Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

Massively Parallel Signature Sequencing (MPSS) is a high-throughput counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression (SAGE) and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflated Poisson (ZIP) distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base-pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using non-parametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries.     

Regeneration-based bootstrap for Markov chains

We consider a bootstrap method for Markov chains where the original chain is broken into a (random) number of cycles based on an atom (regeneration point) and the bootstrap scheme resamples from these cycles. We investigate the asymptotic accuracy of this method for the case of a sum (or a sample mean) related to the Markov chain. Under some standard moment conditions, the method is shown to be at least as good as the normal approximation, and better (second-order accurate) in the case of nonlattice summands. We give three examples to illustrate the applicability of our results.     

Labour market integration of FRY refugees in Sweden vs. Denmark

We compare the long-run labour market integration of the Former Republic of Yugoslavia (FRY) refugees who arrived in the 1990s to the Scandinavian countries in the settings of Sweden and Denmark, respectively. These otherwise similar countries faced different economic conditions at the time of arrival and over the observation period. They also differed in terms of the restrictiveness of asylum policies and in attitudes towards immigration. Using register data from Statistics Sweden and Statistics Denmark, we show that FRY refugees, in both child and adult generations at arrival, are better integrated 12–15 years later in Sweden than in Denmark, even though Sweden experienced a higher level of unemployment than Denmark throughout the period. Our findings suggest that asylum policies promoting early labour market entry and early exposure to a host country's schooling system, as well as access to disability pension and social assistance, are important factors affecting refugee labour market integration.