TY - GEN
T1 - Reconstruction of transcription regulatory networks by stability-based network component analysis
AU - Chen, Xi
AU - Wang, Chen
AU - Shajahan, Ayesha N.
AU - Riggins, Rebecca B.
AU - Clarke, Robert
AU - Xuan, Jianhua
PY - 2012
Y1 - 2012
N2 - Reliable inference of transcription regulatory networks is still a challenging task in the field of computational biology. Network component analysis (NCA) has become a powerful scheme to uncover the networks behind complex biological processes, especially when gene expression data is integrated with binding motif information. However, the performance of NCA is impaired by the high rate of false connections in binding motif information and the high level of noise in gene expression data. Moreover, in real applications such as cancer research, the performance of NCA in simultaneously analyzing multiple candidate transcription factors (TFs) is further limited by the small sample number of gene expression data. In this paper, we propose a novel scheme, stability-based NCA, to overcome the above-mentioned problems by addressing the inconsistency between gene expression data and motif binding information (i.e., prior network knowledge). This method introduces small perturbations on prior network knowledge and utilizes the variation of estimated TF activities to reflect the stability of TF activities. Such a scheme is less limited by the sample size and especially capable to identify condition-specific TFs and their target genes. Experiment results on both simulation data and real breast cancer data demonstrate the efficiency and robustness of the proposed method.
AB - Reliable inference of transcription regulatory networks is still a challenging task in the field of computational biology. Network component analysis (NCA) has become a powerful scheme to uncover the networks behind complex biological processes, especially when gene expression data is integrated with binding motif information. However, the performance of NCA is impaired by the high rate of false connections in binding motif information and the high level of noise in gene expression data. Moreover, in real applications such as cancer research, the performance of NCA in simultaneously analyzing multiple candidate transcription factors (TFs) is further limited by the small sample number of gene expression data. In this paper, we propose a novel scheme, stability-based NCA, to overcome the above-mentioned problems by addressing the inconsistency between gene expression data and motif binding information (i.e., prior network knowledge). This method introduces small perturbations on prior network knowledge and utilizes the variation of estimated TF activities to reflect the stability of TF activities. Such a scheme is less limited by the sample size and especially capable to identify condition-specific TFs and their target genes. Experiment results on both simulation data and real breast cancer data demonstrate the efficiency and robustness of the proposed method.
KW - network component analysis
KW - stability analysis
KW - target genes identification
KW - transcription factor activity
KW - transcription regulatory network
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U2 - 10.1007/978-3-642-30191-9_4
DO - 10.1007/978-3-642-30191-9_4
M3 - Conference contribution
AN - SCOPUS:84861169891
SN - 9783642301902
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 47
BT - Bioinformatics Research and Applications - 8th International Symposium, ISBRA 2012, Proceedings
T2 - 8th International Symposium on Bioinformatics Research and Applications, ISBRA 2012
Y2 - 21 May 2012 through 23 May 2012
ER -