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TreeSummarizedExperiment: a S4 class for data with hierarchical structure

Huang, Ruizhu; Soneson, Charlotte; Ernst, Felix G M; Rue-Albrecht, Kevin C; Yu, Guangchuang; Hicks, Stephanie C; Robinson, Mark D (2020). TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Research, 9:1246.

Abstract

Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
08 Research Priority Programs > Evolution in Action: From Genomes to Ecosystems
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Immunology and Microbiology
Life Sciences > General Pharmacology, Toxicology and Pharmaceutics
Language:English
Date:15 October 2020
Deposited On:25 Jan 2021 06:50
Last Modified:10 Sep 2024 03:41
Publisher:Faculty of 1000 Ltd.
ISSN:2046-1402
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.12688/f1000research.26669.1
PubMed ID:33274053
Project Information:
  • Funder: SNSF
  • Grant ID: 310030_175841
  • Project Title: Beyond the average: computational tools for discovery in high-throughput single cell datasets
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  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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