Hypothesis weighting improves the power of large-scale multiple testing. We describe a method that uses covariates independent of the p-values under the null hypothesis, but informative of each test’s power or prior probability of the null hypothesis. Independent hypothesis weighting (IHW) increases power while controlling the false discovery rate (FDR). IHW is a practical approach to discover associations in large datasets as encountered in genomics and high-throughput biology. Availability: www.bioconductor.org/packages/IHW

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

Non-ribosomal peptide synthetases (NRPSs) are enzymes that catalyze ribosome-independent production of small peptides, most of which are bioactive. NRPSs act as peptide assembly lines where individual, often interconnected modules each incorporate a specific amino acid into the nascent chain. The modules themselves consist of several domains that function in the activation, modification and condensation of the substrate. NRPSs are evidently modular, yet experimental proof of the ability to engineer desired permutations of domains and modules is still sought. Here, we use a synthetic-biology approach to create a small library of engineered NRPSs, in which the domain responsible for carrying the activated amino acid (T domain) is exchanged with natural or synthetic T domains. As a model system, we employ the single-module NRPS IndC from Photorhabdus luminescens that produces the blue pigment indigoidine. As chassis we use Escherichia coli. We demonstrate that heterologous T domain exchange is possible, even for T domains derived from different organisms. Interestingly, substitution of the native T domain with a synthetic one enhanced indigoidine production. Moreover, we show that selection of appropriate inter-domain linker regions is critical for functionality. Taken together, our results extend the engineering avenues for NRPSs, as they point out the possibility of combining domain sequences coming from different pathways, organisms or from conservation criteria. Moreover, our data suggest that NRPSs can be rationally engineered to control the level of production of the corresponding peptides. This could have important implications for industrial and medical applications.

Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify through TGFβ-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.

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Hypothesis weighting is a powerful approach for improving the power of data analyses that employ multiple testing. However, in general it is not evident how to choose the weights. We describe IHW, a method for data-driven hypothesis weighting that makes use of informative covariates that are independent of the test statistic under the null, but informative of each test's power or prior probability of the null hypothesis. Covariates can be continuous or categorical and need not fulfill any particular assumptions. The method increases statistical power in applications while controlling the false discovery rate (FDR) and produces additional insight by revealing the covariate-weight relationship. Independent hypothesis weighting is a practical approach to discovery of associations in large datasets.

In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. Here, we develop confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands. Our intervals include an honest assessment of bias even in situations where empirical Bayes point estimates may converge very slowly. Applied to multiple testing situations, our approach provides flexible and practical confidence statements about the local false sign rate.