Motivation: Hierarchical data structures are prevalent across several research fields, as they represent an organized 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 using phylogenetic trees. In light of this trend, the R/Bioconductor community has established a reproducible analytical framework for hierarchical data, which relies on the generic and optimized TreeSummarizedExperiment data container. However, this framework requires basic programming skills.

Results: To reduce the entry requirements, we developed iSEEtree, an R package, 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 an 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.

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.