apeglm

apeglm is a statistical method used for shrinkage estimation of dispersion parameters in the context of generalized linear models, particularly in high-dimensional data analysis. It is an extension of the popular method 'ashr' (Adaptive Shrinkage of Empirical Bayes) and is designed to handle situations where the dispersion parameter is not constant across different units or groups.

The core idea behind apeglm is to borrow information across different units to obtain more stable and reliable estimates of the dispersion parameter. This is achieved through an empirical Bayes approach, where a prior distribution is estimated from the data, and then used to shrink individual estimates towards a common mean. The "adaptive" nature of apeglm allows the prior to be flexible and data-driven, leading to improved performance in various scenarios.

apeglm finds applications in several fields, including genomics, where it is used for estimating dispersion in RNA sequencing count data. In this context, accurate dispersion estimation is crucial for identifying differentially expressed genes. The method aims to address the inherent variability and the often small number of replicates in such experiments.

The algorithm typically involves an iterative process to estimate the prior distribution and then compute the posterior means for the dispersion parameters. By shrinking noisy estimates, apeglm can lead to more robust conclusions and better statistical power in hypothesis testing. It is implemented in statistical software packages, making it accessible to researchers for their analyses.