Reduction of Gene Expression Variability from Single Cells to Populations follows Simple Statistical Laws

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, researchers from Keio University examined RNA-Seq datasets of 6 mammalian cell types. Their statistical analyses showed for each cell type, increasing cell ensemble size reduced scatter in transcriptome-wide expressions and noise (variance over square mean) values, resulting in increased Pearson and Spearman correlations. Next, accounting for technical variability by removing lowly expressed transcripts, they show transcriptome-wide variability reduces, approximating the law of large numbers. Subsequent analyses reveal the entire gene expressions of cell populations and only the highly expressed portion of single cells are Gaussian distributed, following the central limit theorem.

Expression scatter reduces as cell population size increase.