Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

• DOI: 10.1242/dev.202832
• Publication Date: 2024-05-01T14:56:16Z
• Authors: Arthur Radley; Stefan Boeing; Austin Smith
• Keywords: Inner cell mass

Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after subsetting to highly variable genes (HVGs). Here, we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous Sort Feature Weighting (cESFW) outperforms HVG selection in distinguishing cell-state-specific genes. We apply cESFW six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, generates a high-resolution embedding displaying coherent developmental progression from eight-cell post-implantation stages and delineating 15 distinct states. The highlights sequential lineage decisions during blastocyst development, while unsupervised clustering identifies branch point populations obscured previous analyses. first branching region, where morula cells become specified inner mass trophectoderm, includes previously asserted lack trajectory. quantify relatedness different pluripotent stem cultures types identify marker naïve primed pluripotency. Finally, by revealing with dynamic lineage-specific expression, provide markers staging blastocyst.