  Generalized principal component analysis (GLM-PCA) facilitates dimension
reduction of non-normally distributed data. We provide a detailed derivation of
GLM-PCA with a focus on optimization. We also demonstrate how to incorporate
covariates, and suggest post-processing transformations to improve
interpretability of latent factors.
