High-dimensional Count Research Articles

Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data

This paper explores a fast algorithm to select relevant predictors for the response process with panel count data. Based on the lasso penalized pseudo-objective function derived from an estimating equation, the coordinate ascent accelerates the estimation of regression coefficients. The coordinate ascent algorithm is capable of selecting relevant predictors for underdetermined problems where the number of predictors far exceeds the number of cases. It relies on a tuning constant that can be chosen by generalized cross-validation. Our tests on simulated and real data demonstrate the virtue of penalized regression in model building and prediction for panel count data in ultrahigh-dimensional settings.

Bayesian Mixture Models for Complex High Dimensional Count Data in Phage Display Experiments

Summary Phage display is a biological process that is used to screen random peptide libraries for ligands that bind to a target of interest with high affinity. On the basis of a count data set from an innovative multistage phage display experiment, we propose a class of Bayesian mixture models to cluster peptide counts into three groups that exhibit different display patterns across stages. Among the three groups, the investigators are particularly interested in that with an ascending display pattern in the counts, which implies that the peptides are likely to bind to the target with strong affinity. We apply a Bayesian false discovery rate approach to identify the peptides with the strongest affinity within the group. A list of peptides is obtained, among which important ones with meaningful functions are further validated by biologists. To examine the performance of the Bayesian model, we conduct a simulation study and obtain desirable results.