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Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis.

Tian Lan¹, Gyorgy Hutvagner², Qing Lan³

¹Faculty of Engineering and IT in the University of Technology Sydney.

Briefings in Bioinformatics

|October 19, 2020

Summary

This study surveys 10 tools for single-cell mRNA sequencing data preprocessing. Saver and Baynorm excel at dropout normalization, while Beer and Batchelor are best for batch correction, with combined tools showing promise for mixed effects.

Keywords:

batch effect dropout normalization tools performance evaluation single-cell mRNA sequencing

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Area of Science:

Genomics
Bioinformatics

Background:

Single-cell mRNA sequencing offers deep insights into gene expression.
Challenges include low mRNA capture efficiency, technical noise, and batch effects.
Normalization is crucial for accurate single-cell data analysis.

Purpose of the Study:

To survey and evaluate 10 computational tools for single-cell mRNA sequencing data preprocessing.
To assess the performance of dropout and batch effect normalization methods.
To identify optimal tool combinations for complex data scenarios.

Main Methods:

Surveyed 6 dropout normalization tools and 4 batch effect correction tools.
Evaluated tool performance on simulated datasets with dropout, batch effects, or both.
Compared normalization strategies based on simulated data.

Main Results:

Saver and Baynorm demonstrated superior dropout normalization performance.
Beer and Batchelor showed better batch effect correction.
Saver-Beer and Baynorm-Beer combinations excelled in normalizing mixed dropout and batch effects.

Conclusions:

Specific tools like Saver, Baynorm, and Beer offer effective solutions for single-cell data normalization.
Over-normalization in dropout correction and preserving cell heterogeneity in batch correction require further investigation.