Sparse decomposition of gene expression data to infer transcriptional modules guided by motif information

Ting Gong, Jianhua Xuan, Li Chen, Rebecca B. Riggins, Yue Wang, Eric P. Hoffman, Robert Clarke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings
Pages244-255
Number of pages12
DOIs
StatePublished - Aug 27 2008
Event4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008 - Atlanta, GA, United States
Duration: May 6 2008May 9 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4983 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008
CountryUnited States
CityAtlanta, GA
Period5/6/085/9/08

Keywords

  • Estrogen receptor binding
  • Gene regulatory networks
  • Motif analysis
  • Sparse component analysis
  • Transcriptional modules

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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