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¹State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, China.

Computational and Structural Biotechnology Journal

|February 6, 2025

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Multi-round doublet removal (MRDR) enhances single-cell RNA sequencing (scRNA-seq) data analysis by iteratively applying doublet detection algorithms. This strategy significantly improves doublet removal accuracy and downstream analysis, outperforming single-round methods.

Area of Science:

Single-cell genomics
Bioinformatics
Computational biology

Background:

Doublets are artifacts in single-cell RNA sequencing (scRNA-seq) data, confounding differential expression and trajectory analyses.
Existing doublet removal methods often retain residual doublets due to algorithmic randomness.

Purpose of the Study:

To introduce and evaluate a multi-round doublet removal (MRDR) strategy for enhanced doublet detection in scRNA-seq data.
To assess the performance of MRDR across various datasets and popular doublet detection tools.

Main Methods:

Implemented a multi-round strategy by cyclically applying doublet detection algorithms.
Evaluated MRDR using 14 real-world, 29 barcoded, and 106 synthetic scRNA-seq datasets.
Compared MRDR with single-round removal using DoubletFinder, cxds, bcds, and hybrid tools.

Keywords:

Doublet removal Multi-round doublet removal strategy Single-cell RNA sequencing Synthetic dataset

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Main Results:

MRDR improved recall by 50% for DoubletFinder in real-world datasets and increased ROC by ~0.04 for other tools.
cxds with two rounds yielded optimal results in barcoded datasets.
MRDR demonstrated superior doublet removal and improved ROC by at least 0.05 in simulated datasets compared to single-round methods.

Conclusions:

The MRDR strategy effectively reduces doublets and enhances downstream analyses like differential gene expression and cell trajectory inference.
MRDR is recommended for integration into standard scRNA-seq analysis pipelines, with cxds suggested for two-round iterations.