PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease

Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer’s disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis

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A new insight into underlying disease mechanism through semi-parametric latent differential network model

Background In genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types. Results We propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness. Conclusions The proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended

Phase evolution and superconductivity enhancement in Se-substituted MoTe2_2 thin films

The strong spin$-$orbit coupling (SOC) and numerous crystal phases in few$-$layer transition metal dichalcogenides (TMDCs) MX$_2$ (M$=$W, Mo, and X$=$Te, Se, S) has led to a variety of novel physics, such as Ising superconductivity and quantum spin Hall effect realized in monolayer 2H$-$ and Td$-$MX$_2$, respectively. Consecutive tailoring of the MX$_2$ structure from 2H to Td phase may realize the long$-$sought topological superconductivity in one material system by incorporating superconductivity and quantum spin Hall effect together. In this work, by combing Raman spectrum, X-ray photoelectron spectrum (XPS), scanning transmission electron microscopy imaging (STEM) as well as electrical transport measurements, we demonstrate that a consecutively structural phase transitions from Td to 1T$'$ to 2H polytype can be realized as the Se-substitution concentration increases. More importantly, the Se$-$substitution has been found to notably enhance the superconductivity of the MoTe$_2$ thin film, which is interpreted as the introduction of the two$-$band superconductivity. The chemical constituent induced phase transition offers a new strategy to study the s$_{+-}$ superconductivity and the possible topological superconductivity as well as to develop phase$-$sensitive devices based on MX$_2$ materials.Comment: 27 pages, 5 figure

Transport evidence of asymmetric spin-orbit coupling in few−-layer superconducting 1Td−-MoTe2_2

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) MX2 (M=W, Mo, Nb, and X=Te, Se, S) with strong spin-orbit coupling (SOC) possess plenty of novel physics including superconductivity. Due to the Ising SOC, monolayer NbSe$_2$ and gated MoS$_2$ of 2H structure can realize the Ising superconductivity phase, which manifests itself with in-plane upper critical field far exceeding Pauli paramagnetic limit. Surprisingly, we find that a few-layer 1Td structure MoTe$_2$ also exhibits an in-plane upper critical field ($H_{c2,//}$) which goes beyond the Pauli paramagnetic limit. Importantly, the in-plane upper critical field shows an emergent two-fold symmetry which is different from the isotropic $H_{c2,//}$ in 2H structure TMDCs. We show that this is a result of an asymmetric SOC in 1Td structure TMDCs. The asymmetric SOC is very strong and estimated to be on the order of tens of meV. Our work provides the first transport evidence of a new type of asymmetric SOC in TMDCs which may give rise to novel superconducting and spin transport properties. Moreover, our findings mostly depend on the symmetry of the crystal and apply to a whole class of 1Td TMDCs such as 1Td-WTe$_2$ which is under intense study due to its topological properties.Comment: 34 pages, 12 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data