Tool For Exploratory Analysis Research Articles

GeneSurfer Enables Transcriptome-wide Exploration and Functional Annotation of Gene Co-expression Modules in 3D Spatial Transcriptomics Data.

Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatial transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.

S CIRCLE-An interactive visual exploration tool for single cell RNA-Seq data.

sCIRCLE (single-Cell Interactive Real-time Computer visualization for Low-dimensional Exploration) is a tool for exploratory analysis of single cell RNA-seq (scRNA-seq) data sets, with a focus on bacterial scRNA-seq. The software takes an information design perspective to re-envision visually and interactively exploring low dimensional representations of scRNA-Seq data. Users can project cells in various 3D and 2D spaces and interactively query and paint cells using rich metadata sets reporting on cell cluster, gene function, and gene expression. As a standalone application it contains, among other features, options for dimensionality reduction, navigation and interaction with data in 3d and 2d space, gene filtering, fold change and metacell computation as well as various capabilities for visualization, data import and export.

Leveraging the performance of conventional spectroscopic techniques through data fusion approaches in high-quality edible oil adulteration analyses

The high demand, high cost, and low regulations surrounding high-quality edible oils (HQEO) make them a target for fraudulent actions, particularly adulteration with refined oils. Consequently, the authentication of this kind of oil is of great interest. This work assessed the adulteration degree of five HQEOs: sesame, flaxseed, chia, rapeseed, and extra virgin olive oils, using different chemometric strategies to enhance the detection capability of the analytical methodology. Refined oils used as adulterants were evaluated at low concentrations (2–15 % v/v). Three multidimensional spectroscopic techniques (UV–Visible, near-infrared, and excitation-emission matrix fluorescence) were used, and two data fusion strategies (low- and mid-level) were evaluated. Principal component analysis was applied as an exploratory analysis tool to visualize and interpret the information contained in the dataset. For the adulterant quantification, partial least squares regression analysis was used to build the sensitive predictive models. The results revealed that chemical information enhancement leverages the ability to attain reduced prediction compared to unidimensional signals. In scenarios with low sample variability, conventional unidimensional spectroscopy (UV–Visible or near-infrared) data was shown to be adequate to guarantee predictive efficiency. In contrast, when analysing predictive figures derived from models built using a dataset with high variability, e.g., brands, low-level data fusion approaches enhance predictive efficiency. The results showed that excitation-emission matrix-based or low-level data fusion approaches can be accurately implemented to guarantee the authenticity of edible oils even when a low content of adulterant oil is presented.

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease.

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

Sentimental Approach to Airline Service Quality Evaluation

This paper focuses on the analysis of traditional methods of service quality evaluation and represents a new sentimental approach to airline service quality evaluation employing user-generated content. It identifies aspects of airline service that passengers react to positively or negatively using the word cloud method—a basic straightforward exploratory analysis tool. The aim is to introduce an approach that can be implemented using freely available analytical software tools and freely available data. As a case study, authors evaluated selected airlines’ service quality using sentimental analysis of user-generated content. The research relied on sentiment analysis of Twitter posts related to selected airlines’ service quality. The paper describes how Twitter can be used for data mining, sentimental analysis, and airline service quality evaluation. The authors analysed over 30,000 posts related to the service quality of Ryanair, Southwest Airlines, American Airlines and KLM and proposed two types of word clouds (for each individual airline) which allow more informed decisions about enhancing the service quality of selected airlines. Compared to rather expensive traditional methods of airline service quality evaluation, such as onboard surveys of airline passengers or on-site surveys of passengers at airport departure gates, the key advantages of this new approach are the availability of free data and free analytical software tools. Moreover, this approach allows analysis of the service quality of competing airlines and, thus, provides internal opportunities for comparison. The results contribute to the literature by clarifying how both positive and negative passenger feedback impacts airline service quality and airline product planning.

CARRoT: R-package for predictive modelling by means of regression, adjusted for multiple regularisation methods.

We present an R-package for predictive modelling, CARRoT (Cross-validation, Accuracy, Regression, Rule of Ten). CARRoT is a tool for initial exploratory analysis of the data, which performs exhaustive search for a regression model yielding the best predictive power with heuristic 'rules of thumb' and expert knowledge as regularization parameters. It uses multiple hold-outs in order to internally validate the model. The package allows to take into account multiple factors such as collinearity of the predictors, event per variable rules (EPVs) and R-squared statistics during the model selection. In addition, other constraints, such as forcing specific terms and restricting complexity of the predictive models can be used. The package allows taking pairwise and three-way interactions between variables into account as well. These candidate models are then ranked by predictive power, which is assessed via multiple hold-out procedures and can be parallelised in order to reduce the computational time. Models which exhibited the highest average predictive power over all hold-outs are returned. This is quantified as absolute and relative error in case of continuous outcomes, accuracy and AUROC values in case of categorical outcomes. In this paper we briefly present statistical framework of the package and discuss the complexity of the underlying algorithm. Moreover, using CARRoT and a number of datasets available in R we provide comparison of different model selection techniques: based on EPVs alone, on EPVs and R-squared statistics, on lasso regression, on including only statistically significant predictors and on stepwise forward selection technique.

A universal tool for predicting differentially active features in single-cell and spatial genomics data

With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on comparisons between clusters of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: (1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and (2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as packages in both R and Python.

Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets.

Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides spatially resolved molecular absorption spectra, which can subsequently yield useful information by powerful artificial intelligence methods. A typical analysis pipeline in using IR imaging data for chemical pathology often involves iterative processes of segmentation, evaluation, and analysis that necessitate rapid data exploration. Here, we present a fast, reliable, and intuitive method based on a phasor representation of spectra and discuss its unique applicability for IR imaging data. We simulate different features extant in IR spectra and discuss their influence on the phasor waveforms; similarly, we undertake IR image analysis in the transform space to understand spectral similarity and variance. We demonstrate the potential of phasor analysis for biomedical tissue imaging using a variety of samples, using fresh frozen surgical prostate resections and formalin-fixed paraffin-embedded breast cancer tissue microarray samples as model systems that span common histopathology practice. To demonstrate further generalizability of this approach, we apply the method to data from different experimental conditions─including standard (5.5 μm × 5.5 μm pixel size) and high-definition (1.1 μm × 1.1 μm pixel size) Fourier transform IR (FTIR) spectroscopic imaging using transmission and transflection modes. Quantitative segmentation results from our approach are compared to previous studies, showing good agreement and quick visualization. The presented method is rapid, easy to use, and highly capable of deciphering compositional differences, presenting a convenient tool for exploratory analysis of IR imaging data.

Visualizing high dimensional structures in geochemical datasets using a combined compositional data analysis and Databionic swarm approach

Classical tools for exploratory analysis of large geochemical datasets (e.g., cluster analysis, principal component analysis, etc.) have been successfully utilized for decades to understand complex structures and identify natural, multi-dimensional data clusters. More recent development of machine learning algorithms, both supervised and unsupervised, have increasingly been shown to derive deeper insights from complex datasets than older methods. Moreover, geochemists have long recognized that concentration data are compositional and require special mathematical treatment, such as application of Compositional Data Analysis (CoDA) techniques. Proper approaches for linking CoDA and machine learning is an area of active research. Here, we investigate the behavior of trace elements in coal and coal combustion products from a power plant burning Appalachian Basin coals using a combination of CoDa methods and a relatively new tool for visualizing complex multivariate structures, Databionic swarm (DBS). Databionic swarm is an unsupervised method which combines concepts from emergence, self-organization, game theory, and swarm intelligence, and allows for mapping of higher-order structures onto a lower-dimensional output space (2-dimensions in this case). A suggested approach for converting the raw geochemical data to isometric log-ratios is developed for the system and results from DBS and robust PCA are compared and contrasted.Both PCA and DBS results show similar clustering of feed coal (FC) and pulverized coal (PC) samples and bottom ash (BA) with economizer fly ash (EFA) samples. However, within group variation of the samples was relatively difficult to identify in the PCA analysis. Results from DBS show that the BA and EFA samples are geochemically distinguishable based on the relative abundances of As, Cd, and Pb vs. Cr and Ni and As and Cd vs. Pb. Similarly, the PC and FC samples exhibit statistically different Cr vs. Ni ratios, which was not obvious from the PCA scores. Abnormally low Hg to Cl ratios in the fly ash (FA) samples relative to the rest of the samples, provided insight into the preferential removal of Hg vs. Cl, possibly in response to the generation of more gaseous oxidized mercury in the flue gas, owing to elevated Cl concentrations in the coal. Such findings demonstrate that DBS and other machine learning techniques allow for quick visualization of complex geochemical data structures in a way that is both consistent with the principals of CoDA and an improvement over classical methods.

Reweighted L1 Minimization for Networkwide Weak Spot Detection from Traffic Speed Deflectometer Measurements

The traffic speed deflectometer (TSD) can collect network-level deflection data in a cost-effective and timely manner. However, an automated feature extraction method is needed to interpret such large amounts of data. Basis pursuit (BP) is one such technique: BP can sparsely decompose TSD measurements over a given basis or set of bases of the signal vector space that can represent a particular signal feature. For instance, the TSD surface deflection estimates can be reconstructed as a combination of wavelets, which represent continuously varying features like changes in pavement properties, plus pulses representing the response from structurally weak spots. Yet, the denoised measurements estimated by BP may either be riddled with several false positives (spikes that mismatch real weak spots) or be constructed out of true positive features with damped amplitude. This paper presents reweighed L1 minimization (RWL1), an enhancement to BP to both correct the dampening and discard false positives. This paper introduces RWL1 with examples from simulated data to show its advantage over vanilla BP denoising, plus a demonstration featuring real TSD measurements from a networkwide survey to demonstrate RWL1’s potential as an exploratory analysis tool to detect structurally weak locations within the pavement network worthy of further investigation at the project level.

Identifying the links among urban climate hazards, mitigation and adaptation actions and sustainability for future resilient cities

Comprehensive and objective assessment methods need to be developed to create inclusive, safe, resilient and sustainable cities. Monitoring the evolution of sustainability and well-being in the cities is important for researchers implementing the UN 2030 Agenda. This research explores and analyzes the climate change hazards, adaptation- and mitigation actions and their implementation in 776 cities located in 84 different countries. The climate action co-benefits are supporting the achievement of sustainable development goals, which are comprehensively elaborated in this methodological development. The analyzes are carried out based on the continuously updated Carbon Disclosure Project database. An open source algorithm has been developed that represents the CDP database as a bit table and use frequent itemset mining for the identification of global patterns of climate hazards, mitigation- and adaptation actions and their co-benefits, therefore, this paper offers an exploratory analysis tool that is suitable for monitoring climate actions. The most frequently identified mitigation actions in cities were energy planting (1444 actions), and on-site renewable production (644), while the most common actions for adaptation were tree planting (283) and flood mapping (267). Regarding city size, 41% of large metropolitan areas plan to develop mass transit actions, while the separate collection of recyclables is typical in 85% of towns. 56.2% of CDP database actions support access to sustainable cities and communities goal (SDG11), 54.2% access to climate action goal (SDG13), and the emergence of affordable and clean energy (SDG7) and gender equality goal (SDG5) are below 5%.

대학생 디지털 시민성 측정도구 개발 및 타당화 연구

Objectives The purpose of this study is to establish the basis of digital citizenship education as general education by developing the measurement tool for cultivating digital citizenship of college students in the trans-boundary and hyper-connected era considering that it is very lacking in the researches related to digital citizenship in the level of higher education.
 Methods For this, this study conducted the literary review on the concept and elements of digital citizenship, set up the subfactors, developed the preliminary items, verified the content validity with experts, and verified the construct validity of the measurement(exploratory and confirmatory factor analysis) tool.
 Results As a result, the digital citizenship measurement tool was composed of total 68 items such as 21 items of cognitive area(7 items of understanding of digital information & society, 5 items of critical thinking, 4 items of software design, 5 items of expressive ability), 20 items of justice area(13 items of digital etiquette, 7 items of active participation and motivation), and 27 items of behavioral area(8 items of conflict/problem-solving ability, 7 items of ethical use, 8 items of pursuit of information sharing & change, 4 items of social responsibility).
 Conclusions This study is significant in the aspect of developing and validating the digital citizenship measurement tool for college students. This tool could be utilized for seeking for the direction of digital citizenship education, developing programs for strengthening digital citizenship, and analyzing the effects of programs.

Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study.

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.

PyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.

To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface. A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities. pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories. pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection.

A Simple Algorithm for Semiquantitative Analysis of Scored Histology Data in the R Environment, on the Example of Murine Non-Alcoholic Steatohepatitis Pharmacotherapy

Despite the high medical and socioeconomic burden of non-alcoholic fatty liver disease (NAFLD), treatments that could effectively reduce histological liver damage in this condition are lacking. As providing only qualitative data is a major limitation of most histological scoring systems, we aimed to develop a simple and straightforward algorithm for semiquantitative analysis of scored histology data using the extended Fisher’s exact test in the R environment. As an illustrative example, we used the effects of L-ornithine L-aspartate (LOLA) and empagliflozin (EMPA) in a 3-month chemical/dietary murine model of NAFLD. 100 C57Bl/6 mice were randomized into 4 groups: Intact (n = 10), Control (NAFLD; n = 30), LOLA (NAFLD + 1.5 g·kg−1 b.w./d LOLA orally; n = 30), and EMPA (NAFLD + 10 mg·kg−1 b.w./d EMPA orally; n = 30). LOLA reduced hepatitis activity (p < 0.05), cholestasis, necrosis, and fibrosis severity (p < 0.01), and EMPA prevented necrosis (p < 0.05) and reduced fibrosis severity (p < 0.01). The statistical approach we suggest can be used as a simple complementary tool for exploratory analysis of scored histology data.

МЕТОДЫ И АЛГОРИТМЫ КЛАСТЕРИЗАЦИИ ТЕКСТОВЫХ ДАННЫХ (ОБЗОР)

The article deals with one of the important tasks of artificial intelligence – machine processingof natural language. The solution of this problem based on cluster analysis makes it possibleto identify, formalize and integrate large amounts of linguistic expert information under conditionsof information uncertainty and weak structure of the original text resources obtained fromvarious subject areas. Cluster analysis is a powerful tool for exploratory analysis of text data,which allows for an objective classification of any objects that are characterized by a number offeatures and have hidden patterns. A review and analysis of modern modified algorithms for agglomerativeclustering CURE, ROCK, CHAMELEON, non-hierarchical clustering PAM, CLARAand the affine transformation algorithm used at various stages of text data clustering, the effectivenessof which is verified by experimental studies, is carried out. The paper substantiates therequirements for choosing the most efficient clustering method for solving the problem of increasing the efficiency of intellectual processing of linguistic expert information. Also, the paper considersmethods for visualizing clustering results for interpreting the cluster structure and dependencieson a set of text data elements and graphical means of their presentation in the form ofdendograms, scatterplots, VOS similarity diagrams, and intensity maps. To compare the quality ofthe algorithms, internal and external performance metrics were used: "V-measure", "AdjustedRand index", "Silhouette". Based on the experiments, it was found that it is necessary to use ahybrid approach, in which, for the initial selection of the number of clusters and the distribution oftheir centers, use a hierarchical approach based on sequential combining and averaging the characteristicsof the closest data of a limited sample, when it is not possible to put forward a hypothesisabout the initial number of clusters. Next, connect iterative clustering algorithms that providehigh stability with respect to noise features and the presence of outliers. Hybridization increasesthe efficiency of clustering algorithms. The research results showed that in order to increase thecomputational efficiency and overcome the sensitivity when initializing the parameters of clusteringalgorithms, it is necessary to use metaheuristic approaches to optimize the parameters of thelearning model and search for a global optimal solution.

Evaluating machine learning models to classify occupants’ perceptions of their indoor environment and sleep quality from indoor air quality

ABSTRACT A variety of factors can affect a person’s perception of their environment and health, but one factor that is often overlooked in indoor settings is the air quality. To address this gap, we develop and evaluate four Machine Learning (ML) models on two disparate datasets using Indoor Air Quality (IAQ) parameters as primary features and components of self-reported IAQ satisfaction and sleep quality as target variables. In each case, we compare models to each other as well as to a simple model that always predicts the majority outcome. In the first analysis, we use open-source data collected from 93 California residences to predict occupant’s satisfaction with their indoor environment. Results indicate building ventilation rate, Relative Humidity (RH), and formaldehyde are most influential when predicting IAQ perception and do so with an accuracy greater than the simplified model. The second analysis uses IAQ data gathered from a field study we conducted with 20 participants over 11 weeks to train similar models. We obtain accuracy and F1 scores similar to the simplified model where PM2.5 and TVOCs represent the most important predictors. Our results underscore the ability of IAQ to affect a person’s perception of their built environment and health and highlight the utility of ML models to explore the strength of these relationships. Implications: The results from this study show that two outcome variables – occupant’s indoor air quality (IAQ) satisfaction and perceived sleep quality – are related to the measured IAQ parameters but not heavily influenced by typical values measured in apartments and homes. This study highlights the ability of machine learning models as exploratory analysis tools to determine underlying relationships within and across datasets in addition to understanding the importance of certain features on the outcome variable. We compare four different models and find that the random forest classifier has the best performance in both analysis on IAQ satisfaction and perceived sleep quality. It is a suitable model for predicting IAQ-related subjective metrics and also provides value insight into the feature importance of the IAQ parameters. The accuracy of any of these machine learning models in predicting occupants’ comfort or sleep quality is limited by the dataset size, how data is collected, and range of data. This study identifies the factors that are important to IAQ perception: ventilation rate, relative humidity, and concentrations of formaldehyde, NO2, and particulate matter. It indicates that sensors that can measure these variables are necessary for future, related studies that model occupants’ IAQ satisfaction. However, this study does not find strong relationships between any of the IAQ parameters measured in this study and perceived sleep quality despite the logical pathway between these many pollutants and respiratory issues. A prediction model of IAQ perception or sleep quality can be integrated into home management systems to automatically adjust building operations such as ventilation rates in smart buildings. Once buildings are equipped with a network of low-cost sensors that measure concentrations of pollutants and operating conditions of the ventilation system, the prediction model can be used to predict the occupants’ comfort and facilitate the control of the ventilation system.

PanExplorer: a web-based tool for exploratory analysis and visualization of bacterial pan-genomes.

MotivationAs pan-genome approaches are largely employed for bacterial comparative genomics and evolution analyses, but still difficult to be carried out by non-bioinformatician biologists, there is a need for an innovative tool facilitating the exploration of bacterial pan-genomes.ResultsPanExplorer is a web application providing various genomic analyses and reports, giving intuitive views that enable a better understanding of bacterial pan-genomes. As an example, we produced the pan-genome for 121 Anaplasmataceae strains (including 30 Ehrlichia, 15 Anaplasma, 68 Wolbachia).Availability and implementationPanExplorer is written in Perl CGI and relies on several JavaScript libraries for visualization (hotmap.js, MauveViewer, CircosJS). It is freely available at http://panexplorer.southgreen.fr. The source code has been released in a GitHub repository https://github.com/SouthGreenPlatform/PanExplorer. A documentation section is available on PanExplorer website.

ScGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data.

Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data. scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html. Supplementary data are available at Bioinformatics online.

GenomicDistributions: fast analysis of genomic intervals with Bioconductor

BackgroundEpigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data.ResultsGenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs.ConclusionsGenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html).