. Exploration and Analysis of DNA Microarray andOther High-Dimensional Data, Second Edition is also a usefultext for graduate-level courses on statistics, computationalbiology, and bioinformatics. High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods. This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high-and infinite-dimensional statistics, inference on functional data,

Phase change memory (PCM) is an emerging non‐volatile data storage technology concerned by the semiconductor industry. To improve the performances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge‐Sb‐Te (GST) alloy based on experimental “trial‐and‐error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high‐throughput screening strategy consisting of criteria related to PCM characteristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge2Bi2Te5, GeAs4Te7, GeAs2Te4, so on and other candidates that have not been reported, such as TlBiTe2, TlSbTe2, CdPb3Se4, etc. Compared with GST, materials with close cohesive energy include AgBiTe2, TlSbTe2, As2Te3, TlBiTe2, etc., indicating possible low power consumption. Through further melt‐quenching molecular dynamic calculation and structural/electronic analyses, Ge2Bi2Te5, CdPb3Se4, MnBi2Te4, and TlBiTe2 are found suitable for optical/electrical PCM applications, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big‐data applications.

High-throughput screening of drugs with human induced pluripotent stem cell–derived cardiomyocytes reveals a “cardiac safety index.” Failing fast for tyrosine kinase inhibitors Discovery early in its life cycle that an anticancer drug causes heart damage (a common side effect) can halt development—saving money, time, and perhaps lives. To this end, Sharma and colleagues derived heart cells from human induced pluripotent stem cells and then examined how a battery of anticancer tyrosine kinase inhibitors altered their physiology. By measuring cell death, contraction, excitability, calcium dynamics, and signal transduction and integrating the results, they calculated a drug-specific “cardiac safety index.” This index proved highly informative, with low values corresponding to those drugs known to cause heart problems in patients. The analysis even revealed that VEGFR2-inhibiting drugs caused cells to try to compensate for the toxic effects by up-regulating protective insulin/IGF pathways, prompting the authors to devise a combination treatment that may limit the toxicity of this class of drug. This screening method is expected to reveal early on whether potential anticancer drugs are cardiotoxic. Tyrosine kinase inhibitors (TKIs), despite their efficacy as anticancer therapeutics, are associated with cardiovascular side effects ranging from induced arrhythmias to heart failure. We used human induced pluripotent stem cell–derived cardiomyocytes (hiPSC-CMs), generated from 11 healthy individuals and 2 patients receiving cancer treatment, to screen U.S. Food and Drug Administration–approved TKIs for cardiotoxicities by measuring alterations in cardiomyocyte viability, contractility, electrophysiology, calcium handling, and signaling. With these data, we generated a “cardiac safety index” to reflect the cardiotoxicities of existing TKIs. TKIs with low cardiac safety indices exhibit cardiotoxicity in patients. We also derived endothelial cells (hiPSC-ECs) and cardiac fibroblasts (hiPSC-CFs) to examine cell type–specific cardiotoxicities. Using high-throughput screening, we determined that vascular endothelial growth factor receptor 2 (VEGFR2)/platelet-derived growth factor receptor (PDGFR)–inhibiting TKIs caused cardiotoxicity in hiPSC-CMs, hiPSC-ECs, and hiPSC-CFs. With phosphoprotein analysis, we determined that VEGFR2/PDGFR-inhibiting TKIs led to a compensatory increase in cardioprotective insulin and insulin-like growth factor (IGF) signaling in hiPSC-CMs. Up-regulating cardioprotective signaling with exogenous insulin or IGF1 improved hiPSC-CM viability during cotreatment with cardiotoxic VEGFR2/PDGFR-inhibiting TKIs. Thus, hiPSC-CMs can be used to screen for cardiovascular toxicities associated with anticancer TKIs, and the results correlate with clinical phenotypes. This approach provides unexpected insights, as illustrated by our finding that toxicity can be alleviated via cardioprotective insulin/IGF signaling.

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical safety assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In the present study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening (HTS) assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high throughput hazard evaluation of environmental chemicals.

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

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Exascale focuses on the ecosystems surrounding the world s leading centers for high performance computing (HPC). It covers many of the important factors...

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single-cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high-dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high-dimensional data sets using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high-dimensional clustering or dimensionality reduction algorithms such as t-SNE, UMAP, or FlowSOM. However, large scale studies combined with an in-depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry.