TY - GEN
T1 - Sparse decomposition of gene expression data to infer transcriptional modules guided by motif information
AU - Gong, Ting
AU - Xuan, Jianhua
AU - Chen, Li
AU - Riggins, Rebecca B.
AU - Wang, Yue
AU - Hoffman, Eric P.
AU - Clarke, Robert
PY - 2008
Y1 - 2008
N2 - An important topic in computational biology is to identify transcriptional modules through sequence analysis and gene expression profiling. A transcriptional module is formed by a group of genes under control of one or several transcription factors (TFs) that bind to cis-regulatory elements in the promoter regions of those genes. In this paper, we develop an integrative approach, namely motif-guided sparse decomposition (mSD), to uncover transcriptional modules by combining motif information and gene expression data. The method exploits the interplay of co-expression and co-regulation to find regulated gene patterns guided by TF binding information. Specifically, a motif-guided clustering method is first developed to estimate transcription factor binding activities (TFBAs); sparse component analysis is then followed to further identify TFs' target genes. The experimental results show that the mSD approach can successfully help uncover condition-specific transcriptional modules that may have important implications in endocrine therapy of breast cancer.
AB - An important topic in computational biology is to identify transcriptional modules through sequence analysis and gene expression profiling. A transcriptional module is formed by a group of genes under control of one or several transcription factors (TFs) that bind to cis-regulatory elements in the promoter regions of those genes. In this paper, we develop an integrative approach, namely motif-guided sparse decomposition (mSD), to uncover transcriptional modules by combining motif information and gene expression data. The method exploits the interplay of co-expression and co-regulation to find regulated gene patterns guided by TF binding information. Specifically, a motif-guided clustering method is first developed to estimate transcription factor binding activities (TFBAs); sparse component analysis is then followed to further identify TFs' target genes. The experimental results show that the mSD approach can successfully help uncover condition-specific transcriptional modules that may have important implications in endocrine therapy of breast cancer.
KW - Estrogen receptor binding
KW - Gene regulatory networks
KW - Motif analysis
KW - Sparse component analysis
KW - Transcriptional modules
UR - http://www.scopus.com/inward/record.url?scp=49949101318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49949101318&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-79450-9_23
DO - 10.1007/978-3-540-79450-9_23
M3 - Conference contribution
AN - SCOPUS:49949101318
SN - 3540794492
SN - 9783540794493
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 244
EP - 255
BT - Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings
T2 - 4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008
Y2 - 6 May 2008 through 9 May 2008
ER -