LEA: Latent Eigenvalue Analysis in Application to High-Throughput Phenotypic Drug Screening

Abstract

Understanding the phenotypic characteristics of cells in culture and detecting perturbations introduced by drug stimulation is of great importance for biomedical research. However, a thorough and comprehensive analysis of phenotypic heterogeneity is challenged by the complex nature of cell-level data. Here, we propose a novel Latent Eigenvalue Analysis (LEA) framework and apply it to high-throughput phenotypic profiling with single-cell and single-organelle granularity. Using the publicly available SARS-CoV-2 datasets stained with the multiplexed fluorescent cell-painting protocol, we demonstrate the power of the LEA approach in the investigation of phenotypic changes induced by more than 1800 drug compounds. As a result, LEA achieves a robust quantification of phenotypic changes introduced by drug treatment. Moreover, this quantification can be biologically supported by simulating clearly observable phenotypic transitions in a broad spectrum of use cases. In conclusion, LEA represents a new and broadly applicable approach for quantitative and interpretable analysis in routine drug screening practice.