Evaluating stably expressed genes in single cells

Abstract

Background Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is 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. Results 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. Conclusions 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.

pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools

“…We next investigated the impact of different normalization strategies. Besides the standard log-normalization included in Seurat, we tested scran's pooling-based normalization [33], sctransform's variance-stabilizing transformation [34], normalization based on stable genes [35,36], and SCnorm [37] (scVI-based normalization [38] was included separately, as it was not meant for this purpose and follows a slightly different flow). In addition to log-normalization, the standard Seurat clustering pipeline performs per-feature unitvariance scaling so that the PCA is not too strongly dominated by highly expressed features.…”

Cell Surface Protein mRNAs Show Differential Transcription in Pyramidal and Fast-Spiking Cells as Revealed by Single-Cell Sequencing

“…Using different normalization methods, we revealed that in some cases the total mRNA copy number in a cell does not correlate positively with the total number of genes in the raw sequencing dataset as it should be. In some studies the copy numbers of housekeeping protein mRNAs were used for normalization (Lin et al 2019), but it is known that the expression of them can show pulsatile dynamics at single-cell level (Liu et al 2016). However, when we applied the lowest dispersion and highest copy number transcripts for normalization without any respect to the function of the coded proteins, positive correlation was established between the total normalized copy number and the total original gene number (Figure S2C).…”

Comprehensive Analysis of Ubiquitously Expressed Genes in Human, From a Data-Driven Perspective

“…For example, in this work, we used the lowly expressed UEG clusters to evaluate and determine the optimal expression detection threshold. Interestingly, when we mapped the expression stability of the single-cell [45] onto our gene clusters, we observed that the sparsity (fraction of zeros) of single-cell profiles highly correlates with the global expression specificity and the expression magnitudes at bulk level (Supp Fig 24). The stably expressed UEG clusters with higher expression levels showed lower single-cell sparsity and vice versa .…”

“…Although our observation is limited to expression level through the UEG perspective, it may offer a new angle for these genes in beta-cells. Moreover, a recent single-cell study revealed that even for the most common UEG genes, such as GAPDH and ACTB , they showed a clear repression pattern in some cells [45]. This implies that repression of gene expression at single-cell level is likely a common regulatory mechanism and more disallowed genes might exist in specific cell types.…”