Supplementary Materials

Supplementary Material for:

A Host-Based RT-PCR Gene Expression Signature to Identify Acute Respiratory Viral Infection

Aimee K. Zaas, Thomas Burke, Minhua Chen, Micah McClain, Bradly Nicholson, Timothy Veldman, Ephraim L. Tsalik, Vance Fowler, Emanuel P. Rivers, Ronny Otero, Stephen F. Kingsmore, Deepak Voora, Joseph Lucas, Alfred O. Hero, Lawrence Carin, Christopher W. Woods,* Geoffrey S. Ginsburg*

*Corresponding author. E-mail: Geoffrey.ginsburg@duke.edu (G.S.G.); Christopher.woods@duke.edu (C.W.W.)

Published 18 September 2013, Sci. Transl. Med. 5, 203ra126 (2013)
DOI: 10.1126/scitranslmed.3006280

This PDF file includes:

  • Methods
  • Table S1. Subject identification.
  • Table S2. Probes and classifier weights, training on H3N2.
  • Table S3. Probes and classifier weights, training on H1N1.
  • Table S4. Classification data for real-world patients.
  • Table S5. Comparison of the host viral infection score to commercially available rapid influenza testing.
  • Fig. S1. Classification of H3N2 infection using microbiological and clinical phenotypes.
  • Fig. S2. Classification of H1N1 infection using microbiological and clinical phenotypes.
  • Fig. S3. Cross-viral validation of RT-PCR classification using clinical and microbiological phenotypes (train on H3N2 cohort and test on H1N1 cohort).
  • Fig. S4. Cross-viral validation of RT-PCR classification using clinical and microbiological phenotypes (train on H1N1 cohort and test on H3N2 cohort).
  • Fig. S5. Classification accuracy remains if H3N2 and H1N1 cohorts are combined, with training on half of the total cohort and testing on half of the total cohort.
  • Fig. S6. Classification of virally infected emergency department subjects.

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