Exploring the latent space of transcriptomic data with topic modeling.

ABSTRACT: The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous

SUBMITTER: Valle F 

PROVIDER: S-EPMC12012681 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature