Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis.

Abstract:

:In this article, we present new methods to analyze data from an experiment using rodent models to investigate the role of p27, an important cell-cycle mediator, in early colon carcinogenesis. The responses modeled here are essentially functions nested within a two-stage hierarchy. Standard functional data analysis literature focuses on a single stage of hierarchy and conditionally independent functions with near white noise. However, in our experiment, there is substantial biological motivation for the existence of spatial correlation among the functions, which arise from the locations of biological structures called colonic crypts: this possible functional correlation is a phenomenon we term crypt signaling. Thus, as a point of general methodology, we require an analysis that allows for functions to be correlated at the deepest level of the hierarchy. Our approach is fully Bayesian and uses Markov chain Monte Carlo methods for inference and estimation. Analysis of this data set gives new insights into the structure of p27 expression in early colon carcinogenesis and suggests the existence of significant crypt signaling. Our methodology uses regression splines, and because of the hierarchical nature of the data, dimension reduction of the covariance matrix of the spline coefficients is important: we suggest simple methods for overcoming this problem.

journal_name

Biometrics

journal_title

Biometrics

authors

Baladandayuthapani V,Mallick BK,Young Hong M,Lupton JR,Turner ND,Carroll RJ

doi

10.1111/j.1541-0420.2007.00846.x

subject

Has Abstract

pub_date

2008-03-01 00:00:00

pages

64-73

issue

1

eissn

0006-341X

issn

1541-0420

pii

BIOM846

journal_volume

64

pub_type

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