Saint Petersburg State University of Information Technologies, Mechanics and Optics

About: Saint Petersburg State University of Information Technologies, Mechanics and Optics is a education organization based out in Saint Petersburg, Russia. It is known for research contribution in the topics: Laser & Quantum dot. The organization has 5509 authors who have published 10058 publications receiving 88852 citations.

Fast gene set enrichment analysis

Abstract: Preranked gene set enrichment analysis (GSEA) is a widely used method for interpretation of gene expression data in terms of biological processes. Here we present FGSEA method that is able to estimate arbitrarily low GSEA P-values with a higher accuracy and much faster compared to other implementations. We also present a polynomial algorithm to calculate GSEA P-values exactly, which we use to practically confirm the accuracy of the method.

Fano resonances in photonics

Abstract: The importance of the Fano resonance concept is recognized across multiple fields of physics. In this Review, Fano resonance is explored in the context of optics, with particular emphasis on dielectric nanostructures and metasurfaces. Rapid progress in photonics and nanotechnology brings many examples of resonant optical phenomena associated with the physics of Fano resonances, with applications in optical switching and sensing. For successful design of photonic devices, it is important to gain deep insight into different resonant phenomena and understand their connection. Here, we review a broad range of resonant electromagnetic effects by using two effective coupled oscillators, including the Fano resonance, electromagnetically induced transparency, Kerker and Borrmann effects, and parity–time symmetry breaking. We discuss how to introduce the Fano parameter for describing a transition between two seemingly different spectroscopic signatures associated with asymmetric Fano and symmetric Lorentzian shapes. We also review the recent results on Fano resonances in dielectric nanostructures and metasurfaces.

An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

Abstract: Gene set enrichment analysis is a widely used tool for analyzing gene expression data. However, current implementations are slow due to a large number of required samples for the analysis to have a good statistical power. In this paper we present a novel algorithm, that efficiently reuses one sample multiple times and thus speeds up the analysis. We show that it is possible to make hundreds of thousands permutations in a few minutes, which leads to very accurate p-values. This, in turn, allows applying standard FDR correction procedures, which are more accurate than the ones currently used. The method is implemented in a form of an R package and is freely available at \url{https://github.com/ctlab/fgsea}.