1 Installation

if (!requireNamespace("BiocManager", quietly = TRUE))


1.1 Load packages


2 Introduction

This package introduces a suite of single-cell multimodal landmark datasets for benchmarking and testing multimodal analysis methods via the ExperimentHub Bioconductor package. The scope of this package is to provide efficient access to a selection of curated, pre-integrated, publicly available landmark datasets for methods development and benchmarking.

3 Citing SingleCellMultiModal

Your citations are crucial in keeping our software free and open source. To cite our package see the citation (Eckenrode et al. (2023)) in the Reference section. You may also browse to the publication at the link here.

3.1 Representation

Users can obtain integrative representations of multiple modalities as a MultiAssayExperiment, a common core Bioconductor data structure relied on by dozens of multimodal data analysis packages. MultiAssayExperiment harmonizes data management of multiple experimental assays performed on an overlapping set of specimens. Although originally developed for patient data from multi-omics cancer studies, the MultiAssayExperiment framework naturally applies also to single cells. A schematic of the data structure can be seen below. In this context, “patients” are replaced by “cells”. We use MultiAssayExperiment because it provides a familiar user experience by extending SummarizedExperiment concepts and providing open ended compatibility with standard data classes present in Bioconductor such as the SingleCellExperiment.

3.2 Further resources

For more information on the MultiAssayExperiment data structure, please refer to Ramos et al. (2017) as well as the MultiAssayExperiment vignette.


Eckenrode, Kelly B, Dario Righelli, Marcel Ramos, Ricard Argelaguet, Christophe Vanderaa, Ludwig Geistlinger, Aedin C Culhane, et al. 2023. “Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor.” PLoS Comput. Biol. 19 (8): e1011324.

Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez, Tiffany Chan, et al. 2017. “Software for the Integration of Multiomics Experiments in Bioconductor.” Cancer Res. 77 (21): e39–e42.