Association Testing Strategy for Data from Dense Marker Panels

Genome wide association studies have been usually analyzed in a univariate manner. The commonly used univariate tests have one degree of freedom and assume an additive mode of inheritance. The experiment-wise significance of these univariate statistics is obtained by adjusting for multiple testing. Next generation sequencing studies, which assay 10-20 million variants, are beginning to come online. For these studies, the strategy of additive univariate testing and multiple testing adjustment is likely to result in a loss of power due to (1) the substantial multiple testing burden and (2) the possibility of a non-additive causal mode of inheritance. To reduce the power loss we propose: a new method (1) to summarize in a single statistic the strength of the association signals coming from all not-very-rare variants in a linkage disequilibrium block and (2) to incorporate, in any linkage disequilibrium block statistic, the strength of the association signals under multiple modes of inheritance. The proposed linkage disequilibrium block test consists of the sum of squares of nominally significant univariate statistics. We compare the performance of this method to the performance of existing linkage disequilibrium block/gene-based methods. Simulations show that (1) extending methods to combine testing for multiple modes of inheritance leads to substantial power gains, especially for a recessive mode of inheritance, and (2) the proposed method has a good overall performance. Based on simulation results, we provide practical advice on choosing suitable methods for applied analyses

V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.

SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including \u27unwanted\u27 variation that needs to be removed in downstream analyses (e.g. batch effects) and \u27wanted\u27 or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying \u27wanted\u27 variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. AVAILABILITY AND IMPLEMENTATION: The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA).

Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data

Bio-inspired dewetted surfaces based on SiC/Si interlocked structures for enhanced-underwater stability and regenerative-drag reduction capability

Drag reduction has become a serious issue in recent years in terms of energy conservation and environmental protection. Among diverse approaches for drag reduction, superhydrophobic surfaces have been mainly researched due to their high drag reducing efficiency. However, due to limited lifetime of plastron (i.e., air pockets) on superhydrophobic surfaces in underwater, the instability of dewetted surfaces has been a sticking point for practical applications. This work presents a breakthrough in improving the underwater stability of superhydrophobic surfaces by optimizing nanoscale surface structures using SiC/Si interlocked structures. These structures have an unequaled stability of underwater superhydrophobicity and enhance drag reduction capabilities, with a lifetime of plastron over 18 days and maximum velocity reduction ratio of 56%. Furthermore, through photoelectrochemical water splitting on a hierarchical SiC/Si nanostructure surface, the limited lifetime problem of air pockets was overcome by refilling the escaping gas layer, which also provides continuous drag reduction effects.119Ysciescopu

Methods to investigate the structure and connectivity of the nervous system

Understanding the computations that take place in neural circuits requires identifying how neurons in those circuits are connected to one another. In addition, recent research indicates that aberrant neuronal wiring may be the cause of several neurodevelopmental disorders, further emphasizing the importance of identifying the wiring diagrams of brain circuits. To address this issue, several new approaches have been recently developed. In this review, we describe several methods that are currently available to investigate the structure and connectivity of the brain, and discuss their strengths and limitations

Exact condition on the Kohn-Sham kinetic energy, and modern parametrization of the Thomas-Fermi density

We study the asymptotic expansion of the neutral-atom energy as the atomic number Z goes to infinity, presenting a new method to extract the coefficients from oscillating numerical data. We find that recovery of the correct expansion is an exact condition on the Kohn-Sham kinetic energy that is important for the accuracy of approximate kinetic energy functionals for atoms, molecules and solids, when evaluated on a Kohn-Sham density. For example, this determines the small gradient limit of any generalized gradient approximation, and conflicts somewhat with the standard gradient expansion. Tests are performed on atoms, molecules, and jellium clusters. We also give a modern, highly accurate parametrization of the Thomas-Fermi density of neutral atoms.Comment: 10 pages, 9 figures, submitted at JC

Electronic structure via potential functional approximations

The universal functional of Hohenberg-Kohn is given as a coupling-constant integral over the density as a functional of the potential. Conditions are derived under which potential-functional approximations are variational. Construction via this method and imposition of these conditions are shown to greatly improve the accuracy of the non-interacting kinetic energy needed for orbital-free Kohn-Sham calculations