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.This work was supported by Cancer Research UK (core funding to JCM, award no. A17197), the University of Cambridge and Hutchison Whampoa Limited. JCM was also supported by core funding
from EMBL. LHV was supported by an EMBL Interdisciplinary Postdoctoral fellowship. Work in the G ottgens group was supported by Cancer Research UK, Bloodwise, the National Institute of
Diabetes and Digestive and Kidney Diseases, the Leukemia and Lymphoma Society and core infrastructure grants from the Wellcome Trust and the Medical Research Council to the Cambridge Stem
Cell Institute
