A computational pipeline for the classification of seasonal and pandemic influenza viruses

This advanced analysis is the continuity of previous work done on studying strain-specific immune responses among seasonal (A/New-Caledonia/20/1999 and A/Texas/36/1991) and pandemic (A/Brevig-Mission/1/1918 and A/California/4/2009) H1N1 influenza viruses. We developed a computational pipeline for analyzing longitudinal gene expression data to find gene signatures capable of classifying seasonal and pandemic responses at very earlier time points (first 4 hours post infection). The computational pipeline was based on principal component analysis, decision trees and support vector machines. Using the developed pipeline, we found a small yet robust subset of critical gene signatures (~200) capable of distinguishing between seasonal and pandemic influenza responses. Independent testing of the model on other publically available datasets from GEO, provided over 80% of the prediction accuracy.

Note: This work is an extension of previous work done by Dr. Thakar and her collaborators that can be accessed at:

Hartmann, Boris M., et al. "Human dendritic cell response signatures distinguish 1918, pandemic, and seasonal H1N1 influenza viruses." Journal of virology 89.20 (2015): 10190-10205. (Link to paper)

Thakar, Juilee, et al. "Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism." BMC immunology 16.1 (2015): 46. (Link to paper)

Zaslavsky, Elena, et al. "Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection." BMC bioinformatics 14.Suppl 6 (2013): S1. (Link to paper)