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CURRENT PROTOCOLS 2022年第4期2卷 e411页

作者： Ludt, Annekathrin Ustjanzew, Arsenij Binder, Harald Strauch, Konstantin Marini, Federico Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Med Biostat Epidemiol & Informat IMBEI Div Stat Genom & Bioinformat Mainz Germany Univ Freiburg Inst Med Biometry & Stat IMBI Fac Med Freiburg Germany Univ Freiburg Med Ctr Freiburg Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Ctr Thrombosis & Hemostasis Mainz CTH Mainz Germany

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory data analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. (c) 2022 The Authors. Current Protocols published by Wiley Periodica

关键词： data visualization functional interpretation interactive data analysis reproducible research RNA-seq

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Demonstration of BitGourmet: data analysis via Deterministic Approximation 20

Demonstration of BitGourmet: Data Analysis via Deterministic...

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ACM SIGMOD International Conference on Management of data (SIGMOD)

作者： Jo, Saehan Trummer, Immanuel Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781450367356

We demonstrate BitGourmet, a novel data analysis system that supports deterministic approximate query processing (DAQ). The system executes aggregation queries and produces deterministic bounds that are guaranteed to contain the true value. The system allows users to set a precision constraint on query results. Given a user-defined target precision, we operate on a carefully selected data subset to satisfy the precision constraint. More precisely, we divide each column vertically, bit-by-bit. Our specialized query processing engine evaluates queries on subsets of these bit vectors. This involves a scenario-specific query optimizer which relies on quality and cost models to decide the optimal bit selection and execution plan. In our demonstration, we show that DAQ realizes an interesting trade-off between result quality and execution time, making data analysis more interactive. We also offer manual control over the query plan, i.e., the bit selection and the execution plan, so that users can gain more insights into our system and DAQ in general.

关键词： interactive data analysis approximate query processing deterministic approximation

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Automatically Generating data Exploration Sessions Using Deep Reinforcement Learning 20

Automatically Generating Data Exploration Sessions Using Dee...

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ACM SIGMOD International Conference on Management of data (SIGMOD)

作者： Bar El, Ori Milo, Tova Somech, Amit Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781450367356

Exploratory data analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks - illustrative, curated exploratory sessions, on the same dataset, that were created by fellow data scientists who shared them online. Unfortunately, such notebooks are not always available (e.g., if the dataset is new or confidential). To address this, we present ATENA, a system that takes an input dataset and auto-generates a compelling exploratory session, presented in an EDA notebook. We shape EDA into a control problem, and devise a novel Deep Reinforcement Learning (DRL) architecture to effectively optimize the notebook generation. Though ATENA uses a limited set of EDA operations, our experiments show that it generates useful EDA notebooks, allowing users to gain actual insights.

关键词： interactive data analysis EDA autogenerated data exploration auto EDA notebooks auto generated EDA notebooks

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GeoBrick: exploration of spatiotemporal data

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VISUAL COMPUTER 2019年第2期35卷 191-204页

作者： Park, Ji Hwan Nadeem, Saad Kaufman, Arie SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

We present GeoBrick, an interactive technique for exploring spatiotemporal data. In GeoBrick, each region is comprised of multivariate data, which is encoded into simple shapes with colors. Additionally, users can adjust the resolution of data values to get an overview as well as details of the data. GeoBrick allows users to (1) juxtapose data and spatial profiles of discontiguous regions, (2) identify temporal patterns of user-defined classes of regions, and (3) comparatively evaluate across distinct configurations of regions. We demonstrate the effectiveness and efficacy of GeoBrick using two case studies.

关键词： Visual analytics Spatiotemporal visualization Multivariate data interactive data analysis

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oposSOM-Browser: an interactive tool to explore omics data landscapes in health science

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BMC BIOINFORMATICS 2020年第1期21卷 465-465页

作者： Loeffler-Wirth, Henry Reikowski, Jasmin Hakobyan, Siras Wagner, Jonas Binder, Hans Univ Leipzig Interdisciplinary Ctr Bioinformat Hartelstr 16-18 D-04107 Leipzig Germany Natl Acad Sci Inst Mol Biol Grp Bioinformat 7 Hasratyan Str Yerevan 0014 Armenia Univ Leipzig Leipzig Res Ctr Civilizat Dis LIFE Philipp Rosenthal Str 27 D-04103 Leipzig Germany

BackgroundoposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and *** functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular 'portrait landscapes', of associated phenotype diversity, and signalling pathway activation *** oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at ***/opossom-browser.

关键词： interactive data analysis Transcriptomics Results browser

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Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and analysis

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2020年 14卷 42页

作者： Grein, Stephan Qi, Guanxiao Queisser, Gillian Temple Univ Dept Math Philadelphia PA 19122 USA Res Ctr Julich Inst Neurosci & Med INM 10 Julich Germany

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., NeurolucidaXMLfiles,*** and plainASCIIfiles. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

关键词： GABAergic barrel cortex density visualization visual programming neuronal morphology synaptogenesis density maps interactive data analysis

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ideal: an R/Bioconductor package for interactive differential expression analysis

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BMC BIOINFORMATICS 2020年第1期21卷 565-565页

作者： Marini, Federico Linke, Jan Binder, Harald Johannes Gutenberg Univ Mainz Ctr Thrombosis & Hemostasis CTH Univ Med Ctr Langenbeckstr 1 D-55131 Mainz Germany Johannes Gutenberg Univ Mainz Inst Med Biostat Epidemiol & Informat IMBEI Univ Med Ctr Obere Zahlbacher Str 69 D-55131 Mainz Germany Univ Freiburg Fac Med & Med Ctr Inst Med Biometry & Stat Stefan Meier Str 26 D-79104 Freiburg Germany

BackgroundRNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is *** developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for *** is distributed as an R package in the Bioconductor project (http://***/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

关键词： RNA-Seq Differential expression interactive data analysis data visualization Transcriptomics R Bioconductor Shiny Web application Reproducible research

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Graph-based interactive data Federation System for Heterogeneous data Retrieval and Analytics 19

Graph-based Interactive Data Federation System for Heterogen...

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World Wide Web Conference (WWW)

作者： Vu, Xuan-Son Ait-Mlouk, Addi Elmroth, Erik Jiang, Lili Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9781450366748

Given the increasing number of heterogeneous data stored in relational databases, file systems or cloud environment, it needs to be easily accessed and semantically connected for further data analytic. The potential of data federation is largely untapped, this paper presents an interactive data federation system (https://***/319473546) by applying large-scale techniques including heterogeneous data federation, natural language processing, association rules and semantic web to perform data retrieval and analytics on social network data. The system first creates a Virtual database (VDB) to virtually integrate data from multiple data sources. Next, a RDF generator is built to unify data, together with SPARQL queries, to support semantic data search over the processed text data by natural language processing (NLP). Association rule analysis is used to discover the patterns and recognize the most important co-occurrences of variables from multiple data sources. The system demonstrates how it facilitates interactive data analytic towards different application scenarios (e.g., sentiment analysis, privacy-concern analysis, community detection).

关键词： heterogeneous data federation RDF interactive data analysis

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VAPOR: A Visualization Package Tailored to Analyze Simulation data in Earth System Science

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ATMOSPHERE 2019年第9期10卷 488-488页

作者： Li, Shaomeng Jaroszynski, Stanislaw Pearse, Scott Orf, Leigh Clyne, John Natl Ctr Atmospher Res POB 3000 Boulder CO 80307 USA Univ Wisconsin Cooperat Inst Meteorol Satellite Studies Madison WI 53706 USA

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR's ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR's visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

关键词： scientific visualization interactive data analysis support for Earth System Science graphical user interface-based cross-platform application

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Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年 60卷 104-113页

作者： Finlayson, Samuel G. Levy, Mia Reddy, Sunil Rubin, Daniel L. Harvard Med Sch Boston MA USA Vanderbilt Univ Sch Med Nashville TN USA Stanford Univ Sch Med Stanford CA USA

Objective: Wide-scale adoption of electronic medical records (EMRs) has created an unprecedented opportunity for the implementation of Rapid Learning Systems (RLSs) that leverage primary clinical data for real-time decision support. In cancer, where large variations among patient features leave gaps in traditional forms of medical evidence, the potential impact of a RLS is particularly promising. We developed the Melanoma Rapid Learning Utility (MRLU), a component of the RLS, providing an analytical engine and user interface that enables physicians to gain clinical insights by rapidly identifying and analyzing cohorts of patients similar to their own. Materials and methods: A new approach for clinical decision support in Melanoma was developed and implemented, in which patient-centered cohorts are generated from practice-based evidence and used to power on-the-fly stratified survival analyses. A database to underlie the system was generated from clinical, pharmaceutical, and molecular data from 237 patients with metastatic melanoma from two academic medical centers. The system was assessed in two ways: (1) ability to rediscover known knowledge and (2) potential clinical utility and usability through a user study of 13 practicing oncologists. Results: The MRLU enables physician-driven cohort selection and stratified survival analysis. The system successfully identified several known clinical trends in melanoma, including frequency of BRAF mutations, survival rate of patients with BRAF mutant tumors in response to BRAF inhibitor therapy, and sex-based trends in prevalence and survival. Surveyed physician users expressed great interest in using such on-the-fly evidence systems in practice (mean response from relevant survey questions 4.54/5.0), and generally found the MRLU in particular to be both useful (mean score 4.2/5.0) and useable (4.42/5.0). Discussion: The MRLU is an RLS analytical engine and user interface for Melanoma treatment planning that presents design p

关键词： Rapid learning Learning health system Precision medicine Melanoma interactive data analysis

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