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Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation

Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulat...

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Published in:Bioinformatics 2006-08, Vol.22 (16), p.2005-2011
Main Authors: Joung, Je-Gun, Shin, Dongho, Seong, Rho Hyun, Zhang, Byoung-Tak
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container_end_page 2011
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creator Joung, Je-Gun
Shin, Dongho
Seong, Rho Hyun
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description Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. Results: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2α and Ap2γ for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics. Contact:btzhang@bi.snu.ac.kr, rhseong@snu.ac.kr Supplementary Information: Supplementary data are available at .
doi_str_mv 10.1093/bioinformatics/btl343
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source Oxford Academic Journals (Open Access)
subjects Algorithms
Animals
Biological and medical sciences
Cell Differentiation
Cell Lineage
Cell Proliferation
Cluster Analysis
Computational Biology - methods
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling
General aspects
Genome
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Multigene Family
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated
Stem Cells - cytology
title Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation
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