GENIX – A computational network analysis approach to identify signature genes
2024
Sanofi, Cambridge, USA
Single-cell RNA sequencing (scRNA-seq) has transformed the understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects, and is susceptible to false positives. In this study, GENIX (gene expression network importance examination) is presented, a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. GENIX was benchmarked using both synthetic and experimental datasets. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.
GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions
Virginia Savova, Nima Nouri
Added on: 07-08-2024
[1] https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(24)00150-4
