Marina Vannucci's professional page

Marina Vannucci
Noah Harding Professor and Chair of Statistics, Rice University

This website contains links to codes developed by Dr. Vannucci and her students and collaborators, together with some of the datasets used in their publications.

GitHub Repositories

NPBayes-fMRI Bayesian Non-Parametric Spatio-Temporal Models for fMRI Data
Reference paper: Kook et al. (2018). NPBayes-fMRI: Nonparametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data. Statistics in Biosciences, in press.

dmbvs Dirichlet Multinomial Bayesian Variable Selection
Reference paper: Wadsworth et al. (2017). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome Data. BMC Bioinformatics 18:94, DOI 10.1186/s12859-017-1516-0.

SSBVS - Stochastic Search Bayesian Variable Selection

Matlab code for Multivariate Linear Regression Models.
Reference paper: Brown, P.J. et al. (2002). Bayes model averaging with selection of regressors. JRSS, Series B, 64(3), 519-536.

Matlab code for Multinomial Probit Regression Models.
Reference paper: Sha, N. et al. (2004). Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60(3), 812-819.

Matlab code for Accelerated Failure Time Models.
Reference paper: Sha, N. et al. (2006). Bayesian variable selection for the analysis of microarray data with censored outcome. Bioinformatics, 22(18), 2262-2268.

Datasets:

NIR Spectral Data on Biscuit Doughs

ppls).

NIR Spectral Data on Sugar Concentrations

R Packages in CRAN

iBATCGH Integrative Bayesian Analysis of Transcriptomic and CGH Data
Reference paper: Cassese, A. et al. (2014). A Hierarchical Bayesian Model for Inference on Copy Number Variants and their Association to Gene Expression. Annals of Applied Stats, 8(1), 148-175.
Other reference: Cassese, A. et al.(2016). iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH data. In Statistical Analysis for High-Dimensional Data - The Abel Symposium 2014, Frigessi, A. et al.(Eds). Springer Verlag, 105-123 (R/C++ code also available)

KSCONS for Predicting Protein Residue Contact (shinyapp also available)
Reference paper: Li et al. (2016). A Bayesian Approach for Protein Residue Contact Prediction using the Knob-Socket Model of Protein Tertiary Structure. Bioinformatics, 32(24): 3774-3781.

SCRSELECT Bayesian Variable Selection for Semi-Competing Risks
Reference paper: Chapple, A. et al. (2017). Bayesian variable selection for a semi-competing risks model with multiple components. Computational Statistics and Data Analysis, 112, 170-185.

Other Code

Matlab code
Reference paper: Stingo et al. (2011). Incorporating Biological Information into Linear Models: A Bayesian Approach to the Selection of Pathways and Genes. Annals of Applied Stats, 5(3), 1978-2002.

Matlab code
Reference paper: Stingo et al.(2013). An Integrative Bayesian Modeling Approach to Imaging Genetics. Journal of the American Statistical Association, 108, 876-891.

Dataset: Processed fMRI and SNP data on 210 subjects from a study on schizophrenia provided by the MCIC consortium (Gollub et al., Neuroinformatics, 11(3), 367-388, 2013).

Matlab code
Reference paper: Peterson et al.(2016). Bayesian Inference of Multiple Gaussian Graphical Models. Journal of the American Statistical Association, 110, 159-174.

Protein Structure Prediction

Web app for Bayesian Modeling of Protein Primary Sequence for Secondary Structure Prediction (paper).

Software for A Dirichlet process mixture of hidden Markov models for protein structure prediction (paper).