Factorized Discriminant Analysis for Genetic Signatures of Neuronal Phenotypes
This work addresses the problem of interpretable dimensionality reduction for single-cell transcriptomics in neuroscience, offering a method to link gene expression to neuronal phenotypes, but it is incremental as it builds on existing linear discriminant analysis with sparsity regularization.
The authors tackled the challenge of identifying meaningful representations in single-cell transcriptomic data by introducing factorized linear discriminant analysis (FLDA), a method that identifies linear functions of gene expression correlated with specific phenotypic features while minimizing others, and demonstrated its effectiveness on Drosophila optic lobe neuron datasets by capturing structural patterns and uncovering key genes.
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 augment this method, we integrate it with a sparsity-based regularization algorithm. This integration is crucial as it selects a subset of genes pivotal to a specific phenotypic feature or a combination thereof. To illustrate the effectiveness of FLDA, we apply it to transcriptomic datasets from neurons in the Drosophila optic lobe. We demonstrate that FLDA not only captures the inherent structural patterns aligned with phenotypic features but also uncovers key genes associated with each phenotype.