Existing Analysis Tools Research Articles

Ceas: an R package for Seahorse data analysis and visualization.

Measuring cellular energetics is essential to understanding a matrix's (e.g. cell, tissue, or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization. ceas is available on CRAN (https://cran.r-project.org/package=ceas). Source code and installable tarballs are freely available for download at https://github.com/jamespeapen/ceas/releases/ under the MIT license. Package documentation may be found at https://jamespeapen.github.io/ceas/. ceas is implemented in R and is supported on macOS, Windows and Linux.

MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

A Case Study on the Operational Performance Evaluation of a Manufacturing Process and System (MaPS) Sustainability Analysis Tool for Engineering Education

As the future product and process design decision-makers, students in engineering should have knowledge of sustainability and its application in industry. However, educators are challenged in training students in these concepts. Existing analysis tools are deficient in supporting sustainability assessment by nonexperts. To address this gap, the Manufacturing Process and System (MaPS) Sustainability Analysis tool was developed under the Constructionism in Learning: Sustainable Life Cycle Engineering (CooL:SLiCE) project. The technical aspects of developing a framework and its associated proof-of-concept MaPS Sustainability Analysis tool are reported in a separate study. Herein, the educational aspects of the MaPS Sustainability Analysis tool and its operational performance in terms of ease of use and usefulness to assess its suitability for the classroom are investigated. Undergraduate and graduate engineering students at Tampere University and Oregon State University were recruited to use and evaluate the tool. Through application of the Technology Acceptance Model (TAM), it was determined that the 24 study participants found the tool easy to use and useful in analyzing product design, manufacturing process, and supply chain sustainability performance. To move this proof of concept toward general use, key software operational characteristics and a GUI should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS Sustainability Analysis tool.

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures. SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study.

We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures-total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation.

A strategy for fast evaluation of nonviscously damped systems with arbitrary kernels

In this work, a fast algorithm to evaluate the dynamic response of nonviscously damped systems with arbitrary distributions of damping characterised by arbitrary kernel functions (both number and form) is proposed. The proposed algorithm requires mere matrix–vector operations. It can be integrated into existing analysis tools with various time integration methods in forms of either element-based dashpots or global damping model. Compared to other existing methods, the proposed algorithm does not explicitly evaluate the convolution integral and thus does not require the storage of the whole velocity history. It shows superior accuracy while the corresponding computational cost is low.

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data.

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e.the higher-rank matrices. However, the existing analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. SCOIT incorporates various distributions, including Gaussian, Poisson, and negative binomial distributions, to deal with sparse, noisy, and heterogeneous single-cell data. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to eight single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to nine state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming current imputation methods with the Pearson correlation coefficient increased by 3.38-39.26%; moreover, SCOIT accommodates the scenario that subsets of the cells are with merely one omic profile available.

Inferring sources of substandard and falsified products in pharmaceutical supply chains

Substandard and falsified pharmaceuticals, prevalent in low- and middle-income countries, substantially increase levels of morbidity, mortality and drug resistance. Regulatory agencies combat this problem using post-market surveillance by collecting and testing samples where consumers purchase products. Existing analysis tools for post-market surveillance data focus attention on the locations of positive samples. This article looks to expand such analysis through underutilized supply-chain information to provide inference on sources of substandard and falsified products. We first establish the presence of unidentifiability issues when integrating this supply-chain information with surveillance data. We then develop a Bayesian methodology for evaluating substandard and falsified sources that extracts utility from supply-chain information and mitigates unidentifiability while accounting for multiple sources of uncertainty. Using de-identified surveillance data, we show the proposed methodology to be effective in providing valuable inference.

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in, and several users have participated in the accumulation of massive transaction data. Complete transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in blockchains. Network modeling and analysis of cryptocurrency transaction records are common methods in blockchain data analysis. The analysis of attribute graphs can provide insights into various economic indicators, illegal activities, and general Internet security, among others. Accordingly, this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex networks. To provide systematic guidance for researchers, we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects: blockchain data model, data acquisition on blockchains, existing analysis tools, available insights, and common analysis methods. For each aspect, we introduce the research problems, summarize the methods, and discuss the results and findings. Finally, we present future research points and several open questions in the study of cryptocurrency transaction networks.

Bayesian regression analysis of skewed tensor responses.

Tensor regression analysis is finding vast emerging applications in a variety of clinical settings, including neuroimaging, genomics, and dental medicine. The motivation for this paper is a study of periodontal disease (PD) with an order-3 tensor response: multiple biomarkers measured at prespecified tooth-sites within each tooth, for each participant. A careful investigation would reveal considerable skewness in the responses, in addition to response missingness. To mitigate the shortcomings of existing analysis tools, we propose a new Bayesian tensor response regression method that facilitates interpretation of covariate effects on both marginal and joint distributions of highly skewed tensor responses, and accommodates missing-at-random responses under a closure property of our tensor model. Furthermore, we present a prudent evaluation of the overall covariate effects while identifying their possible variations on only a sparse subset of the tensor components. Our method promises Markov chain Monte Carlo (MCMC) tools that are readily implementable. We illustrate substantial advantages of our proposal over existing methods via simulation studies and application to a real data set derived from a clinical study of PD. The R package BSTN available in GitHub implements our model.

GREAP: a comprehensive enrichment analysis software for human genomic regions.

The rapid development of genomic high-throughput sequencing has identified a large number of DNA regulatory elements with abundant epigenetics markers, which promotes the rapid accumulation of functional genomic region data. The comprehensively understanding and research of human functional genomic regions is still a relatively urgent work at present. However, the existing analysis tools lack extensive annotation and enrichment analytical abilities for these regions. Here, we designed a novel software, Genomic Region sets Enrichment Analysis Platform (GREAP), which provides comprehensive region annotation and enrichment analysis capabilities. Currently, GREAP supports 85 370 genomic region reference sets, which cover 634 681 107 regions across 11 different data types, including super enhancers, transcription factors, accessible chromatins, etc. GREAP provides widespread annotation and enrichment analysis of genomic regions. To reflect the significance of enrichment analysis, we used the hypergeometric test and also provided a Locus Overlap Analysis. In summary, GREAP is a powerful platform that provides many types of genomic region sets for users and supports genomic region annotations and enrichment analyses. In addition, we developed a customizable genome browser containing >400000000 customizable tracks for visualization. The platform is freely available at http://www.liclab.net/Greap/view/index.

Variational Phylodynamic Inference Using Pandemic-scale Data.

The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.

CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model.

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.

Selection of at-grade highway-rail crossings for grade separation

ABSTRACT Highway-rail at-grade crossings (HRGCs) are critical spatial locations of utmost importance for transportation safety. Using the national HRGC inventory database and 5 years of accident data in Oklahoma, a data-driven hierarchical evaluation process was developed for identifying and prioritizing potential HRGCs as candidates for grade separations, which includes four sequential levels of analyses: the practice guidelines, hazard index-based ranking, safety analysis, and full investment analysis. Various types of benefits, in terms of traffic safety, travel time, vehicle operating cost, and environment, were considered. The top-ranked HRGCs were identified and their ranking results were compared among the different methods. The prioritization results were mostly consistent while with several exceptions. This study relied on existing analysis tools and developed a comprehensive while practical sequential prioritization process, which can be directly implemented at state highway agencies for statewide selection of grade separation projects.

Research on Structural Flexibility and Acceptance Model (SFAM) Reconstruction Based on Disruption Innovation in the Social Humanities and Education Sector

The research objectives are as follows: (1) Developing a flexible structural model of the relationship between variables. (2) Develop a structural model that is robust with the assumptions of normality and homoscedasticity. (3) Obtain estimator properties from the flexible and robust SFAM structural model. (4) Obtaining hypothesis testing of each relationship constructed from a flexible and robust SFAM structural model. This research is integrated with a flexible and robust model approach based on nonparametric smoothing spline (RNSS) regression analysis which can capture the form of relationships that depend on empirical data, and the robustness of the model based on the distribution assumption and the assumption of homoscedasticity error variance. There are at least three transformation methods, namely SRS, MSI, and RASCH, which will be used in the development of the Structural Flexibility and Acceptance Model (SFAM). The results obtained from the research progress report are obtaining the development of a flexible structural model of the form of the relationship between variables, obtaining the development of a robust structural model of the assumptions of normality and homoscedasticity, obtaining the estimator properties of the flexible and robust SFAM structural model, and obtaining hypothesis testing. of each relationship constructed from a flexible and robust SFAM structural model. The originality of the theory in this study is very visible in the discovery of a new model, namely SFAM which can accommodate several things, which are the weaknesses of several existing analysis tools such as reciprocal and recursive models, more than one endogenous variable, flexible and robust models, overcoming inadmissible solutions, reflective indicators, formative , and reflective/formative (on the second-order), metric and non-metric data, and simultaneous processing of the input score data (through transformation to scale).

Finite element method for singularly perturbed problems with two parameters on a Bakhvalov-type mesh in 2D

On a Bakhvalov-type mesh widely used for boundary layers, we consider the finite element method for singularly perturbed elliptic problems with two parameters on the unit square. It is a very challenging task to analyze uniform convergence of finite element method on this mesh in 2D. The existing analysis tool, quasi-interpolation, is only applicable to one-dimensional case because of the complexity of Bakhvalov-type mesh in 2D. In this paper, a powerful tool, Lagrange-type interpolation, is proposed, which is simple and effective and can be used in both 1D and 2D. The application of this interpolation in 2D must be handled carefully. Some boundary correction terms must be introduced to maintain the homogeneous Dirichlet boundary condition. These correction terms are difficult to be handled because the traditional analysis do not work for them. To overcome this difficulty, we derive a delicate estimation of the width of some mesh. Moreover, we adopt different analysis strategies for different layers. Finally, we prove uniform convergence of optimal order. Numerical results verify the theoretical analysis.

TidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

BackgroundThe drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa.ResultsWe developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow.ConclusionstidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

A novel image analysis algorithm reveals that media conditioned with chitosan and platelet-rich plasma biomaterial dose dependently increases fibroblast migration in a scratch assay

Chitosan (CS) and Platelet-Rich Plasma (PRP) both display interesting properties for wound healing applications. A hybrid CS-PRP biomaterial was previously developped, consisting of a freeze dried CS formulation solubilized in PRP that promotes tissue repair and regeneration. The purpose of the current study was to investigate the ability of the CS-PRP biomaterial to stimulate cell migration in vitro. Scratch assays revealed that CS-PRP significantly stimulates the migration rate of cells compared to cells in culture medium but not differently than PRP alone. The increase in the migration rate is dose-dependent at low dose and reaches a plateau corresponding with maximum cell motility. Cell migration rate as a function of the number of platelets that have degranulated in culture medium (to which total concentration of growth factors contributing to cell response is proportionnal), follows a modified Hill model. To analyze photographs taken during the assay and follow cell migration, an open source image analysis algorithm was developed: SAMScratch (Systematic Area Measurement of Scratch - available here: https://github.com/Biomaterials-and-Cartilage-Laboratory/SAM-Scratch). Compared with other existing analysis tools, the algorithm is precise in the determination of the scratch area and performs equally well with usual and challenging images. This study resulted in the creation of a freely available application for scratch assay analysis and provided evidence that CS-PRP implants hold promise for treatment of wounds.

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.