IN-PERSON - High dimensional analysis -Thinking beyond tradition

Single-cell RNA-sequencing is able to identify the gene expression heterogeneity within complex biological systems, though interpretation is challenging due to a mix of biological and technical factors. Previous studies have demonstrated the utility of reduced dimensional representations to identify shared cellular attributes and unique biological processes across single-cell datasets.However, in many cases, the inferred dimensions from standard matrix factorization methods may not align with biologically meaningful gene expression programs, and nonlinear methods often lack interpretability. In this session, we’ll discuss factor-analysis-based approaches for identifying and interpreting the biological and technical sources of variation in single-cell transcriptomics maps. We’ll demonstrate the application of an effective factor analysis model, Varimax PCA, on a novel single-cell map of the healthy rat liver, providing a good example of the utility of factor decomposition for single-cell genomics data analysis and interpretation.