title: Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.
abstract: BACKGROUND
  Conventional differential gene expression analysis by methods such as student's t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.
  
  
  RESULTS
  Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method.
  
  
  CONCLUSIONS
  The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203, VA, USA.|Department of Radiation Oncology, Stanford University, Palo Alto, 94304, CA, USA.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, 20007, DC, USA.
authors.forename: Yiming
authors.initials: Y
authors.lastname: Zuo
authors.affiliation: Department of Radiation Oncology, Stanford University, Palo Alto, 94304, CA, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203, VA, USA.
authors.forename: Guoqiang
authors.initials: G
authors.lastname: Yu
authors.affiliation: Department of Radiation Oncology, Stanford University, Palo Alto, 94304, CA, USA.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
authors.affiliation: Lombardi Comprehensive Cancer Center, Georgetown University, Washington, 20007, DC, USA. hwr@georgetown.edu.
authors.forename: Habtom W
authors.identifier: http://orcid.org/0000-0001-9296-2132
authors.initials: HW
authors.lastname: Ressom
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000378:metabolism; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D012313:RNA / Q000737:chemistry / Q000302:isolation & purification / Q000378:metabolism; D012372:ROC Curve; D017423:Sequence Analysis, RNA
chemical_list: D015415:Biomarkers; D012313:RNA
keywords: Gaussian graphical model; Network-based differential gene expression analysis; Prior biological knowledge; Weighted graphical LASSO