Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

ABSTRACT: The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer sampl

SUBMITTER: Vitelli V 

PROVIDER: S-EPMC10061294 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature