Statistical methods in graphs with applications to life sciences

• Grant number: 16/13422-9
• Support Opportunities: Regular Research Grants
• Start date: November 01, 2016
• End date: October 31, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Principal Investigator: André Fujita
• Grantee: André Fujita
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Alexandre Galvão Patriota ; Edgard Morya ; João Ricardo Sato ; Koichi Sameshima

Abstract

Recently it has been demonstrated that the structure of functional brain and gene regulatory networks are more adequate to characterize biological organisms states than the individual analysis of their parts. Besides, it is known that such networks are not exactly equal (isomorphic) even by comparing individuals belonging to the same group due to intrinsic variability. Thus, approaches based on traditional Computer Science that search for isomorphism become unfruitful in the analysis of those networks. To overcome this problem, it is crucial to develop statistical methods for graphs. To construct a bridge between Graph Theory and Statistics, we investigated the spectral properties of random graphs. It is known that several structural properties of a random graph, such as the number of walks, diameter, and cliques, can be described by the spectrum of its adjacency matrix. In Takahashi et al. (2012), we analyzed the graph spectrum and introduced statistical methods in graphs for (i) model selection, (ii) parameter estimation, and (iii) a hypothesis test to discriminate two samples of functional brain networks. Here, we will develop methods to generalize the hypothesis test of Takahashi et al. to compare more than two groups, to identify correlation and information flow between graphs and finally to integrate and analyze both genetic and neuroimaging data. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

JARDIM, VINICIUS CARVALHO; SANTOS, SUZANA DE SIQUEIRA; FUJITA, ANDRE; BUCKERIDGE, MARCOS SILVEIRA. BioNetStat: A Tool for Biological Networks Differential Analysis. FRONTIERS IN GENETICS, v. 10, JUN 21 2019. (14/50884-5, 18/21934-5, 16/13422-9, 15/21162-4, 08/57908-6)

VIDAL, MACIEL C.; SATO, JOAO R.; BALARDIN, JOANA B.; TAKAHASHI, DANIEL Y.; FUJITA, ANDRE. ANOCVA in R: A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder. FRONTIERS IN NEUROSCIENCE, v. 11, JAN 24 2017. (16/13422-9, 13/03447-6, 15/01587-0, 13/10498-6, 14/09576-5)

GUZMAN, GROVER E. C.; SATO, JOAO R.; VIDAL, MACIEL C.; FUJITA, ANDRE. Identification of alterations associated with age in the clustering structure of functional bra in networks. PLoS One, v. 13, n. 5, MAY 24 2018. (16/13422-9, 15/01587-0)

FERNANDO ANDRADE; ASUKA NAKATA; NORIKO GOTOH; ANDRÉ FUJITA. Large miRNA survival analysis reveals a prognostic four-biomarker signature for triple negative breast cancer. GENETICS AND MOLECULAR BIOLOGY, v. 43, n. 1, 2020-03-02. (18/21934-5, 15/01587-0, 16/13422-9)

RAMOS, TAIANE COELHO; BALARDIN, JOANA BISOL; SATO, JOAO RICARDO; FUJITA, ANDRE. Abnormal Cortico-Cerebellar Functional Connectivity in Autism Spectrum Disorder. FRONTIERS IN SYSTEMS NEUROSCIENCE, v. 12, JAN 15 2019. (16/13422-9, 18/17996-5, 13/07375-0, 15/01587-0)

FUJITA, ANDRE; TAKAHASHI, DANIEL YASUMASA; BALARDIN, JOANA BISOL; VIDAL, MACIEL CALEBE; SATO, JOAO RICARDO. Correlation between graphs with an application to brain network analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, v. 109, p. 76-92, MAY 2017. (13/10498-6, 16/13422-9, 13/00506-1, 15/01587-0)