NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.

ABSTRACT: Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant g

SUBMITTER: Ruiz-Arenas C 

PROVIDER: S-EPMC11109970 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature