ArticleNucleic acids research2021
Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- T cell receptor repertoire dynamics and newly identified functional regulators of autologous tumor-infiltrating lymphocyte therapy in advanced solid tumors.Journal of translational medicine · 2026Article
- A highly resolved integrated single-cell atlas of HPV-negative head and neck cancer.Communications medicine · 2026Article
- A highly resolved integrated single-cell atlas of human breast cancers.NAR genomics and bioinformatics · 2026Article
- INHBA: a mitochondrial-related pan-cell death gene associated with the prognosis and immunity of OSCC.Scientific reports · 2026Article
- Plasma exosomes from individuals with type 2 diabetes drive breast cancer aggression in patient-derived organoids.Communications biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- β-catenin/CBP activation of mTORC1 signaling promotes partial epithelial-mesenchymal states in head and neck cancer.Translational research : the journal of laboratory and clinical medicine · 2023Article
- Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity.EBioMedicine · 2023Article
- A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens.Environmental health perspectives · 2021Article
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2 authors.
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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 types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.
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