TY - JOUR
T1 - Curated single cell multimodal landmark datasets for R/Bioconductor
AU - Eckenrode, Kelly B.
AU - Righelli, Dario
AU - Ramos, Marcel
AU - Argelaguet, Ricard
AU - Vanderaa, Christophe
AU - Geistlinger, Ludwig
AU - Culhane, Aedin C.
AU - Gatto, Laurent
AU - Carey, Vincent
AU - Morgan, Martin
AU - Risso, Davide
AU - Waldron, Levi
N1 - Publisher Copyright:
© 2023 Eckenrode et al. 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.
PY - 2023/8
Y1 - 2023/8
N2 - Background The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of 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. Results 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. Conclusions 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.
AB - Background The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of 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. Results 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. Conclusions 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.
UR - http://www.scopus.com/inward/record.url?scp=85170717169&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1011324
DO - 10.1371/journal.pcbi.1011324
M3 - Article
C2 - 37624866
AN - SCOPUS:85170717169
SN - 1553-734X
VL - 19
SP - e1011324
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8 August
M1 - e1011324
ER -