Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas.

ABSTRACT: Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene prediction. We address the limitations of de novo network inference from single-cell expression data through dropout imputation, metacell formation, and data transformation. Employing this data preprocessing pipeline, we inferred cell-type-specific co-expression links from single-cell atlas data,

SUBMITTER: Sung E 

PROVIDER: S-EPMC11892045 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature