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The reduction of gene expression variability from single cells to populations follows simple statistical laws

机译：从单细胞到群体的基因表达变异性的降低遵循简单的统计定律

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Recent studies on single cells and population transcriptomics have revealed striking differences in global gene expression distributions. Single cells display highly variable expressions between cells, while cell populations present deterministic global patterns. The mechanisms governing the reduction of transcriptome-wide variability over cell ensemble size, however, remain largely unknown. To investigate transcriptome-wide variability of single cells to different sizes of cell populations, we examined RNA-Seq datasets of 6 mammalian cell types. Our statistical analyses show, for each cell type, increasing cell ensemble size reduces scatter in transcriptome-wide expressions and noise (variance over square mean) values, with corresponding increases in Pearson and Spearman correlations. Next, accounting for technical variability by the removal of lowly expressed transcripts, we demonstrate that transcriptome-wide variability reduces, approximating the law of large numbers. Subsequent analyses reveal that the entire gene expressions of cell populations and only the highly expressed portion of single cells are Gaussian distributed, following the central limit theorem. (C) 2014 Elsevier Inc. All rights reserved.
机译：最近对单细胞和群体转录组学的研究表明，全球基因表达分布存在显着差异。单细胞在细胞之间表现出高度可变的表达，而细胞群体则呈现确定性的整体模式。然而，控制转录组范围内的变异性越过细胞整体大小减小的机制仍然很大程度上未知。为了调查单个细胞对不同大小的细胞群体的转录组范围内的变异性，我们检查了6种哺乳动物细胞类型的RNA-Seq数据集。我们的统计分析表明，对于每种细胞类型，增加的细胞集合大小可减少转录组范围内表达的分散和噪声（均方差），并相应增加Pearson和Spearman相关性。接下来，通过去除低表达的转录本来考虑技术变异性，我们证明转录组范围内的变异性降低，近似于大数定律。随后的分析表明，遵循中心极限定理，细胞群体的整个基因表达以及单个细胞的仅高度表达的部分呈高斯分布。 （C）2014 Elsevier Inc.保留所有权利。

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