Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions.

Autor: Chatterjee S; Department of Epidemiology and Biostatistics, Indiana University, School of Public Health, Bloomington, Indiana, USA., Chowdhury S; Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, USA., Ryu D; Department of Statistics and Actuarial Science, Northern Illinois University, Illinois, USA., Basu S; Division of Epidemiology and Biostatistics, University of Illinois Chicago, Illinois, USA.
Jazyk: angličtina
Zdroj: Biometrics [Biometrics] 2023 Dec; Vol. 79 (4), pp. 3294-3306. Date of Electronic Publication: 2023 Jul 21.
DOI: 10.1111/biom.13902
Abstrakt: We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.
(© 2023 The International Biometric Society.)
Databáze: MEDLINE
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