multiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasets
- 1Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen
Abstract
The 10x Genomics Epi Multiome assay measures gene expression and chromatin accessibility in the same nuclei, and studies increasingly profile many samples. Yet analysis after Cell Ranger ARC is usually assembled by hand from separate tools, which makes it slow to revise, hard to reproduce and memory-intensive for large studies. multiomeR addresses these problems with a configurable R pipeline for quality control, processing and integration of both data types, and cell-type annotation across many samples. Plain-text configuration files record every analytical choice, each step is tracked so that a change reruns only what it affects, and data stay on disk rather than in memory. Optional modules compare groups of donors, link accessible regions to the genes they may regulate, and test which cell types are enriched for genetic associations with a trait. In a benchmark on 31 public samples (375,757 nuclei), multiomeR had a shorter estimated run time than a Seurat v5.6/Signac v2 workflow (2.2 versus 5.6 h), because it runs independent steps in parallel. It also used fewer memory gigabyte-hours (87 versus 114 GB-hours), with similar total CPU time. multiomeR thus makes large multi-sample multiome studies practical to analyze, revise and extend.
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BibTeX
@misc{koefoed2026multiomer,
title = {{multiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasets}},
author = {T. G. Koefoed and S. He and M. Thodberg and L. M. Pikkupeura and K. Krizic and B. S. Kapel and R. S. Martinez and S. Rasmussen and T. Hansen},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.759117},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.759117v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - multiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasets
AU - T. G. Koefoed
AU - S. He
AU - M. Thodberg
AU - L. M. Pikkupeura
AU - K. Krizic
AU - B. S. Kapel
AU - R. S. Martinez
AU - S. Rasmussen
AU - T. Hansen
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.759117
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.759117v1
AB - The 10x Genomics Epi Multiome assay measures gene expression and chromatin accessibility in the same nuclei, and studies increasingly profile many samples. Yet analysis after Cell Ranger ARC is usually assembled by hand from separate tools, which makes it slow to revise, hard to reproduce and memory-intensive for large studies. multiomeR addresses these problems with a configurable R pipeline for quality control, processing and integration of both data types, and cell-type annotation across many samples. Plain-text configuration files record every analytical choice, each step is tracked so that a change reruns only what it affects, and data stay on disk rather than in memory. Optional modules compare groups of donors, link accessible regions to the genes they may regulate, and test which cell types are enriched for genetic associations with a trait. In a benchmark on 31 public samples (375,757 nuclei), multiomeR had a shorter estimated run time than a Seurat v5.6/Signac v2 workflow (2.2 versus 5.6 h), because it runs independent steps in parallel. It also used fewer memory gigabyte-hours (87 versus 114 GB-hours), with similar total CPU time. multiomeR thus makes large multi-sample multiome studies practical to analyze, revise and extend.
ER -