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Curated single cell multimodal landmark datasets for R/Bioconductor

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journal contribution
posted on 2023-09-14, 14:28 authored by Kelly B. Eckenrode, Dario RighelliI, Marcel Ramos, Ricard Argelaguet, Christophe Vanderaa, Ludwig Geistlinger, Aedin CulhaneAedin Culhane, Laurent Gatto, Vincent Carey, Martin Morgan, Davide Risso, Levi Waldron


The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurementof genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.


We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor’s Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor’s ecosystem of hundreds of packages for single-cell and multimodal data.


We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.



PLoS Comput Biol, 2023, 19(8): e1011324


Public Library of Science

Other Funding information

This research was supported in part by the National Cancer Institute of the National Institutes of Health (2U24CA180996) to DRis, DRig, VC, MM, MR, KE, LG, and LW, and by the Chan Zuckerberg Initiative DAF (CZF2019-002443), an advised fund of Silicon Valley Community Foundation to DRis, DRig, MM. CV was supported by a PhD fellowship from the Belgian National Fund for Scientific Research (FNRS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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