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Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

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arXiv 2023年

作者： Jeon, Hyeongseon Xie, Juan Jeon, Yeseul Jung, Kyeong Joo Gupta, Arkobrato Chang, Won Chung, Dongjun Department of Biomedical Informatics The Ohio State University ColumbusOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. © 2023, CC BY-NC-ND.

关键词： RNA

Graph Fourier MMD for Signals on Graphs

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arXiv 2023年

作者： Leone, Samuel Venkat, Aarthi Huguet, Guillaume Tong, Alexander Wolf, Guy Krishnaswamy, Smita Applied Math Program New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Dept. of Math. & Stat Université de Montréal Mila - Quebec AI Institute MontrealQC Canada Dept. of Comp. Sci. & Oper. Res. Université de Montréal Mila - Quebec AI Institute MontrealQC Canada Department of Computer Science Department of Genetics Yale University New HavenCT United States

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization. Copyright © 2023, The Authors. All rights reserved.

关键词： Genes

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Pasquali, Matteo Yomogida, Yohei Yanagi, Kazuhiro Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. LincolnNE United States The Smalley-Curl Institute Rice University HoustonTX United States Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Dichroism

Performance comparison of InGaN-based 40–80 μm micro-LEDs fabricated with and without plasma etching

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Materials Today Advances 2024年 22卷

作者： Lo, Yu-Yun Chen, Yi-Ho Hsu, Yun-Cheng Lee, Tzu-Yi Hung, Yu-Ying Kao, Yu-Cheng Zan, Hsiao-Wen Wuu, Dong- Sing Kuo, Hao-Chung Samukawa, Seiji Horng, Ray-Hua Institute of Electronics National Yang Ming Chiao Tung University 1001 University Road Hsinchu30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

The fabrication of InGaN-based blue 4✕4 array micro-LEDs (μLEDs) with 40 μm ✕40 μm chip size and 2✕2 array μLEDs with 80 μm ✕80 μm chip size etching by the inductive coupled plasma reactive ion etching (ICPRIE) and defect-free neutral beam etching (NBE) processes was studied in this work. In μLEDs of this size, the influence of defects formation in the sidewalls on EQE was evaluated. There was almost no difference in EQE between μLEDs array etched by the NBE process no matter 40 μm ✕40 μm and 80 μm ✕80 μm, but the dependence was observed in the ICPRIE. Even with this size, it was found that the size effect of EQE is smaller than that in case of using ICPRIE for defect-free neutral beam etching. This impact is substantial since μLED predominantly operated at low current density, around 1–5 A/cm2. Consequently, the reduction of defect density, encompassing both internal and sidewall defects, becomes imperative even in 40–80 μm InGaN-based μLEDs. This not only improves the overall efficiency of μLEDs but also fortifies the brightness stability of μLED displays if process etching by NBE. It was also found that the etching shape had an influence on EQE. It could be attributed to fact that the etching profile angle of NBE was more vertical than that of ICPRIE. Because the different angles of the mesa resulted in different light intensity. The μLEDs emitting with a wavelength of 450 nm, the light extraction efficiency and intensity at a mesa angle 58° of NBE etching μLEDs was about 8% lower than those of an angle (38°) of ICPRIE etching μLEDs by simulation. © The Authors

关键词： Light emitting diodes

Scientific Machine Learning Framework to Understand Flash Graphene Synthesis

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arXiv 2023年

作者： Sattari, Kianoosh Eddy, Lucas Beckham, Jacob L. Wyss, Kevin M. Byfield, Richard Qian, Long Tour, James M. Lin, Jian Department of Mechanical and Aerospace Engineering University of Missouri ColumbiaMO65211 United States Department of Chemistry 6100 Main Street HoustonTX77005 United States Applied Physics Program Smalley-Curl Institute 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering 6100 Main Street HoustonTX77005 United States Department of Computer Science and Engineering NanoCarbon Center The Welch Institute for Advanced Materials Rice University 6100 Main Street HoustonTX77005 United States

Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction mechanism have been limited due to complexity involved in the FFE process. Data-driven machine learning (ML) models effectively account for this complexity, but the model training requires considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 ± 0.05 and an average RMSE of 12.1% ± 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 ± 0.05 and an RMSE of 14.3% ± 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

ON THE STABILITY OF POSITIVE SEMIGROUPS

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arXiv 2021年

作者： Del Moral, Pierre Horton, Emma Jasra, Ajay Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 France Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science & Technology Building 1 Thuwal23955-6900 Saudi Arabia

The stability and contraction properties of positive integral semigroups on Polish spaces are investigated. Our novel analysis is based on the extension of V -norm contraction methods, associated to functionally weighted Banach spaces for Markov semigroups, to positive semigroups. This methodology is applied to a general class of positive and possibly time-inhomogeneous bounded integral semigroups and their normalised versions. The spectral theorems that we develop are an extension of Perron-Frobenius and Krein-Rutman theorems for positive operators to a class of time-varying positive semigroups. In the context of time-homogeneous models, the regularity conditions discussed in the present article appear to be necessary and sufficient condition for the existence of leading eigenvalues. We review and illustrate the impact of these results in the context of positive semigroups arising in transport theory, physics, mathematical biology and signal processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Banach spaces

Physics-Informed Neural Nets for Control of Dynamical Systems

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arXiv 2021年

作者： Antonelo, Eric Aislan Camponogara, Eduardo Seman, Laio Oriel de Souza, Eduardo Rehbein Jordanou, Jean Panaioti Hübner, Jomi Fred Department of Automation and Systems Engineering Federal University of Santa Catarina FlorianópolisCEP: 88040-900 Brazil Gradute Program in Applied Computer Science University of Vale do Itajaí Itajaí CEP: 88302-901 Brazil

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs, neither can they simulate for variable long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to control problems and able to simulate for longer-range time horizons that are not fixed beforehand, making it a very flexible framework when compared to traditional PINNs. Furthermore, this long-range time simulation of differential equations is faster than numerical methods since it relies only on signal propagation through the network, making it less computationally costly and, thus, a better alternative for simulation of models in Model Predictive Control. We showcase our proposal in the control of two nonlinear dynamic systems: The Van der Pol oscillator and the four-tank system. Copyright © 2021, The Authors. All rights reserved.

关键词： Ordinary differential equations

Quantum Error Mitigation by Pauli Check Sandwiching

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arXiv 2022年

作者： Gonzales, Alvin Shaydulin, Ruslan Saleem, Zain H. Suchara, Martin Intelligence Community Postdoctoral Research Fellowship Program Argonne National Laboratory LemontIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Global Technology Applied Research JPMorgan Chase New YorkNY United States Amazon Web Services Amazon SeattleWA United States

We describe and analyze an error mitigation technique that uses multiple pairs of parity checks to detect the presence of errors. Each pair of checks uses one ancilla qubit to detect a component of the error operator and represents one layer of the technique. We build on the results on extended flag gadgets and put it on a firm theoretical foundation. We prove that this technique can recover the noiseless state under the assumption of noise not affecting the checks. The method does not incur any encoding overhead and instead chooses the checks based on the input circuit. We provide an algorithm for obtaining such checks for an arbitrary target circuit. Since the method applies to any circuit and input state, it can be easily combined with other error mitigation techniques. We evaluate the performance of the proposed methods using extensive numerical simulations on 1,850 random input circuits composed of Clifford gates and non-Clifford single-qubit rotations, a class of circuits encompassing most commonly considered variational algorithm circuits. We observe average improvements in fidelity of 34 percentage points with six layers of checks. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

Bio-FlatScope: A flat, lensless microscope for fluorescence imaging

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Bio-FlatScope: A flat, lensless microscope for fluorescence ...

Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Wu, Jimin Yan, Dong Boominathan, Vivek Adams, Jesse K. Veeraraghavan, Ashok Robinson, Jacob T. Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Program Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States

We developed a lensless microscope with small, inexpensive form factor, large field-of-view (FOV) and cellular resolution for fluorescence imaging. Proof-of-principle measurements with "Bio-FlatScope" are pr... 详细信息

ISBN: (纸本)9781557528209

关键词： Fluorescence imaging