ArticleCell reports methods2024
GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Conserved Blood Transcriptome Patterns Highlight microRNA and Hub Gene Drivers of Neurodegeneration.Genes · 2025Article
- Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP.Genome research · 2025Article
- Single-cell RNA-seq data augmentation using generative Fourier transformer.Communications biology · 2025Article
- iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.Bioinformatics advances · 2025Article
- Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis.Frontiers in systems biology · 2024Article
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Authors and funding
5 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our 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. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. 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.
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