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iSEEtree: interactive explorer for hierarchical data

Giulio Benedetti, Ely Seraidarian, Theotime Pralas, Akewak Jeba, Tuomas Borman, Leo Lahti

TL;DR

iSEEtree addresses the need for accessible exploration of hierarchical microbiome data by providing a Shiny-based graphical interface built on TreeSummarizedExperiment and iSEE. It enables interactive, multi-panel visualization and linking across hierarchy-aware data layers without requiring extensive programming. The app supports compositional profiling, ordination, and hierarchical structure exploration, demonstrated on a gut microbiome ADHD dataset and extendable to other domains via miaDash. This work contributes a general, reproducible, and extensible tool for hierarchical data analysis within Bioconductor.

Abstract

$\textbf{Motivation:}$ Hierarchical data structures are prevalent across several fields of research, as they represent an organised and efficient approach to study complex interconnected systems. Their significance is particularly evident in microbiome analysis, where microbial communities are classified at various taxonomic levels along the phylogenetic tree. In light of this trend, the R/Bioconductor community has established a reproducible analytical framework for hierarchical data, which relies on the highly generic and optimised TreeSummarizedExperiment data container. However, using this framework requires basic proficiency in programming. $\textbf{Results:}$ To reduce the entry requirements, we developed iSEEtree, an R shiny app which provides a visual interface for the analysis and exploration of TreeSummarizedExperiment objects, thereby expanding the interactive graphics capabilities of related work to hierarchical structures. This way, users can interactively explore several aspects of their data without the need for extensive knowledge of R programming. We describe how iSEEtree enables the exploration of hierarchical multi-table data and demonstrate its functionality with applications to microbiome analysis. $\textbf{Availability and Implementation:}$ iSEEtree was implemented in the R programming language and is available on Bioconductor at https://bioconductor.org/packages/iSEEtree under an Artistic 2.0 license. $\textbf{Contact:}$ giulio.benedetti@utu.fi or leo.lahti@utu.fi.

iSEEtree: interactive explorer for hierarchical data

TL;DR

iSEEtree addresses the need for accessible exploration of hierarchical microbiome data by providing a Shiny-based graphical interface built on TreeSummarizedExperiment and iSEE. It enables interactive, multi-panel visualization and linking across hierarchy-aware data layers without requiring extensive programming. The app supports compositional profiling, ordination, and hierarchical structure exploration, demonstrated on a gut microbiome ADHD dataset and extendable to other domains via miaDash. This work contributes a general, reproducible, and extensible tool for hierarchical data analysis within Bioconductor.

Abstract

Hierarchical data structures are prevalent across several fields of research, as they represent an organised and efficient approach to study complex interconnected systems. Their significance is particularly evident in microbiome analysis, where microbial communities are classified at various taxonomic levels along the phylogenetic tree. In light of this trend, the R/Bioconductor community has established a reproducible analytical framework for hierarchical data, which relies on the highly generic and optimised TreeSummarizedExperiment data container. However, using this framework requires basic proficiency in programming. To reduce the entry requirements, we developed iSEEtree, an R shiny app which provides a visual interface for the analysis and exploration of TreeSummarizedExperiment objects, thereby expanding the interactive graphics capabilities of related work to hierarchical structures. This way, users can interactively explore several aspects of their data without the need for extensive knowledge of R programming. We describe how iSEEtree enables the exploration of hierarchical multi-table data and demonstrate its functionality with applications to microbiome analysis. iSEEtree was implemented in the R programming language and is available on Bioconductor at https://bioconductor.org/packages/iSEEtree under an Artistic 2.0 license. giulio.benedetti@utu.fi or leo.lahti@utu.fi.

Paper Structure

This paper contains 14 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Software Implementation
  3. Data containers
  4. Operative framework
  5. App functionality
  6. Applications
  7. Use cases
  8. Extensions
  9. Limitations
  10. Conclusion
  11. Software Availability
  12. Competing interests
  13. Author contributions statement
  14. Acknowledgments

Figures (1)

  • Figure 1: iSEEtree operative framework. A, Analytical workflow. (i) Data in standardised format is imported in R. This may include abundance assays and optional metadata on features, samples and tree hierarchies. (ii) A TreeSE object is constructed from the data. This is an optimised and standardised container for hierarchical data analysis. (iii) The object is input to iSEEtree, which launches an interface with a set of data-dependent visualisations. Adapted from huang2020treesummarizedexperiment. B, iSEEtree panel layout. The app provides a customisable interface, where each panel describes a certain aspect of the data under investigation. Panels can help explore the compositional organisation (i, iv and vi), identify main components (ii and iii) and inspect the hierarchical structure (v) of the data. The dataset shown in the snapshot was derived from tengeler2020gut.