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Curated single cell multimodal landmark datasets for R/Bioconductor.

Kelly B Eckenrode1,2, Dario Righelli3, Marcel Ramos1,2,4

  • 1Graduate School of Public Health and Health Policy, City University of New York, NY, NY, United States of America.

Plos Computational Biology
|August 25, 2023
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Summary

A new Bioconductor package, SingleCellMultiModal, provides easy access to landmark single-cell multimodal datasets. This facilitates the development of computational methods for analyzing complex biological data, including cell differentiation and disease states.

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Area of Science:

  • Computational Biology
  • Genomics
  • Epigenomics
  • Proteomics
  • Single-cell Analysis

Background:

  • High-throughput single-cell profiling primarily quantifies RNA expression.
  • Emerging multimodal methods simultaneously measure multiple molecular layers (genomic, proteomic, epigenetic, spatial) from the same cells.
  • Developing new statistical and computational methods for multimodal single-cell data requires accessible landmark datasets.

Purpose of the Study:

  • To facilitate the development of statistical and computational methods in Bioconductor for single-cell multimodal data analysis.
  • To provide easy access to standardized, processed landmark datasets from various single-cell multimodal technologies.
  • To enable integrative analyses of molecular layers and phenotypic outputs.

Main Methods:

  • Collected, processed, and packaged publicly available landmark datasets from single-cell multimodal protocols (e.g., CITE-Seq, 10X Multiome, seqFISH).
  • Integrated data modalities using the MultiAssayExperiment Bioconductor class.
  • Distributed datasets via the SingleCellMultiModal package in Bioconductor's ExperimentHub.

Main Results:

  • Single-command access to landmark datasets from seven single-cell multimodal technologies is now available.
  • Datasets are provided without requiring further processing or data wrangling.
  • The SingleCellMultiModal package enables immediate use within Bioconductor's extensive ecosystem.

Conclusions:

  • The SingleCellMultiModal package simplifies integrative analyses of multimodal single-cell data.
  • It will accelerate the development of bioinformatic and statistical methods for analyzing cell differentiation, activity, and disease.
  • Facilitates addressing the challenges of integrating molecular layers and analyzing complex phenotypic outputs.