Functional Data Analysis Pipeline, Extracting Functional Traits from Biological Time-Series Data

Provides a pipeline of tools for analysing circadian time-series data using functional data analysis (FDA). The package supports smoothing of rhythmic time series, functional principle component analysis (FPCA), and extraction of group-level traits from functional representations. Analyses can incorporate multiple curve derivatives and optional temporal segmentation, enabling comparative analysis of circadian dynamics across experimental groups and time windows.

TimeTraits

TimeTraits provides a set of tools for analysing biological time-series data using functional data analysis (FDA). The package is designed to support end-to-end workflows, from smoothing rhythmic time series to extracting group-level traits from functional principal component analysis (FPCA).

The methods implemented here are particularly suited to circadian and other biological time-series data where interest lies in comparing functional patterns across experimental groups, time windows, or curve derivatives.

Features

• Smoothing of biological time-series data using functional data representations
• Functional principal component analysis (FPCA) of smoothed curves
• Extraction of group-level FPCA-derived traits
• Support for multiple curve derivatives (e.g. 0th, 1st, 2nd)
• Optional temporal segmentation (e.g. pre/post environmental shifts)
• Shape-based outlier detection (where applicable)

Installation

You can install the development version of TimeTraits directly from GitHub:

# install.packages("devtools")
devtools::install_github("scllock/TimeTraits")