Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo

• DOI: 10.1016/j.stemcr.2022.09.007
• Publication Date: 2022-10-13T16:36:03Z
• Authors: Arthur Radley; Elena Corujo-Simon; Jennifer Nichols; Austin Smith; Sara-Jane Dunn
• Keywords: Cell Sorting

A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves an unsupervised manner by quantifying if observed correlations between features are more likely have occurred due random chance versus dependent relationship, without the need for any user-defined significance threshold. On synthetic data, demonstrate removal noisy signals reveal higher resolution patterns than commonly used feature selection methods. We then apply human pre-implantation embryo RNA sequencing (scRNA-seq) data. Previous studies failed unambiguously identify early inner mass (ICM), suggesting may diverge mouse paradigm. In contrast, resolves ICM and reveals sequential lineage bifurcations as classical model. thus provides powerful approach maximizing information extraction high-dimensional datasets such scRNA-seq