Published September 23, 2020 | Version v1.4.8
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fionarhuang/TreeSummarizedExperiment: v1.4.8 TreeSummarizedExperiment

  • 1. Department of Molecular Life Sciences, University of Zurich; SIB Swiss Institute of Bioinformatics.
  • 2. Friedrich Miescher Institute for Biomedical Research
  • 3. Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
  • 4. MRC WIMM Centre for Computational Biology, University of Oxford, Oxford, OX3 9DS, UK.
  • 5. Department of Bioinformatics, School of Basic Medical University, Guangzhou, Guangdong, China; Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • 6. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health; Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.

Description

V1.4.8 in Bioconductor. 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.

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fionarhuang/TreeSummarizedExperiment-v1.4.8.zip

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Additional details

Funding

Beyond the average: computational tools for discovery in high-throughput single cell datasets 310030_175841
Swiss National Science Foundation