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Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample
Michael R Fiorini1,2, Saeid Amiri2, Allison A Dilliott2,3
1Department of Human Genetics, McGill University, Montreal, QC, H3A 2B4, Canada.
Genome Biology
|July 3, 2025
Summary
Ensemblex improves genetic demultiplexing accuracy for population-scale single-cell RNA sequencing. This ensemble framework enhances donor identification in highly multiplexed samples, unlocking greater biological insights.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Population-scale single-cell RNA sequencing (scRNA-seq) is crucial for biological discovery but limited by high costs.
- Genetic demultiplexing tools identify cell origins using genetic variation but struggle with accuracy in highly multiplexed samples.
Purpose of the Study:
- To develop a robust genetic demultiplexing framework that enhances accuracy in high-multiplexing scenarios.
- To improve the analytical potential of population-scale scRNA-seq datasets.
Main Methods:
- Introduction of Ensemblex, an accuracy-weighted ensemble framework integrating four distinct demultiplexing algorithms.
- Validation using computationally and experimentally pooled samples.
Main Results:
- Ensemblex demonstrates superior accuracy in genetic demultiplexing compared to existing methods.
- The framework effectively identifies the most probable subject labels for pooled cells.
Conclusions:
- Ensemblex significantly enhances the reliability of genetic demultiplexing for large-scale scRNA-seq studies.
- Accurate demultiplexing with Ensemblex enables more robust downstream biological analyses and discoveries.
