The reduction of gene expression variability from single cells to populations follows simple statistical laws
The reduction of gene expression variability from single cells to populations follows simple statistical laws
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DOI：
10.1016/j.ygeno.2014.12.007
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发表时间：
2015-03-01
期刊：
影响因子：
4.4
通讯作者：
Selvarajoo, Kumar
中科院分区：
文献类型：
--
作者：
Piras, Vincent;Selvarajoo, Kumar
最近对单细胞和群体转录组学的研究揭示了全球基因表达分布的显著差异。单个细胞在细胞之间显示高度可变的表达，而细胞群体呈现确定性的全局模式。然而，在细胞系综大小上的全转录组变异性降低的机制在很大程度上仍然未知。为了研究单个细胞对不同大小的细胞群体的转录组范围的变异性，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，增加细胞系综大小减少了转录组范围内表达和噪声（方差对平方均值）值的分散，Pearson和斯皮尔曼相关性相应增加。接下来，通过去除低表达的转录本来解释技术变异性，我们证明了转录组范围的变异性降低，近似于大数定律。随后的分析表明，细胞群体的整个基因表达和只有单细胞的高表达部分是高斯分布的，遵循中心极限定理。(C)2014 Elsevier Inc. All rights reserved.
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.