Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling

Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling

Abstract Multiplexing samples from distinct individuals prior to sequencing is a promising step towards achieving population-scale single-cell RNA sequencing by reducing the restrictive costs of the technology. Individual genetic demultiplexing tools resolve the donor-of-origin identity of pooled ce...

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Bibliographic Details
Main Authors: Michael R. Fiorini, Saeid Amiri, Allison A. Dilliott, Cristine M. Yde Ohki, Lukasz Smigielski, Susanne Walitza, Edward A. Fon, Edna Grünblatt, Rhalena A. Thomas, Sali M. K. Farhan
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Genome Biology
Subjects:
Single-cell RNA sequencing
Multiplexing
Sample pooling
Genetic demultiplexing
Induced pluripotent stem cells
Differential gene expression
Online Access:https://doi.org/10.1186/s13059-025-03643-1
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Summary:Abstract Multiplexing samples from distinct individuals prior to sequencing is a promising step towards achieving population-scale single-cell RNA sequencing by reducing the restrictive costs of the technology. Individual genetic demultiplexing tools resolve the donor-of-origin identity of pooled cells using natural genetic variation but present diminished accuracy on highly multiplexed experiments, impeding the analytic potential of the dataset. In response, we introduce Ensemblex: an accuracy-weighted, ensemble genetic demultiplexing framework that integrates four distinct algorithms to identify the most probable subject labels. Using computationally and experimentally pooled samples, we demonstrate Ensemblex’s superior accuracy and illustrate the implications of robust demultiplexing on biological analyses.
ISSN:1474-760X

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