Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm.

ABSTRACT: Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL's latent space has a fairly straightforward biological interpretation. PASL is shown to outperform in predictive performance the state-of-the-art method (PLIER) on two collections of breast cancer and leukemia gene expression dat

SUBMITTER: Karagiannaki I 

PROVIDER: S-EPMC10600308 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature