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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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Benchmarking integration of single-cell differential expression.

Hai C T Nguyen¹, Bukyung Baik¹, Sora Yoon^1,2

¹Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

Nature Communications

|March 22, 2023

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This summary is machine-generated.

Benchmarking 46 workflows for single-cell RNA sequencing differential expression analysis reveals batch effects significantly impact performance. Specific methods excel under different conditions, with cell-type analysis outperforming bulk analysis for disease gene discovery.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Integrating single-cell RNA sequencing (scRNA-seq) data across samples is crucial for robust cell population analysis.
Differential expression (DE) analysis strategies for batch-corrected scRNA-seq data are not well-established.

Purpose of the Study:

To benchmark the performance of 46 workflows for DE analysis of scRNA-seq data with multiple batches.
To identify optimal DE analysis strategies considering batch effects, sequencing depth, and data sparsity.

Main Methods:

Comparative benchmarking of 46 distinct scRNA-seq DE analysis workflows.
Evaluation using simulated and real scRNA-seq datasets with varying batch effects, sequencing depths, and sparsity levels.
Assessment of batch correction strategies, including data correction and covariate modeling.

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Last Updated: Aug 6, 2025

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Main Results:

Batch effects, sequencing depth, and data sparsity significantly influence DE analysis performance.
Batch-corrected data offers limited improvement for sparse datasets; batch covariate modeling is effective for substantial batch effects.
Zero-inflation models underperform with low-depth data; limma-trend, Wilcoxon test, and fixed effects models perform well on uncorrected data.
Cell-type-specific DE analysis is superior to bulk analysis for identifying disease-related genes.

Conclusions:

No single workflow is universally optimal; method selection depends on data characteristics (sparsity, depth, batch effects).
Batch covariate modeling is recommended for datasets with significant batch effects.
Cell-type-specific DE analysis enhances the discovery of disease-associated genes.