Flexible Data Analysis Research Articles

VCF observer: a user-friendly software tool for preliminary VCF file analysis and comparison

BackgroundAdvancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files.ResultsIn response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application’s interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision–recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists.ConclusionsBy providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries.

A Conceptual Framework for Immersive Acoustic Auralisation: Investigating the Key Attributes

In architectural acoustics, the assessment of sound propagation in interior and/or environmental conditions has now become progressively more prominent throughout the past few decades, as a response to the development of advanced prediction tools. Within the adaptation of virtual reality (VR) systems, it is necessary to considerably expedite the prediction and simulation software as well as to enable flexible and responsive data analysis during simulation and 3D audio sensory projection. To generate ideal immersion in a simulated virtual environment, the generated stimulus across all senses should therefore be coherent. Accordingly, in the domain of acoustic in virtual reality, the system sound simulation must be constructed efficiently in order to convey the auditory stimuli to the user in an appropriate manner. This necessitates the implementation of virtual reality system as an advanced prediction tool that can accurately anticipate and replicate realistic audio experiences. Therefore, this study explores the realm of acoustic virtual reality (AVR) through a critical review with the purpose of elucidating design attributes and determining factors in generating immersive acoustic VR experiences. In light of these findings, the aim of this paper is to develop a comprehensive conceptual framework that will serve as a beneficial guide and road map for future researchers and developers in the field.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

ИНТЕЛЛЕКТУАЛЬНАЯ БЕСПРОВОДНАЯ СЕНСОРНАЯ СЕТЬ С ФУНКЦИЕЙ ПОТОКОВОГО РАСПОЗНАВАНИЯ МНОГОЧАСТОТНЫХ СИМВОЛОВ

Intelligent wireless sensor networks are an innovative class of technologies that combine wireless communications and sensor information systems to create dynamic and flexible data collection and analysis systems. The article proposes a variant of the method for stream recognition of multi-frequency symbols generated by a group of information sources with the possibility of significantly improving the energy and information indicators of the data transmission channel.

Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package.

Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.

"Nano COVID-19": Nanopore sequencing of spike gene to identify SARS-CoV-2 variants of concern.

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). World Health Organization (WHO) has defined the viral variants of concern (VOC) which cause more severe disease, higher transmissibility, and reduced vaccine efficacy. In this study, the "Nano COVID-19" workflow based on Oxford nanopore sequencing of the full-length spike gene combined with flexible data analysis options was developed to identify SARS-CoV-2 VOCs. The primers were designed to cover the full-length spike gene and can amplify all VOC strains. The results of VOC identification based on phylogenetic analysis of the full-length spike gene were comparable to the whole genome sequencing (WGS). Compared to the standard VOC identification pipeline, the fast analysis based on Read Assignment, Mapping, and Phylogenetic Analysis in Real Time (RAMPART) and the user-friendly method based on EPI2ME yielded 89.3% and 97.3% accuracy, respectively. The EPI2ME pipeline is recommended for researchers without bioinformatic skills, whereas RAMPART is more suitable for bioinformaticians. This workflow provides a cost-effective, simplified pipeline with a rapid turnaround time. Furthermore, it is portable to point-of-care SARS-CoV-2 VOC identification and compatible with large-scale analysis. Therefore, "Nano COVID-19" is an alternative viral epidemic screening and transmission tracking workflow.

Interpretation and Classification of Phonocardiogram Using Principal Component Analysis

Background: Large datasets are logically common yet frequently difficult to interpret. Principal Component Analysis (PCA) is a technique to reduce the dimensionality of a dataset. Objective: The main objective of this work is to use principal component analysis to interpret and classify phonocardiogram signals. Methods: Finding new factors aids in the reduction of important components of an eigenvalue/ eigenvector problem, thus enabling the new factors to be represented by the current dataset and making PCA a flexible data analysis tool. PCA is adaptable to a variety of systems created to update different data types and technology advancements. Results: Signals acquired from a patient, i.e., bio-signals, are used to investigate the patient's strength. One such bio-signal of central significance is the phonocardiogram (PCG), which addresses the working of the heart. Any change in the PCG signal is a characteristic proportion of heart failure, an arrhythmia condition. Conclusion: Long-term observation is difficult due to the many complexities, such as the lack of human competence and the high chance of misdiagnosis.

LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis

BackgroundAmplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences.ResultsIn LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios.We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences.ConclusionLotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes.AvailabilityLotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.6hTcPpJ8wvT9bQx45qQZLRVideo

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr65Cu17.5Ni10Al7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

Flexible force-sensitive distributed pressure sensing system for soft connection of aircraft Cockpit Cover

With respect to the actual application of the edge bonding of transparent part of the fighter jet cockpit cover by soft connection, the flexible force-sensitive distributed pressure sensor system is developed, which covers the flexible force-sensitive sensor, calibration device, circuit and data analysis software. The pressure distribution during pressurizing of the edge bonding of multidimensional curved surface transparent parts is vividly showcased by 2D and 3D images. A number of people are assigned to carry out operation. Based on the pressure distribution map and data acquisition, it is determined that the existing bonding process has the serious decificiency of uneven pressure distributio that cannot be addressed, and there is huge potential for improvement.

Riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow.

MotivationRibosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses.ResultsWe present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system for end-to-end processing of Ribo-seq data. riboviz 2 has been extensively tested on diverse species and library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline.Availability and implementationriboviz 2 is freely available at github.com/riboviz/riboviz.

Registration and primary outcome reporting in behavioral health trials

BackgroundRegistration of research studies is designed to lock investigators into a data collection and analysis plan before a study starts and thereby limit their ability to engage in flexible data analysis and selective outcome reporting. Studies of registered clinical trials show that one- to two-thirds are registered after the study has started and that non-adherence to important design and analytic features, such as reporting data pertaining to all primary outcomes, remains high. Less is known about the effects of registration on research transparency and integrity outside of clinical trials. To address this gap in knowledge, the current study examined the effects of registration on the reporting of research findings in a sample of behavioral health trials published in BMC Public Health.MethodsRegistered trials published in the BMC Public Health section “Health Behavior, Health Promotion and Society” between 2011 and 2015 were included in the study. For each trial, we reviewed associated online submissions from 13 different registration sites. For those determined to have been prospectively registered, we used the trial registry, MEDLINE (Pubmed), PsychINFO, Web of Science and e-mails to investigators to identify subsequent publications from the study that reported results pertaining to primary outcomes. The two investigators then independently reviewed the outcome publication(s) and compared the primary outcomes reported in these to the registered primary outcomes.ResultsThe final analytic sample comprised 136 locatable, registered trials with an identifiable start date. Sixty-eight of the 136 were prospectively registered. Among these prospectively registered trials, only 16 published manuscripts reported outcomes and methods that were concordant with their registrations.ConclusionsRetrospective submission of protocols for publication and retrospective registration remain common in public health research, and adherence to prespecified outcomes is rare. In its current form, registration of behavioral and health promotion trials is likely to have minimal effect on preventing selective outcome reporting in publications, and the pervasiveness of vague and incomplete registry entries means that registries will have limited utility in terms of facilitating replication studies.

Exploring the Personal Informatics Analysis Gap: "There's a Lot of Bacon".

Personal informatics research helps people track personal data for the purposes of self-reflection and gaining self-knowledge. This field, however, has predominantly focused on the data collection and insight-generation elements of self-tracking, with less attention paid to flexible data analysis. As a result, this inattention has led to inflexible analytic pipelines that do not reflect or support the diverse ways people want to engage with their data. This paper contributes a review of personal informatics and visualization research literature to expose a gap in our knowledge for designing flexible tools that assist people engaging with and analyzing personal data in personal contexts, what we call the personal informatics analysis gap. We explore this gap through a multistage longitudinal study on how asthmatics engage with personal air quality data, and we report how participants: were motivated by broad and diverse goals; exhibited patterns in the way they explored their data; engaged with their data in playful ways; discovered new insights through serendipitous exploration; and were reluctant to use analysis tools on their own. These results present new opportunities for visual analysis research and suggest the need for fundamental shifts in how and what we design when supporting personal data analysis.

The Brain Electrophysiological recording & STimulation (BEST) toolbox

Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The Brain Electrophysiological recording and STimulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimulation (TMS, tES, TUS). Interfacing with a variety of recording and stimulation devices, the BEST toolbox analyzes EMG and EEG data, and configures stimulation parameters on-the-fly to facilitate closed-loop protocols and real-time applications. Its functionality is continuously expanded and includes e.g., TMS motor hotspot search, threshold estimation, motor evoked potential (MEP) and TMS-evoked EEG potential (TEP) measurements, dose-response curves, paired-pulse and dual-coil TMS, rTMS interventions.

Multimodal multisample spectroscopic imaging analysis of tumor tissues using multivariate curve resolution

Chemical imaging aims to characterize the molecular patterns present in a tissue or surface. High-resolution images with high content of molecular information represent a clear advantage in many research areas, such as in biomedicine. Specific molecular patterns, when related to pathological traits, could help understanding the molecular events related to the condition studied. Chemical composition of the different regions within the tissue may reveal interesting patterns that cannot be detected using conventional staining techniques. In this work, a new chemometrics approach is proposed for the simultaneous multimodal chemical imaging analysis of six tissue slices from two types of tumor tissues (EG and 1070) exhibiting different sensitivity to the docetaxel drug. Therefore, a deeper chemical imaging study of these tissues could reveal new chemical fingerprints related to the different susceptibility and response to this drug treatment. The spectral imaging techniques used in this study were MALDI-TOF mass spectrometry imaging (MSI) and FT-IR hyperspectral imaging. In addition, a RGB color picture of the hematoxylin/eosin staining was also included in the joint image analysis. The huge spectral data information generated by these multiple imaging techniques in the simultaneous analysis of the different tissue samples was organized in combined multiset and multimodal spectral image data structures which were then analyzed by multivariate curve resolution. Using this approach, the nature of the chemical constituents and their high-resolution spatial distribution were detangled showing what were the main chemical patterns present in specific areas of the tissues which distinguished the two types of tumors. In summary, in the present work, the application of multivariate curve resolution is confirmed to be a powerful and flexible data analysis tool able to extract chemical information from the simultaneous analysis of huge, fused image data sets from obtained by different type of spectroscopic imaging techniques having different spatial resolution powers.

Time sequence method for prompt neutron decay constant determination

Aiming at measuring the prompt neutron decay constant α of fast critical assemblies, Rossi-α method was improved by means of the acquisition of neutron and gamma time sequence. The application of the time sequence method in delayed critical & subcritical experiments at a fast critical assembly was here in described, focus in particular on the gamma analysis and the exploration of A/C ratio which directly affects the measurement precision of α. The reliability of the time sequence method is proved by the agreement of the α values obtained based on neutron and gamma. The A/C ratio based on neutron and gamma shows an obvious difference and the corresponding conclusion guides to a larger A/C ratio. The time sequence method shows advantages of higher experimental efficiency and flexible data analysis which contribute to more accurate experimental result.

Analysis of ‘Pre-Fit’ Datasets of gLAB by Robust Statistical Techniques

The GNSS LABoratory tool (gLAB) is an interactive educational suite of applications for processing data from the Global Navigation Satellite System (GNSS). gLAB is composed of several data analysis modules that compute the solution of the problem of determining a position by means of GNSS measurements. The present work aimed to improve the pre-fit outlier detection function of gLAB since outliers, if undetected, deteriorate the obtained position coordinates. The methodology exploits robust statistical tools for regression provided by the Flexible Statistics and Data Analysis (FSDA) toolbox, an extension of MATLAB for the analysis of complex datasets. Our results show how the robust analysis FSDA technique improves the capability of detecting actual outliers in GNSS measurements, with respect to the present gLAB pre-fit outlier detection function. This study concludes that robust statistical analysis techniques, when applied to the pre-fit layer of gLAB, improve the overall reliability and accuracy of the positioning solution.

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems.

Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

A probabilistic hammer for nailing complex neural data analyses

In this issue of Neuron, Młynarski etal. (2021) provide a maxent-based normative method for flexible neural data analysis by combining data-driven and theory-driven approaches. The next challenge is identifying the right frameworks to use this method at its best.

Analysis and Comparison of Forecasting Algorithms for Telecom Customer Churn

The integrated algorithm is a highly flexible data analysis and prediction algorithm. In many big data competitions at home and abroad, the winning teams basically adopt the idea of integrated algorithms such as random forest, GBDT, XGBoost and other algorithms. This shows that accuracy of ensemble algorithms is still very advantageous in terms of predictive classification. The main task of this article is to predict the loss of telecom customers. Under the current situation of saturation of the telecom market, how to retain the original customers is the main task of each telecom operator. This article mainly compares the four prediction models on the telecom data set. Predictive performance, the final performance evaluation index also shows that the random forest model and XGBoost model of integrated thought have better predictive models.