Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Maternal Effects of Respiratory Syncytial Virus Infection during Pregnancy.

Given the illness and deaths caused by respiratory syncytial virus (RSV) infection during the first year of life, preventing infant RSV infections through maternal vaccination is intriguing. However, little is known about the extent and maternal effects of RSV infection during pregnancy. We describe 3 cases of maternal RSV infection diagnosed at a US center during winter 2014. Case-patient 1 (26 years old, week 33 of gestation) received a diagnosis of RSV infection and required mechanical ventilation. Case-patient 2 (27 years old, week 34 of gestation) received a diagnosis of infection with influenza A(H1N1) virus and RSV and required mechanical ventilation. Case-patient 3 (21 years old, week 32 of gestation) received a diagnosis of group A streptococcus pharyngitis and RSV infection and was monitored as an outpatient. Clarifying the effects of maternal RSV infection could yield valuable insights into potential maternal and fetal benefits of an effective RSV vaccination program.