Knowledge-guided multi-scale independent component analysis for biomarker identification.
BACKGROUND
Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.


RESULTS
Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.


CONCLUSIONS
We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.
Chen
Li
L
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu
Xuan
Jianhua
J
Wang
Chen
C
Shih
Ie-Ming
IeM
Wang
Yue
Y
Zhang
Zhen
Z
Hoffman
Eric
E
Clarke
Robert
R
D015415:Biomarkers / Q000032:analysis*; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D003628:Database Management Systems; D030541:Databases, Genetic; D005260:Female; D015870:Gene Expression / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D051188:Knowledge Bases; D015201:Meta-Analysis as Topic; D016571:Neural Networks, Computer; D009687:Nuclear Proteins / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010051:Ovarian Neoplasms / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis / Q000379:methods*; D012045:Regulatory Sequences, Nucleic Acid; D012441:Saccharomyces cerevisiae / Q000502:physiology; D017422:Sequence Analysis, DNA / Q000379:methods; D049490:Systems Biology / Q000379:methods; D015534:Trans-Activators / Q000235:genetics
D015415:Biomarkers; D009687:Nuclear Proteins; C452851:RSF1 protein, human; D015534:Trans-Activators