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

• Published in: Nucleic acids research Vol. 49; no. 17; p. e98
• Main Authors: Reed, Eric R, Monti, Stefano
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27.09.2021
• Subjects: Algorithms; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Cluster Analysis; Computational Biology - methods; Female; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genomics - methods; Humans; Lymphocytes, Tumor-Infiltrating - classification; Lymphocytes, Tumor-Infiltrating - metabolism; Methods Online; Prognosis; Reproducibility of Results; Single-Cell Analysis - methods; Survival Analysis; T-Lymphocyte Subsets - classification; T-Lymphocyte Subsets - metabolism
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkab552

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. Graphical Abstract Graphical Abstract Overview of the K2Taxonomer algorithm, performance evaluation (simulated, bulk, single-cell data), and in-silico study identifying translation as a transcriptional program shared across a diverse subgroup of breast tumor infiltrating immunocytes.