data. In this paper, we propose a new scheme with noise filtering technique to analyze the periodicity of gene expression base on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectrum estimation. With the combination of these methods, noise can be filtered out and over 85% of periodic gene expression can be identified in mouse presomitic mesoderm transcriptome data set.