Mango: Exploratory Data Analysis for Large-Scale Sequencing Datasets
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
SEARCH · Search NASA
Results for “Exploratory Visual Analysis”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Retinal Image Quality Assessment for Spaceflight-Induced Vision Impairment Study
Long-term exposure to space microgravity poses significant risks for visual impairment. Evidence suggests such vision changes are linked to cephalad fluid shifts, prompting a need to directly quantify microgravity-induced retinal vascular changes. The quality of retinal images used for such vascular remodeling analysis, however, is dependent on imaging methodology. For our exploratory study, we hypothesized that retinal images captured using fluorescein imaging methodologies would be of higher quality in comparison to images captured without fluorescein. A semi-automated image quality assessment was developed using Vessel Generation Analysis (VESGEN) software and MATLAB® image analysis toolboxes. An analysis of ten images found that the fluorescein imaging modality provided a 36% increase in overall image quality (two-tailed p=0.089) in comparison to nonfluorescein imaging techniques.
retina↗
SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces
Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.
Krause, Josua↗
FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data
The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).
47 OTHER INSTRUMENTATION↗
Exploratory Analysis of Carbon Dioxide Levels, Ultrasound and Optical Coherence Tomography Measures of the Eye During ISS Missions
Carbon dioxide (CO2) levels on board the International Space Station (ISS) have typically averaged 2.3 to 5.3 mmHg, with large fluctuations occurring over periods of hours and days. CO2 has effects on cerebral vascular tone, resulting in vasodilation and alteration of cerebral blood flow (CBF). Increased CBF leads to elevated intracranial pressure (ICP), a factor leading to visual disturbances, headaches, and other central nervous system symptoms. Ultrasound of the optic nerve and optical coherence tomography (OCT) provide surrogate measurements of ICP; in-flight measurements of both were implemented as enhanced screening tools for the Visual Impairment/Intracranial Pressure (VIIP) syndrome. This analysis examines the relationships between ambient CO2 levels on ISS, ultrasound and OCT measures of the eye in an effort to understand how CO2 may possibly be associated with VIIP and to inform future analysis of in-flight VIIP data.
Schaefer, C.↗
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.
Matchev, Konstantin T. (ORCID:0000000341829096)↗
Multi-modal virtual environment research at Armstrong Laboratory
One mission of the Paul M. Fitts Human Engineering Division of Armstrong Laboratory is to improve the user interface for complex systems through user-centered exploratory development and research activities. In support of this goal, many current projects attempt to advance and exploit user-interface concepts made possible by virtual reality (VR) technologies. Virtual environments may be used as a general purpose interface medium, an alternative display/control method, a data visualization and analysis tool, or a graphically based performance assessment tool. An overview is given of research projects within the division on prototype interface hardware/software development, integrated interface concept development, interface design and evaluation tool development, and user and mission performance evaluation tool development.
Eggleston, Robert G.↗
Hypergraph Analytics of Domain Name System Relationships
We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.
Joslyn, Cliff A.↗
Exploratory geological analysis of AVIRIS data
The size and complexity of AVIRIS data sets makes analysis difficult. We present a technique that allows the researcher to rapidly scan the full data set in image form in order to empirically find significant spatial patterns anywhere across the spectrum. Our approach takes advantage of the speed and power of computer graphics workstations to allow real-time, interactive data manipulation and display. Empirical data selection avoids the bias inherent in theoretical selection criteria, and allows discovery of unexpected relationships within the data set. Link Winds, a prototype data analysis system under development, provides the visualization tools used in this study. We are applying this approach to geological studies in the eastern Mojave Desert.
Beratan, K. K.↗
Experimenter's Laboratory for Visualized Interactive Science
ELVIS (Experimenter's Laboratory for Visualized Interactive Science) is an interactive visualization environment that enables scientists, students, and educators to visualize and analyze large, complex, and diverse sets of scientific data. It accomplishes this by presenting the data sets as 2-D, 3-D, color, stereo, and graphic images with movable and multiple light sources combined with displays of solid-surface, contours, wire-frame, and transparency. By simultaneously rendering diverse data sets acquired from multiple sources, formats, and resolutions and by interacting with the data through an intuitive, direct-manipulation interface, ELVIS provides an interactive and responsive environment for exploratory data analysis.
Hansen, Elaine R.↗
Some effects of Field Of View (FOV) and target size on lateral tracking at hover
An exploratory flight-simulator experiment examined the gross effects of several factors potentially important to the design of a visual display system for aiding VTOL pilots in the difficult task of landing on a small sea-control ship. Field of view (FOV) and target size were the primary variables examined for a lateral tracking task in a full motion 5 degree-of-freedom hover simulation. The mean absolute value of tracking error was used to measure tracking performance, and cross spectral transfer function analysis was performed to determine the pilot's ability to generate good open-loop transfer function characteristics as a function of the experimental variables. It was found that FOV and target size can have a large effect on the pilot's ability to generate open-loop gain, and on his tracking performance.
Breul, H. T.↗
Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.
36 MATERIALS SCIENCE↗
Exploratory flow visualization investigation of mast-mounted sights in presence of a rotor
A flow visualization investigation with a laser light sheet system was conducted on a 27-percent-scale AH-64 attack helicopter model fitted with two mast-mounted sights in the langley 14- by 22-foot subsonic tunnel. The investigation was conducted to identify aerodynamic phenomena that may have contributed to adverse vibration encountered during full-scale flight of the AH-64D apache/longbow helicopter with an asymmetric mast-mounted sight. Symmetric and asymmetric mast-mounted sights oriented at several skew angles were tested at simulated forward and rearward flight speeds of 30 and 45 knots. A laser light sheet system was used to visualize the flow in planes parallel to and perpendicular to the free-stream flow. Analysis of these flow visualization data identified frequencies of flow patterns in the wake shed from the sight, the streamline angle at the sight, and the location where the shed wake crossed the rotor plane. Differences in wake structure were observed between the sight configurations and various skew angles. Analysis of lateral light sheet plane data implied significant vortex structure in the wake of the asymmetric mast-mounted sight in the configuration that produced maximum in-flight vibration. The data showed no significant vortex structure in the wake of the asymmetric and symmetric configurations that produced no increase in in-flight adverse vibration.
Ghee, Terence A.↗
LOCAD-PTS: Operation of a New System for Microbial Monitoring Aboard the International Space Station (ISS)
Microorganisms within the space stations Salyut, Mir and the International Space Station (ISS), have traditionally been monitored with culture-based techniques. These techniques involve growing environmental samples (cabin water, air or surfaces) on agar-type media for several days, followed by visualization of resulting colonies; and return of samples to Earth for ground-based analysis. This approach has provided a wealth of useful data and enhanced our understanding of the microbial ecology within space stations. However, the approach is also limited by the following: i) More than 95% microorganisms in the environment cannot grow on conventional growth media; ii) Significant time lags occur between onboard sampling and colony visualization (3-5 days) and ground-based analysis (as long as several months); iii) Colonies are often difficult to visualize due to condensation within contact slide media plates; and iv) Techniques involve growth of potentially harmful microorganisms, which must then be disposed of safely. This report describes the operation of a new culture-independent technique onboard the ISS for rapid analysis (within minutes) of endotoxin and -1, 3-glucan, found in the cell walls of gram-negative bacteria and fungi, respectively. This technique involves analysis of environmental samples with the Limulus Amebocyte Lysate (LAL) assay in a handheld device. This handheld device and sampling system is known as the Lab-On-a-Chip Application Development Portable Test System (LOCAD-PTS). A poster will be presented that describes a comparative study between LOCAD-PTS analysis and existing culture-based methods onboard the ISS; together with an exploratory survey of surface endotoxin throughout the ISS. It is concluded that while a general correlation between LOCAD-PTS and traditional culture-based methods should not necessarily be expected, a combinatorial approach can be adopted where both sets of data are used together to generate a more complete story of the microbial ecology on the ISS.
Maule, J.↗
Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms
Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures
Storrie-Lombardi, Michael C.↗
AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Rapid Monitoring of Bacteria and Fungi aboard the International Space Station (ISS)
Microorganisms within spacecraft have traditionally been monitored with culture-based techniques. These techniques involve growth of environmental samples (cabin water, air or surfaces) on agar-type media for several days, followed by visualization of resulting colonies or return of samples to Earth for ground-based analysis. Data obtained over the past 4 decades have enhanced our understanding of the microbial ecology within space stations. However, the approach has been limited by the following factors: i) Many microorganisms (estimated > 95%) in the environment cannot grow on conventional growth media; ii) Significant time lags (3-5 days for incubation and up to several months to return samples to ground); iii) Condensation in contact slides hinders colony counting by crew; and iv) Growth of potentially harmful microorganisms, which must then be disposed of safely. This report describes the operation of a new culture-independent technique onboard the ISS for rapid analysis (within minutes) of endotoxin and beta-1, 3-glucan, found in the cell walls of gramnegative bacteria and fungi, respectively. The technique involves analysis of environmental samples with the Limulus Amebocyte Lysate (LAL) assay in a handheld device, known as the Lab-On-a-Chip Application Development Portable Test System (LOCAD-PTS). LOCADPTS was launched to the ISS in December 2006, and here we present data obtained from Mach 2007 until the present day. These data include a comparative study between LOCADPTS analysis and existing culture-based methods; and an exploratory survey of surface endotoxin and beta-1, 3-glucan throughout the ISS. While a general correlation between LOCAD-PTS and traditional culture-based methods should not be expected, we will suggest new requirements for microbial monitoring based upon culture-independent parameters measured by LOCAD-PTS.
Gunter, D.↗
Improve Data Mining and Knowledge Discovery Through the Use of MatLab
Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.
Shaykhian, Gholam Ali↗