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 flexible to different data structures, enabling its applicability to analyze both bulk and single-cell data sets. In addition to implementing the algorithm, this 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:
The documentation of this package describes how
K2Taxonomer can be applied to either bulk or single-cell 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.
A preprint of the manuscript describing
K2Taxonomer is publicly available here.
Here we demonstrate the basic functionality of
K2Taxonomer, which is described in more detail in the vignette, Running K2Taxonomer.
An alternative workflow for running
K2Taxonomer for subgrouping cell type labels using single-cell expression data is described in the vignette, Running K2Taxonomer on single-cell RNA sequencing data.
K2res <- K2preproc(sample.ExpressionSet)
K2res <- K2tax(K2res, stabThresh=0.5)
K2res <- runDGEmods(K2res)
K2res <- runGSVAmods(K2res, ssGSEAalg="gsva", ssGSEAcores=1, verbose=FALSE)
K2res <- runDSSEmods(K2res)
K2dashboard(K2res, analysis_name="Example", output_dir=".")