Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

Chin-Yi Chu; Xing Qiu; Matthew N. McCall; Lu Wang; Anthony Corbett; Jeanne Holden-Wiltse; Christopher Slaunwhite; Qian Wang; Christopher Anderson; Alex Grier; Steven R. Gill; Gloria S. Pryhuber; Ann R. Falsey; David J. Topham; Mary T. Caserta; Edward E. Walsh; Thomas J Mariani

Our evaluation of human biospecimen-derived RNAseq data has utilized multiple analytical and statistical approaches, in an effort to thoroughly interrogate the in vivo responses they reflect. Critically, we have applied appropriately conservative corrections for multiple testing in each case. We believe that use of each of these analytical approaches is justified, as they each provide distinct yet important insight. Univariate analyses are used to estimate the direct associations between one covariate and individual genes. These analyses are easy to perform and interpret and do not suffer from potential collinearity issues among covariates. As such, they are useful to initially explore the overall pattern of associations between clinical covariates and transcriptome profiles. On the other hand, a multivariate model considers the influence of several covariates on gene expression simultaneously; therefore, the estimated association between the response variable and a given covariate is less likely to be affected by unmodeled interdependences. Arguably, a multivariate model is more natural than multiple univariate models because it more closely approximates the complex biological processes that collectively influence the transcriptome. That being said, more caution must be exercised when using complex multivariate models. When uninformative covariates are included in a multivariate model, they may mask true associations and reduce the statistical power to detect informative associations. A more serious issue is All rights reserved. No reuse allowed without permission.