Pathway expression analysis

• Published in: Scientific reports Vol. 12; no. 1; pp. 21839 - 13
• Main Authors: Mankovich, Nathan, Kehoe, Eric, Peterson, Amy, Kirby, Michael
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/2017/2079; 631/1647/48; Classification; Datasets; Gene expression; Gene Expression Profiling; Humanities and Social Sciences; Humans; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H3N2 Subtype; multidisciplinary; Respiratory syncytial virus; Respiratory Syncytial Virus Infections - genetics; Respiratory Syncytial Virus, Human - genetics; Rhinovirus; Science; Science (multidisciplinary); Support vector machines; Transcriptome; Transcriptomics; Viruses
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-26381-x

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.