Development Of Data Analysis Tools Research Articles

Bayesian-inspired minimum contamination designs under a double-pair conditional effect model

In two-level fractional factorial designs, conditional main effects can provide insights by which to analyze factorial effects and facilitate the de-aliasing of fully aliased two-factor interactions. Conditional main effects are of particular interest in situations where some factors are nested within others. Most of the relevant literature has focused on the development of data analysis tools that use conditional main effects, while the issue of optimal factorial design for a given linear model involving conditional main effects has been largely overlooked. Mukerjee, Wu and Chang [Statist. Sinica 27 (2017) 997–1016] established a framework by which to optimize designs under a conditional effect model. Although theoretically sound, their results were limited to a single pair of conditional and conditioning factors. In this paper, we extend the applicability of their framework to double pairs of conditional and conditioning factors by providing the corresponding parameterization and effect hierarchy. We propose a minimum contamination-based criterion by which to evaluate designs and develop a complementary set theory to facilitate the search of minimum contamination designs. The catalogues of 16- and 32-run minimum contamination designs are provided. For five to twelve factors, we show that all 16-run minimum contamination designs under the conditional effect model are also minimum aberration according to Fries and Hunter [Technometrics 22 (1980) 601–608].

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Progress in kidney transplantation: The role for systems immunology.

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

Instantaneous Current Signal Analysis Based on Z-Transform to Detect Oscillation Modes

In power systems, phasor measurement units of current, voltage, power, and frequency have traditionally allowed the capture of electromechanical oscillations and inspired the development of data analysis tools. These oscillations can be described by oscillatory modes, which provide security and stability. In this study, a technique is proposed to detect and identify oscillation modes quickly from instantaneous current signals. The proposed method is based on the Z-transform of the analyzed signal. Simulated data resulting from power systems previously used to analyze the generated oscillations between buses were obtained to demonstrate the effectiveness of the proposed technique. The accuracy of this methodology was compared with that of Prony analysis and the eigensystem realization algorithm. The results show that the oscillatory modes are characterized more rapidly; therefore, the proposed method is a promising tool for oscillation monitoring.

Third-generation sequencing data analytics on mobile devices: cache oblivious and out-of-core approaches as a proof-of-concept

Mobile (third-generation) sequencing technologies, including Oxford Nanopore’s MinION and SmidgION, have the benefit of outputting long sequence reads (up to hundred thousands of bases) in a portable manner. These sequencing devices fit in the palm of a hand and only require a USB outlet. Unfortunately, the development of data analysis tools for these technologies is in a nascent stage, impeding on the portability of these devices. The objective of this work is to introduce an out-of-core approach to port Nanopore analytics on mobile devices such as tablets or smartphones, often used in extreme experimental settings with special ergonomics needs and ease of sterilization. In this paper, we present a serial k-mer parser/counter for FAST5 files, and a de Bruijn graph construction method which can run on a hand-held device. In order to accomplish this portability we develop novel cache oblivious data structures and out-of-core chunked processing methods. Our toolset, which we refer to as Nanopore Portable Analytics Library (NanoPAL), wase implemented in ISO C++ v.14 and compiled for Android devices. Using MinION data (Zaire Ebolavirus species and others), we evaluate the time required to parse and build the de Bruijn graph with respect to the file sizes and RAM allocation. These metrics were compared to those of minimap/miniasm. On an LG Nexus 5 with 2GB or RAM, 2MB L2 cache and 16GB storage, the out-of-core NanoPAL is able to process FAST5 files at about 30 minutes per 0.5 GB, creating sorted k-mer and de Bruijn graph files. The recompiled minimap/miniasm tool cannot complete FAST5 files larger than 170MB. In conjunction with base calling/error correction, and with addition of assembly procedures downstream, NanoPAL can be effectively used to perform analyses of MinION/SmidgION data locally on a mobile device.

Operating mode recognition. Application to a grinding mill process.

Abstract: Process monitoring needs the development of data analysis tools aiming at recognizing, at each time instant, system operating mode using the measurement collected on the system. This communication aims at presenting a method relying on measurement analysis, able to identify operating modes without the knowledge of the mathematical models describing these modes. The proposed method relies on the writing of a global model combining, in a multiplicative way, the models describing the different modes. The parameters of this global model are then numerically identified from the available set of measurements. The sensitivity analysis of the global model with regard the input/output variables then provides an indicator to identify, at each instant, the current operating mode. The proposed method is applied on a simplified model of a grinding mill.

Development of data analysis tool for combat system integration

System integration is an important element for the construction of naval combat ships. In particular, because impeccable combat system integration together with the sensors and weapons can ensure the combat capability and survivability of the ship, the integrated performance of the combat system should be verified and validated whether or not it fulfills the requirements of the end user. In order to conduct systematic verification and validation, a data analysis tool is requisite. This paper suggests the Data Extraction, Recording and Analysis Tool (DERAT) for the data analysis of the integrated performance of the combat system, including the functional definition, architecture and effectiveness of the DERAT by presenting the test results.

Development of data analysis tool for combat system integration

System integration is an important element for the construction of naval combat ships. In particular, because impeccable combat system integration together with the sensors and weapons can ensure the combat capability and survivability of the ship, the integrated performance of the combat system should be verified and validated whether or not it fulfills the requirements of the end user. In order to conduct systematic verification and validation, a data analysis tool is requisite. This paper suggests the Data Extraction, Recording and Analysis Tool (DERAT) for the data analysis of the integrated performance of the combat system, including the functional definition, architecture and effectiveness of the DERAT by presenting the test results.

Quantitative Bayesian predictions of source water concentration for QMRA from presence/absence data for E. coli O157:H7

A hierarchical Bayesian framework was applied for describing variability in pathogen concentration (with associated uncertainty) from presence/absence observations for E. coli O157:H7. Laboratory spiking experiments (method performance) and environmental sample assays were undertaken for a surface drinking water source in France. The concentration estimates were strongly dependent upon the assumed statistical model used (gamma, log-gamma or log-gamma constrained), highlighting the need for a solid theoretical basis for model choice. Bayesian methods facilitate the incorporation of additional data into the statistical analysis; this was illustrated using faecal indicator results of E. coli (Colilert) to reduce the posterior parameter uncertainty and improve model stability. While conceptually simple, application of these methods is still specialised, hence there is a need for the development of data analysis tools to make Bayesian simulation techniques more accessible for QMRA practitioners.

Generalized Multiresolution Decomposition Frameworks for the Analysis of Industrial Data with Uncertainty and Missing Values

In this paper, we address the problem of extending the multiscale decomposition framework based upon the wavelet transform to situations where datasets contain any type of missing data patterns (e.g., random, multirate). Furthermore, the proposed approaches explicitly integrate data uncertainty information into their algorithms, to explore all knowledge available about data during the decomposition stage, as well as to enable its posterior use in subsequent data-analysis tasks. Beside their role in the multiscale decomposition of complex datasets, these frameworks, called generalized multiresolution decomposition frameworks (GMRD), also lead to new developments in data-analysis tools based upon the information they provide, namely, in the selection of relevant scales for data analysis and in the improvement of signal-denoising procedures. Guidelines are presented regarding their adequate use and how they can be combined into an integrated multiscale data analysis framework. The pertinence of the proposed GMRD frameworks is illustrated through several examples involving simulated, laboratory, and industrial datasets.

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

Educational affairs

The EARTHSTORM Project is a K–12 teacher enhancement program for Oklahoma science and mathematics teachers who desire to incorporate real-time environmental data into their classroom activities. EARTHSTORM provides a model for the professional development of teachers, daily staff support for participants, scientific mentorship of students, development of data analysis tools for students and teachers, and use of real-time data in the K–12 classroom.