SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways

• Published in: PLoS computational biology 2019-10, Vol.15 (10), p.e1007357-e1007357
• Main Authors: Salviato, Elisa, Djordjilović, Vera, Chiogna, Monica, Romualdi, Chiara
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Androgens; Biochemistry; Bioinformatics; Biology and Life Sciences; Brain cancer; Case studies; Computational Biology - methods; Computer simulation; Covariance; Data interpretation; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks - genetics; Genes; Genomics; Humans; Hypothesis testing; Identification methods; Leukemia; Likelihood ratio; Medical research; Medicine and Health Sciences; Methods; Models, Theoretical; Mutation; Normal Distribution; Perturbation; Physical Sciences; Prostate cancer; Research and Analysis Methods; Sensitivity and Specificity; Software; Supervision; Transcriptome - genetics; Tumors; Visualization (Computer)
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007357

Topological gene-set analysis has emerged as a powerful means for omic data interpretation. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. Here, we propose a new method, called SourceSet, able to distinguish between the primary and the secondary dysregulation within a Gaussian graphical model context. The proposed method compares gene expression profiles in the control and in the perturbed condition and detects the differences in both the mean and the covariance parameters with a series of likelihood ratio tests. The resulting evidence is used to infer the primary and the secondary set, i.e. the genes responsible for the primary dysregulation, and the genes affected by the perturbation through network propagation. The proposed method demonstrates high specificity and sensitivity in different simulated scenarios and on several real biological case studies. In order to fit into the more traditional pathway analysis framework, SourceSet R package also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.