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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • 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

摘要

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.