Entropy sorting of single cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo
Entropy sorting of single cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo
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单细胞RNA测序数据的熵排序揭示了人类植入前胚胎的内细胞团
DOI：
10.1101/2022.04.08.487653
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Radley A
中科院分区：
文献类型：
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作者：
Radley A
单细胞基因表达分析的一个主要挑战是从技术或生物噪音中辨别有意义的细胞异质性。为了应对这一挑战，我们提出了熵排序（ES），这是一种区分指示细胞身份的基因的数学框架。 ES 以无监督的方式实现这一点，通过量化观察到的特征之间的相关性是否更有可能由于随机机会与依赖关系而发生，而不需要任何用户定义的显着性阈值。在合成数据上，我们证明了噪声信号的去除可以揭示比常用特征选择方法更高分辨率的基因表达模式。然后，我们将 ES 应用于人类植入前胚胎单细胞 RNA 测序 (scRNA-seq) 数据。先前的研究未能明确识别早期内细胞团（ICM），这表明人类胚胎可能与小鼠胚胎模式不同。相比之下，ES 解决了 ICM 并揭示了经典模型中的连续谱系分叉。因此，ES 提供了一种强大的方法，可以最大限度地从高维数据集（例如 scRNA-seq 数据）中提取信息。
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 this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data.