par2-circadian

par2-circadian fits second-order autoregressive AR(2) models to gene expression time series and extracts the eigenvalue modulus |lambda| as a scalar measure of temporal persistence to rank genes into a clock/target/background hierarchy.

Key Features:

• AR(2) Model Fitting: Fits second-order autoregressive models to gene expression time series data.
• Eigenvalue Modulus Extraction: Reports |lambda|, a single statistic quantifying temporal persistence: how strongly a gene's recent past constrains its next value.
• Gene Hierarchy Ranking: Ranks genes into a Clock > Target > Background hierarchy in circadian datasets.
• Per-Gene Statistics: Reports correlation length, half-life and root type (real or complex) per gene.

Scientific Applications:

• Circadian Gene Classification: Recovers the Clock > Target > Background hierarchy in circadian gene expression datasets.
• Temporal Persistence Quantification: Measures how strongly a gene's recent past constrains its next expression value.

Methodology:

Fits second-order autoregressive AR(2) models to gene expression time series and extracts the eigenvalue modulus of the characteristic polynomial.