Rank-based Bayesian variable selection for genome-wide transcriptomic analyses.

ABSTRACT: Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Ba

SUBMITTER: Eliseussen E 

PROVIDER: S-EPMC9796757 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature