GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.
Cell Rep Methods
; 4(6): 100794, 2024 Jun 17.
Article
em En
| MEDLINE
| ID: mdl-38861988
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Leucócitos Mononucleares
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Análise de Sequência de RNA
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Redes Reguladoras de Genes
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Análise de Célula Única
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SARS-CoV-2
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COVID-19
Limite:
Humans
Idioma:
En
Revista:
Cell Rep Methods
Ano de publicação:
2024
Tipo de documento:
Article