Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning

• Published in: BMC genomics Vol. 26; no. 1; pp. 358 - 21
• Main Authors: Rezapour, Mostafa, Narayanan, Aarthi, Mowery, Wyatt H., Gurcan, Metin Nafi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.04.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 2',5'-Oligoadenylate Synthetase - genetics; Agreements; Animal Genetics and Genomics; Animals; Bias; Binomial distribution; Biomarkers; Biomedical and Life Sciences; Cancer; Concordance analysis; Correlation analysis; Dendritic cells; Dendritic Cells - metabolism; Dendritic Cells - virology; Ebola virus; Ebola virus infections; Ebolavirus; Ebolavirus - genetics; Ebolavirus - physiology; Gene expression; Gene expression analysis; Gene Expression Profiling - methods; Gene sequencing; Generalized linear models; Genes; Genetic aspects; Genetic research; Hemorrhagic Fever, Ebola - genetics; Hemorrhagic Fever, Ebola - virology; Humans; Immunoregulation; Infections; Learning algorithms; Life Sciences; Machine Learning; Microarrays; Microbial Genetics and Genomics; Monocytes; NanoString technology; Physiological aspects; Plant Genetics and Genomics; Primates; Proteomics; Regression analysis; Regression models; Ribonucleic acid; RNA; RNA sequencing; RNA-Seq - methods; Statistical analysis; Strains (organisms); USP18 protein; Viral diseases; Viral infections; Viruses; United States; RNA sequencing; Gene expression analysis; Concordance analysis; NanoString technology; Machine learning
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-025-11553-6

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong correlation, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88. The mean and median coefficients were 0.83 and 0.85, respectively. Bland-Altman analysis confirmed high consistency across most measurements, with values falling within the 95% limits of agreement. Using a machine learning approach with the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, OAS1 was identified as a key gene signature for distinguishing RT-qPCR positive from negative samples. Remarkably, when used as the sole predictor in a logistic regression model, OAS1 maintained its predictive power on RNA-Seq data from the same cohort of EBOV-infected NHPs, achieving 100% accuracy in distinguishing infected from non-infected samples. OAS1 was also tested in a completely independent held-out test set, consisting of human monocyte-derived dendritic cells (DC) isolated and infected with different strains of the Ebola virus: wild-type (wt), VP35m, VP24m, along with a double mutant VP35m & VP24m, and again demonstrated a 100% accuracy rate in differentiating EBOV-infected from mock-infected samples, confirming its effectiveness as a predictive marker across diverse experimental setups and virus strains. Further differential expression analysis across both platforms identified 12 common genes (including ISG15 , OAS1 , IFI44 , IFI27 , IFIT2 , IFIT3 , IFI44L , MX1 , MX2 , OAS2 , RSAD2 , and OASL ) that showed the highest levels of statistical significance and biological relevance. Gene Ontology (GO) analysis confirmed the involvement of these genes in key immune and viral infection pathways, highlighting their importance in EBOV infection. RNA-Seq uniquely identified genes such as CASP5 , USP18 , and DDX60 , which are important in immune regulation and antiviral defense and were not detected by NanoString, demonstrating the broader detection capabilities of RNA-Seq. This study indicates a very strong agreement between RNA-Seq and NanoString platforms in gene expression analysis, with RNA-Seq displaying broader capabilities in identifying gene signatures.