treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses
Ruizhu Huang, Charlotte Soneson, Pierre‐Luc Germain, Thomas Schmidt, Christian von Mering, Mark D. Robinson
Abstract
treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.