K2Taxonomer
Introduction
K2Taxonomer is an R package built around a “top-down” recursive partitioning framework to perform unsupervised learning of nested “taxonomy-like” subgroups from high-throughput -omics data. This framework was devised to be flexibly applicable to different data structures, supporting the analysis of both bulk and single-cell data sets. In addition to implementing the algorithm, the package includes functionality to annotate estimated subgroups using gene- and pathway-level analyses.
The recursive partitioning approach utilized by K2Taxonomer presents advantages over conventional unsupervised approaches, including:
- Identification of robust partitions of a set of observations by aggregating ensembles of partition estimates from repeated perturbations of the data.
- Partition-specific feature selection, preventing the need to perform feature selection on the whole data set prior to running the algorithm.
- Tailoring of analyses to specific data structures through the use of different clustering algorithms for partition estimation.
The package documentation describes applications of K2Taxonomer to both single-cell and bulk gene expression data. For analyses of single-cell gene expression data K2Taxonomer is designed to characterize nested subgroups of previously identified cell types, such as those previously estimated by scRNAseq clustering analysis.
Cite
Reed, Eric R, and Stefano Monti. “Multi-Resolution Characterization of Molecular Taxonomies in Bulk and Single-Cell Transcriptomics Data.” Nucleic Acids Research 49, no. 17 (September 27, 2021): e98. https://doi.org/10.1093/nar/gkab552.
Documentation
Articles describing K2Taxonomer workflows can be found on the package’s GitHub Page.
Installation
You may install K2Taxonomer from GitHub directly using the devtools R package or clone the repository and download from source.
install.packages("devtools")
devtools::install_github("montilab/K2Taxonomer")