Factorized discriminant analysis for genetic signatures of neuronal phenotypes.

ABSTRACT: Navigating the complex landscape of single-cell transcriptomic data presents significant challenges. Central to this challenge is the identification of a meaningful representation of high-dimensional gene expression patterns that sheds light on the structural and functional properties of cell types. Pursuing model interpretability and computational simplicity, we often look for a linear transformation of the original data that aligns with key phenotypic features of cells. In response to this need, we introduce factorized linear discriminant analysis (FLDA), a novel method for linear dimensionality reduction. The crux of FLDA lies in identifying a linear function of gene expression levels that is highly correlated with one phenotypic feature while minimizing the influence of others. To augm

SUBMITTER: Qiao M 

PROVIDER: S-EPMC10752939 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature