Benchmarking integration of single-cell differential expression

Hai C. T. Nguyen, Bukyung Baik, Sora Yoon, Taesung Park and Dougu Nam ()
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Hai C. T. Nguyen: Ulsan National Institute of Science and Technology
Bukyung Baik: Ulsan National Institute of Science and Technology
Sora Yoon: Ulsan National Institute of Science and Technology
Taesung Park: Seoul National University
Dougu Nam: Ulsan National Institute of Science and Technology

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.

Date: 2023
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DOI: 10.1038/s41467-023-37126-3

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