Supporting data for "Evaluating stably expressed genes in single cells"

Single cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level are intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. <br>Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the 'Mouse Atlas' dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. <br>SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, 'scSEGIndex', is incorporated into the scMerge Bioconductor R package https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html and can be used for identifying genes with stable expression in scRNA-seq datasets.

单细胞RNA测序（single cell RNA-seq, scRNA-seq）分析已揭示转录过程存在显著异质性，提示单细胞水平下多数基因的表达本质上具有随机性与噪声特性。然而在细胞群体层面，传统上被称为持家基因（housekeeping genes, HKGs）的一类基因子集，在不同细胞与组织类型中均呈现稳定表达。因此，一个关键问题亟待解答：能否在单细胞水平鉴定稳定表达基因（stably expressed genes, SEGs）？若可以，又当如何评估其表达稳定性？此前我们曾提出一种计算框架，用于对单细胞基因的表达稳定性进行排序，以服务于scRNA-seq数据的标准化与整合。本研究针对该框架衍生出的稳定表达基因开展了细致的评估与特征表征。

在此项工作中，我们证实，基于早期人类与小鼠发育scRNA-seq数据集以及‘小鼠图谱’（Mouse Atlas）数据集所得到的基因表达稳定性指数，在跨物种间具备可重复性与保守性。我们证明，依据稳定性指数从单细胞中鉴定出的稳定表达基因，其稳定性显著优于此前基于细胞群体定义的持家基因，且该结论在多种不同生物系统中均成立。分析结果表明，稳定表达基因在单细胞水平上本就具备更高的稳定性，其特征与持家基因高度相似，提示它们在维持单个细胞的基础生命功能中可能发挥关键作用。

本研究鉴定出的稳定表达基因，在解析单细胞转录组的异质性与稳定性方面，以及在scRNA-seq数据标准化等实际应用场景中，均具备直接的应用价值。我们用于计算基因稳定性指数的工具‘scSEGIndex’，已集成至scMerge Bioconductor R包（https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html）中，可用于在scRNA-seq数据集中鉴定稳定表达的基因。