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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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BIRD: identifying cell doublets via biallelic expression from single cells.

Kerem Wainer-Katsir¹, Michal Linial¹

¹Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Givat Ram 91904, Israel.

Bioinformatics (Oxford, England)

|July 14, 2020

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A new method, BIallelic Ratio for Doublets (BIRD), identifies cell doublets in single-cell RNA sequencing data from genetically identical cells. This approach uses allelic expression ratios to accurately detect doublets, improving data quality for research.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell transcriptomics enables large-scale cellular analysis.
Increased scale risks cell doublet contamination, complicating data interpretation.
Existing doublet detection tools primarily focus on cells with different genetic backgrounds.

Purpose of the Study:

To develop a novel method for identifying cell doublets originating from genetically identical cells.
To leverage monoallelic and biallelic expression ratios for doublet detection.
To introduce the BIallelic Ratio for Doublets (BIRD) pipeline.

Main Methods:

Analysis of transcriptomic data from single cells with identical genetic backgrounds.
Utilizing heterologous genetic variations and allelic expression ratios.

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Last Updated: Dec 15, 2025

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Training a predictive model using artificially created doublets from actual data.

Application of the BIRD pipeline to Smart-seq and 10X Genomics datasets.

Main Results:

The BIRD pipeline achieved 100% accuracy in identifying simulated doublets in Smart-seq fibroblast data.
Verification of bonafide doublets using X-chromosome biallelic expression in female fibroblasts.
Achieved 83% average accuracy and 0.88 AUC for 10X Genomics human peripheral blood cell data (∼13,300 cells).
Performance is dependent on data type, coverage, and genetic diversity.

Conclusions:

BIRD effectively identifies doublets from cell mixtures with identical genetic backgrounds.
High-coverage Smart-seq data yields maximal performance for doublet detection.
The method's success is context-dependent on experimental and genomic factors.