Listen to Genes: Dealing with Microarray Data in the Frequency Domain
作者： Jianfeng Feng , Dongyun Yi , Ritesh Krishna , Shuixia Guo , Vicky Buchanan-Wollaston
DOI: 10.1371/JOURNAL.PONE.0005098
关键词: Network analysis 、 Normalization (statistics) 、 Artificial intelligence 、 DNA microarray 、 Cluster analysis 、 Granger causality 、 Gene regulatory network 、 Pattern recognition 、 Microarray analysis techniques 、 Genetics 、 Biology 、 Frequency domain 、 General Biochemistry, Genetics and Molecular Biology 、 General Agricultural and Biological Sciences 、 General Medicine
摘要: Background: We present a novel and systematic approach to analyze temporal microarray data. The includes normalization, clustering network analysis of genes. Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources variations. minimizes correlation among terms across replicates. The normalized gene expressions then clustered in their power spectrum density. complex Granger causality is introduced reveal interactions between sets genes. Complex Granger causality along with partial applied both time frequency domains selected as well all genes interesting networks interactions. successfully Arabidopsis leaf data generated from 31,000 genes observed over 22 points days. Three circuits: circadian circuit, ethylene circuit new global showing hierarchical structure determine initiators senescence analyzed detail. Conclusions: use totally data-driven form biological hypothesis. Clustering power-spectrum analysis helps us identify potential interest. Their dynamics can be captured accurately frequency domain methods causality. With rise availability microarray data, such useful tools uncovering hidden show our step by step manner help toy models real dataset. also analyse three distinct circuits of potential interest researchers.
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