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As the main primary producers of the oceans, microalgae serve as cornerstones of the ecosystems they are part of. Additionally, they are increasingly used for biotechnological purposes such as the pro...

Most microbial ecologists understand the importance of rigorous statistical analysis for conducting a replicable study. However, formal courses in the statis...

<p>Cyanobacteria form diverse communities and are important primary producers in Antarctic freshwater environments, but their geographic distribution pattern...

We consider the problem of estimating fold-changes in the expected value of a multivariate outcome that is observed subject to unknown sample-specific and category-specific perturbations. We are motivated by high-throughput sequencing studies of the abundance of microbial taxa, in which microbes are systematically over- and under-detected relative to their true abundances. Our log-linear model admits a partially identifiable estimand, and we establish full identifiability by imposing interpretable parameter constraints. To reduce bias and guarantee the existence of parameter estimates in the presence of sparse observations, we apply an asymptotically negligible and constraint-invariant penalty to our estimating function. We develop a fast coordinate descent algorithm for estimation, and an augmented Lagrangian algorithm for estimation under null hypotheses. We construct a model-robust score test, and demonstrate valid inference even for small sample sizes and violated distributional assumptions. The flexibility of the approach and comparisons to related methods are illustrated via a meta-analysis of microbial associations with colorectal cancer.

Upcoming events This is a list of upcoming conferences, symposia, and workshops that may be of interest to researchers working on natural products (particularly secondary/specialized metabolites) Sub...

Picking a great lab for your postdoc is crucial for your success in academia. In this video I go through the process of identifying the host lab(s) for you!#...