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

TL;DR

This paper introduces a skew-t-normal multi-level reduced-rank functional PCA model for analyzing replicated omics time series data, capturing correlations among variables and improving interpretability and estimation accuracy.

Contribution

It proposes a novel joint modeling approach for replicated time series data that accounts for correlations among variables, enhancing biological interpretability and reducing estimation error.

Findings

Model captures correlations among variables effectively.

Low-dimensional representations are biologically interpretable.

Simulation shows reduced estimation error compared to independent models.

Abstract

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 the variables are known to be highly correlated. In this article we present a skew-t-normal multi-level reduced-rank functional principal components analysis (FPCA) model for simultaneously modelling the between-variable and between-replicate variance. The reduced-rank FPCA model is computationally efficient and, analogously with a standard PCA for vectorial data, provides a low dimensional representation that can be used to identify the major patterns of temporal variation. Using an example case study exploring the genetic response to BCG infection we demonstrate that these low dimensional representations are eminently biologically interpretable. We also show using a simulation study that modelling all variables simultaneously greatly reduces the estimation error compared to the independence assumption.