Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.

ABSTRACT: As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a 'taxonomy-like' structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other '-omics', data. For each of these data typ

SUBMITTER: Reed ER 

PROVIDER: S-EPMC8464061 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature