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Bias, robustness and scalability in single-cell differential expression analysis


Many methods have been used to determine differential gene expression from single-cell RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that bulk RNA-seq analysis methods do not generally perform worse than those developed specifically for scRNA-seq. We also present conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results. Each data set provides abundance estimates for both genes and transcripts, as well as quality control and exploratory analysis reports.

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Figure 1: Type I error control across several instances from eight single-cell null data sets.
Figure 2: Characteristics of genes falsely called significant by DE methods.
Figure 3: Average similarities between gene rankings obtained by the evaluated DE methods.
Figure 4: Differential expression detection performance, summarized across all instances of the three simulated data sets.
Figure 5: Summary of DE method performance across all major evaluation criteria.

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The authors acknowledge M. Love and V. Svensson for helpful online instructions regarding automated download of raw data from ENA. This study was supported by the Forschungskredit of the University of Zurich, grant no. FK-16-107 to C.S.

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Authors and Affiliations



C.S. and M.D.R. designed analyses and wrote the manuscript. C.S. performed analyses. Both authors read and approved the final manuscript.

Corresponding authors

Correspondence to Charlotte Soneson or Mark D Robinson.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–32 and Supplementary Tables 1–3 (PDF 15651 kb)

Life Sciences Reporting Summary (PDF 213 kb)

Supplementary Data

countsimQC reports, illustrating the similarity between each simulated dataset and the respective underlying real data set (ZIP 18523 kb)

Supplementary Software

Snapshot (at time of publication) of the two GitHub repositories containing the code used to build the conquer database ( and to perform the method comparison ( (ZIP 4213 kb)

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Soneson, C., Robinson, M. Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods 15, 255–261 (2018).

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