multiomeR Manual
1 Start here
1.1 What is multiomeR?
multiomeR is a targets-based workflow for processing and analyzing single-nucleus 10x Genomics Multiome ATAC + Gene Expression datasets. It is meant to be adapted to your own data, compute setup, and biological questions.
The workflow starts from cellranger-arc count outputs, processes gene-expression (GEX) and ATAC data, builds multimodal aggregations, and supports optional downstream modules for differential analyses and genetic enrichment for human datasets.
1.2 Your first analysis
Start with the public demo: two human GEM wells with supplied configuration. You will install the software, run one joint analysis, and read its cell metadata and multimodal object. This gives you a working example before you choose settings for your own study.
You need basic R skills, a Linux terminal, and a machine with sufficient memory and disk space. You do not need to know how to write a targets pipeline. Commands labelled Bash run in the terminal; commands labelled R run in the R session opened during installation. Run both from the repository folder unless stated otherwise.
1.3 Find what you need
| If you want to… | Start here |
|---|---|
| See what the workflow produces | Browse the main pipeline output gallery. |
| Try multiomeR on public data | Follow the three-part quickstart: install, run, then inspect the outputs. |
| Configure your own data | Read the main-pipeline overview, prepare the configuration and inputs, then run one aggregation. |
| Add a downstream analysis | Check the prerequisites for differential analyses or genetic enrichment. |
| Understand or modify the internals | Use the separate implementation book. |
1.4 Terms used in this manual
In this manual, a GEM well is one configured 10x library and output directory, an aggregation is a joint analysis of one or more GEM wells, and a donor is the individual identified by donor_id. One GEM well may contain multiple donors.
A target is a named result, such as a metadata table, matrix directory, or plot. Its dependencies are the inputs and earlier results needed to build it. You request the result you want; targets works out the order and reuses results that are up to date. The store is the folder where it keeps results and the records needed for reruns. For a small worked introduction, see the targets walkthrough.
1.5 Workflow at a glance
The main pipeline processes each GEM well, aggregates selected GEM wells, and builds multimodal RNA/ATAC outputs for clustering, cell typing, and WNN integration. Two optional modules extend completed aggregations with differential analyses or genetic enrichment.
1.6 Development status
multiomeR is in beta and may introduce breaking changes. The numbered QC reviews show what to inspect before each analysis stage. Acceptance criteria still depend on the tissue, study design, and intended use. Report problems or questions through GitHub issues.
Continue to Install and prepare the demo.