By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.

通过分析单个细胞的转录组，单细胞 RNA 测序为研究细胞异质性提供了无与伦比的分辨率。然而，这是以高技术噪声为代价的，包括捕获效率和文库生成方面的细胞特异性偏差。消除这些偏差的一种策略是向每个细胞中添加恒定量的尖峰 RNA，并缩放观察到的表达值，以使尖峰转录本的覆盖率在整个细胞中保持恒定。这种方法以前曾受到批评，因为它的准确性取决于向每个样品中精确添加尖峰 RNA。在这里，我们使用两组不同的尖峰 RNA 进行混合实验，以量化在基于板的协议中添加到每个孔中的尖峰 RNA 量的变化。由于两个尖峰输入集之间的行为差​​异，我们还获得了方差的上限。我们证明这两个因素对总技术差异的贡献很小，并且对下游分析的影响很小，例如检测高度可变的基因和聚类。我们的结果表明，使用spike-in 转录本进行缩放标准化对于单细胞RNA 测序数据分析中的常规使用而言足够可靠。