title: Graphical modeling for gene set analysis: A critical appraisal.
abstract: Current demand for understanding the behavior of groups of related genes, combined with the greater availability of data, has led to an increased focus on statistical methods in gene set analysis. In this paper, we aim to perform a critical appraisal of the methodology based on graphical models developed in Massa et al. (2010) that uses pathway signaling networks as a starting point to develop statistically sound procedures for gene set analysis. We pay attention to the potential of the methodology with respect to the organizational aspects of dealing with such complex but highly informative starting structures, that is pathways. We focus on three themes: the translation of a biological pathway into a graph suitable for modeling, the role of shrinkage when more genes than samples are obtained, the evaluation of respondence of the statistical models to the biological expectations. To study the impact of shrinkage, two simulation studies will be run. To evaluate the biological expectation we will use data from a network with known behavior that offer the possibility of carrying out a realistic check of respondence of the model to changes in the experimental conditions.
authors.affiliation: Department of Statistical Sciences, University of Padua, via Cesare Battisti 241, 35121 Padova, Italy.
authors.forename: Vera
authors.initials: V
authors.lastname: Djordjilović
authors.affiliation: Department of Statistical Sciences, University of Padua, via Cesare Battisti 241, 35121 Padova, Italy.
authors.forename: Monica
authors.initials: M
authors.lastname: Chiogna
authors.affiliation: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, United Kingdom.
authors.forename: M Sofia
authors.initials: MS
authors.lastname: Massa
authors.affiliation: Department of Biology, University of Padua, Via Ugo Bassi 58/B, 35121 Padova, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Romualdi
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003196:Computer Graphics; D006801:Humans; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000235:genetics / Q000473:pathology; D008297:Male; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000235:genetics / Q000473:pathology; D015398:Signal Transduction; D059467:Transcriptome
keywords: Gene set analysis; Graphical modeling; Pathways