rmRNAseq

Key Features:

• General Linear Model with Precision Weights: Models normalized log-transformed RNA-seq count data within a GLM framework incorporating precision weights to account for within-unit correlation.
• Continuous Autoregressive Structure: Applies a continuous autoregressive (CAR) correlation structure to model temporal dependencies in longitudinal or time-course experiments.
• Parametric Bootstrap Inference: Uses parametric bootstrap to generate empirical null distributions for robust differential expression testing and type I error control.

Scientific Applications:

• Longitudinal RNA-seq Analysis: Detects dynamic gene expression changes in repeated-measures and time-course studies by accounting for within-subject temporal correlation.

Methodology:

rmRNAseq normalizes RNA-seq counts via log transformation, fits a precision-weighted general linear model with a continuous autoregressive correlation structure to capture within-unit dependencies, and performs differential expression inference using parametric bootstrap resampling.