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Title: Autoregressive models for periodicity detection in DNA microarray time series data
Other Titles: Ji yu ji yin xin pian shi jian xu lie zhou qi xing jian ce de zi hui gui mo xing
Authors: Tang, Tsz Yan Vivian ( 鄧祉欣)
Department: Department of Electronic Engineering
Degree: Master of Philosophy
Issue Date: 2011
Publisher: City University of Hong Kong
Subjects: DNA microarrays.
Time-series analysis.
Notes: CityU Call Number: QP624.5.D726 T36 2011
viii, 58 leaves : ill. 30 cm.
Thesis (M.Phil.)--City University of Hong Kong, 2011.
Includes bibliographical references (leaves 54-58)
Type: thesis
Abstract: In a DNA microarray experiment, expression levels of thousands of genes are recorded simultaneously so that the functions of genes, the effects of certain therapies, disease, and developmental processes, among others, can be studied. With microarray technology, genome-wide gene expression data are being generated at a rapid rate. Biologists are interested in identifying the characteristics, trends, and patterns of gene expression profiles from a series of microarray experiments. However, each gene expression profile usually contains a certain amount of noise. It remains difficult to identify periodic gene expression profiles, especially when the number of data points is small and the level of noise is high. To increase accuracy when detecting periodic profiles, a noise filtering technique is needed before analysis of the gene expression data. We propose a new scheme combining singular value decomposition (SVD) with singular spectrum analysis (SSA). By considering the singular values of time series data, the trend component is extracted effectively so that noise can be filtered out. To detect the period of a time series, an autoregressive (AR) model-based estimation is used to assess the power spectrum density. Several datasets, including simulated sinusoidal signals and real DNA microarray time series experiments, are used to evaluate our scheme. The results show that our algorithm can reduce the noise level significantly and locate the correct period of time series signals.
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