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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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03643-1 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Michael R. Fiorini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c19bbee6db9649c3b9029639c11d8d6e |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-c19bbee6db9649c3b9029639c11d8d6e2025-08-20T04:01:36ZengBMCGenome Biology1474-760X2025-07-0126113910.1186/s13059-025-03643-1Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample poolingMichael R. Fiorini0Saeid Amiri1Allison A. Dilliott2Cristine M. Yde Ohki3Lukasz Smigielski4Susanne Walitza5Edward A. Fon6Edna Grünblatt7Rhalena A. Thomas8Sali M. K. Farhan9Department of Human Genetics, McGill UniversityThe Montreal Neurological Institute-Hospital, McGill UniversityThe Montreal Neurological Institute-Hospital, McGill UniversityDepartment of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital of Zurich, University of ZurichDepartment of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital of Zurich, University of ZurichDepartment of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital of Zurich, University of ZurichThe Montreal Neurological Institute-Hospital, McGill UniversityDepartment of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital of Zurich, University of ZurichThe Montreal Neurological Institute-Hospital, McGill UniversityDepartment of Human Genetics, McGill UniversityAbstract 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.https://doi.org/10.1186/s13059-025-03643-1Single-cell RNA sequencingMultiplexingSample poolingGenetic demultiplexingInduced pluripotent stem cellsDifferential gene expression |
| spellingShingle | 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 Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling Genome Biology Single-cell RNA sequencing Multiplexing Sample pooling Genetic demultiplexing Induced pluripotent stem cells Differential gene expression |
| title | Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling |
| title_full | Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling |
| title_fullStr | Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling |
| title_full_unstemmed | Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling |
| title_short | Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling |
| title_sort | ensemblex an accuracy weighted ensemble genetic demultiplexing framework for population scale scrnaseq sample pooling |
| topic | Single-cell RNA sequencing Multiplexing Sample pooling Genetic demultiplexing Induced pluripotent stem cells Differential gene expression |
| url | https://doi.org/10.1186/s13059-025-03643-1 |
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