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Yuan S and Li KC  (2007) Context-dependent clustering for dynamic cellular state modeling of microarray gene expression. Bioinformatics 23(22):3039-47

Abstract: MOTIVATION: High-throughput expression profiling allows researchers to study gene activities globally. Genes with similar expression profiles are likely to encode proteins that may participate in a common structural complex, metabolic pathway, or biological process. Many clustering, classification and dimension reduction approaches, powerful in elucidating the expression data, are based on this rationale. However, the converse of this common perception can be misleading. In fact, many biologically related genes turn out uncorrelated in expression RESULTS: In this paper, we present a novel method for investigating gene co-expression patterns. We assume the correlation between functionally related genes can be strengthened or weakened according to changes in some relevant, yet unknown, cellular states. We develop a context-dependent clustering (CDC) method to model the cellular state variable. We apply it to the transcription regulatory study for Saccharomyces cerevisiae, using the Stanford cell-cycle gene expression data. We investigate the co-expression patterns between transcription factors (TFs) and their target genes (TGs) predicted by the genome-wide location analysis of Harbison et al. (2004). Since TF regulates the expression of its TGs, correlation between TFs and TGs expression profiles can be expected. But as many authors have observed, the expression of transcription factors do not correlate well with the expression of their target genes. Instead of attributing the main reason to the lack of correlation between the transcript abundance and TF activity, we search for cellular conditions that would facilitate the TF-TG correlation. The results for sulfur amino acid pathway regulation by MET4, respiratory genes regulation by HAP4, and mitotic cell cycle regulation by ACE2/SWI5 are discussed in detail. Our method suggests a new way to understand the complex biological system from microarray data. AVAILABILITY: The program is written in ANSI C. The source code could be downloaded from http://kiefer.stat.sinica.edu.tw/CDC/index.php CONTACT: kcli@stat.ucla.edu.

Status: Published Type: Journal Article PubMed ID: 17846037

Topics addressed in this paper

Number of different genes curated to this paper: 53

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Topics Topics not linked to Genes Genes linked to topics (#1 - 10 )
ACE2 ADD37 AIM44 AMN1 ATP1 ATP15 ATP17 ATP20 ATP7 COR1
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Topics Genes linked to topics (#11 - 20 )
COX12 COX13 COX5A COX6 COX8 COX9 CTS1 CYS3 DSE1 DSE2
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Topics Genes linked to topics (#21 - 30 )
DSE3 EIS1 GGC1 HAP4 ICP55 IES5 MET1 MET10 MET14 MET17
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Topics Genes linked to topics (#31 - 40 )
MET2 MET28 MET4 MET5 MET6 MHT1 MMP1 MUP1 NDI1 NIS1
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Topics Genes linked to topics (#41 - 50 )
PIR1 QCR7 RIP1 SAM1 SAM2 SCW11 SDH1 SEO1 STR3 SWI5
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Topics Genes linked to topics (#51 - 53 )
UPC2 YLR179C YNL046W
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