• Corpus ID: 88512061

A Skew-t-Normal Multi-Level Reduced-Rank Functional PCA Model with Applications to Replicated `Omics Time Series Data Sets

@article{Berk2011ASM,
  title={A Skew-t-Normal Multi-Level Reduced-Rank Functional PCA Model with Applications to Replicated `Omics Time Series Data Sets},
  author={Maurice Berk and G. Montana},
  journal={arXiv: Methodology},
  year={2011}
}
A powerful study design in the fields of genomics and metabolomics is the 'replicated time course experiment' where individual time series are observed for a sample of biological units, such as human patients, termed replicates. Standard practice for analysing these data sets is to fit each variable (e.g. gene transcript) independently with a functional mixed-effects model to account for between-replicate variance. However, such an independence assumption is biologically implausible given that… 

Figures from this paper

Intakes of whey protein hydrolysate and whole whey proteins are discriminated by LC–MS metabolomics
TLDR
High elevated plasma levels of a number of cyclic dipeptides and other AA metabolites were found following intake of the WH meal and these metabolites are primary candidates to explain the superior insulinotropic effect of WH.

References

SHOWING 1-10 OF 26 REFERENCES
Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data
TLDR
This work proposes a general framework of functional mixed effects model for within-unit and within-subunit variations are modeled through two separate sets of principal components; the subunit level functions are allowed to be correlated.
On Gene Ranking Using Replicated Microarray Time Course Data
TLDR
A multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression is derived, from both longitudinal and cross‐sectional replicated developmental microarray time course data.
Longitudinal Analysis of Gene Expression Profiles Using Functional Mixed-Effects Models
TLDR
A functional mixed-effects model is proposed for estimating the temporal pattern of each gene, which is assumed to be a smooth function, and a statistical test based on the distance between the fitted curves is carried out to detect differential expression.
Identifying temporally differentially expressed genes through functional principal components analysis.
TLDR
Functional principal components analysis is proposed to test hypotheses in the change of the mean curves and outperforms the recently developed extraction of differential gene expression and a 2-way mixed effects ANOVA under reasonable gene expression models in simulation.
Significance analysis of time course microarray experiments.
TLDR
A significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes and shows that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay.
Robust linear mixed models using the skew t distribution with application to schizophrenia data
TLDR
An efficient alternating expectation-conditional maximization (AECM) algorithm for the computation of maximum likelihood estimates of parameters on the basis of two convenient hierarchical formulations is presented.
Joint modelling of paired sparse functional data using principal components.
TLDR
A modelling framework to study the relationship between two paired longitudinally observed variables using penalized splines to model the mean curves and the principal component curves and cast the proposed model into a mixed-effects model framework for model fitting, prediction and inference.
Functional Data Analysis for Sparse Longitudinal Data
We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the
Principal component models for sparse functional data
SUMMARY The elements of a multivariate data set are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has
...
1
2
3
...