![]() Single-cell multimodal omics technologies (Nature Method of the Year 2019 ) couple single-cell RNA sequencing with other molecular profiles such as DNA sequences, methylation, chromatin accessibility, cell surface proteins, and spatial information, simultaneously in the same cell. ![]() Understanding the quantitative relationship between molecules and physiology has motivated the development of quantitative profiling techniques, especially for single-cell sequencing. This is a PLOS Computational Biology Benchmarking paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. CV was supported by a PhD fellowship from the Belgian National Fund for Scientific Research (FNRS). įunding: 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. The original 10X Genomics Multiome data are available from. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data reviewed and curated in this review are publicly available under the Artistic 2.0 license as the SingleCellMultiModal Bioconductor package ( ), with open development and issue tracking on Github ( ). Received: NovemAccepted: JPublished: August 25, 2023Ĭopyright: © 2023 Eckenrode et al. ![]() PLoS Comput Biol 19(8):Įditor: Mingyao Li, University of Pennsylvania, UNITED STATES (2023) Curated single cell multimodal landmark datasets for R/Bioconductor. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductor’s ecosystem of hundreds of packages for single-cell and multimodal data.Ĭitation: Eckenrode KB, Righelli D, Ramos M, Argelaguet R, Vanderaa C, Geistlinger L, et al. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. Experimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures.
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