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Multivariate Spatial LGCP Modeling using INLA-SPDE, with Application to Microbiome Image Data

Abstract Number:

3088 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yan Gong (1), Brent Coull (1), Jessica Mark Welch (2), Patrick La Riviere (3), Jacqueline Starr (4), Kyu Ha Lee (1)

Institutions:

(1) Harvard T.H. Chan School of Public Health, Boston, (2) The Forsyth Institute, Cambridge, (3) University of Chicago, Chicago, (4) Brigham and Women’s Hospital, Boston

Co-Author(s):

Brent Coull  
Harvard T.H. Chan School of Public Health
Jessica Mark Welch  
The Forsyth Institute
Patrick La Riviere  
University of Chicago
Jacqueline Starr  
Brigham and Women’s Hospital
Kyu Ha Lee  
Harvard T.H. Chan School of Public Health

First Author:

Yan Gong  
Harvard T.H. Chan School of Public Health

Presenting Author:

Yan Gong  
Harvard T.H. Chan School of Public Health

Abstract Text:

Human microbiome data exhibit complex spatial structures. Understanding the spatial dependence structures can often enhance inference about microbes' functions. In this work, we propose a novel parsimonious multivariate spatial log Gaussian Cox process (LGCP) model using the concept of the linear model of regionalization, which can explicitly capture within-species and cross-species dependence structures and interactions. The model is inherently latent Gaussian, thus we adopt the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE) method to efficiently speed up the computation using an approximate Bayesian approach. We apply the model to study human oral microbiome biofilm image data from samples of multiple patients obtained using spectral imaging fluorescence in situ hybridization (FISH), where the spatial information of how taxa's cells are located relative to each other and to host cells are preserved.

Keywords:

Multivariate Spatial LGCP|INLA-SPDE|Microbiome Image Data| | |

Sponsors:

Biometrics Section

Tracks:

Genomics, Metabolomics, Microbiome and NextGen Sequencing

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