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  1. Book ; Thesis: Supervised and unsupervised analyses in next generation sequencing data

    Hübschmann, Daniel / Eils, Roland

    2018  

    Institution Universität Heidelberg
    Author's details presented by Dr. med. Dipl. Phys. Daniel Huebschmann, CASM
    Subject code 610
    Language English
    Size xxiv, 269 Seiten, Illustrationen, Diagramme, 30 cm
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruperto-Carola University of Heidelberg, 2018
    Note Mit einer Zusammenfassung in englischer und deutscher Sprache
    HBZ-ID HT019803674
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    2018 E 1140 order for loan Magazin

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  2. Book ; Thesis: Investigation of the effect of caffeine on human brain metabolism by magnetic resonance spectroscopy and multimodal magnetic resonance imaging

    Hübschmann, Daniel

    2014  

    Author's details vorgelegt von: Daniel Huebschmann
    Language English
    Size 79 Blätter, Diagramme, 30 cm
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Dissertation, Ruprecht-Karls-Universität Heidelberg, 2016
    HBZ-ID HT018973734
    Database Catalogue ZB MED Medicine, Health

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  3. Article ; Online: simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results

    Gu, Zuguang / Hübschmann, Daniel

    Genomics, Proteomics & Bioinformatics. 2023 Feb., v. 21, no. 1 p.190-202

    2023  

    Abstract: Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant ... ...

    Abstract Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.
    Keywords algorithms ; bioinformatics ; data collection ; genes ; genomics ; proteomics ; Functional enrichment ; Simplify enrichment ; Clustering ; R/Bioconductor ; Software ; Visualization
    Language English
    Dates of publication 2023-02
    Size p. 190-202.
    Publishing place Elsevier B.V.
    Document type Article ; Online
    Note Pre-press version ; Creative Commons Attribution 4.0 Generic (CC BY 4.0) ; Resource is Open Access
    ZDB-ID 2240213-5
    ISSN 2210-3244 ; 1672-0229
    ISSN (online) 2210-3244
    ISSN 1672-0229
    DOI 10.1016/j.gpb.2022.04.008
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: rGREAT: an R/bioconductor package for functional enrichment on genomic regions.

    Gu, Zuguang / Hübschmann, Daniel

    Bioinformatics (Oxford, England)

    2022  Volume 39, Issue 1

    Abstract: Summary: GREAT (Genomic Regions Enrichment of Annotations Tool) is a widely used tool for functional enrichment on genomic regions. However, as an online tool, it has limitations of outdated annotation data, small numbers of supported organisms and gene ...

    Abstract Summary: GREAT (Genomic Regions Enrichment of Annotations Tool) is a widely used tool for functional enrichment on genomic regions. However, as an online tool, it has limitations of outdated annotation data, small numbers of supported organisms and gene set collections, and not being extensible for users. Here, we developed a new R/Bioconductorpackage named rGREAT which implements the GREAT algorithm locally. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions.
    Availability and implementation: The package rGREAT is freely available from the Bioconductor project: https://bioconductor.org/packages/rGREAT/. The development version is available at https://github.com/jokergoo/rGREAT. Gene Ontology gene sets for more than 600 organisms retrieved from Ensembl BioMart are presented in an R package BioMartGOGeneSets which is available at https://github.com/jokergoo/BioMartGOGeneSets.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Software ; Genomics ; Genome ; Algorithms ; Gene Ontology
    Language English
    Publishing date 2022-11-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac745
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results.

    Gu, Zuguang / Hübschmann, Daniel

    Genomics, proteomics & bioinformatics

    2022  Volume 21, Issue 1, Page(s) 190–202

    Abstract: Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant ... ...

    Abstract Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.
    MeSH term(s) Software ; Algorithms ; Computational Biology/methods ; Cluster Analysis ; Semantics
    Language English
    Publishing date 2022-06-06
    Publishing country China
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2240213-5
    ISSN 2210-3244 ; 1672-0229
    ISSN (online) 2210-3244
    ISSN 1672-0229
    DOI 10.1016/j.gpb.2022.04.008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Improve consensus partitioning via a hierarchical procedure.

    Gu, Zuguang / Hübschmann, Daniel

    Briefings in bioinformatics

    2022  Volume 23, Issue 3

    Abstract: Consensus partitioning is an unsupervised method widely used in high-throughput data analysis for revealing subgroups and assigning stability for the classification. However, standard consensus partitioning procedures are weak for identifying large ... ...

    Abstract Consensus partitioning is an unsupervised method widely used in high-throughput data analysis for revealing subgroups and assigning stability for the classification. However, standard consensus partitioning procedures are weak for identifying large numbers of stable subgroups. There are two major issues. First, subgroups with small differences are difficult to be separated if they are simultaneously detected with subgroups with large differences. Second, stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitioning in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more meaningful subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a large deoxyribonucleic acid methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionalities for analysis and visualization. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis. The cola package is available at https://bioconductor.org/packages/cola/.
    MeSH term(s) Consensus ; Software
    Language English
    Publishing date 2022-03-10
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbac048
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Pkgndep: a tool for analyzing dependency heaviness of R packages.

    Gu, Zuguang / Hübschmann, Daniel

    Bioinformatics (Oxford, England)

    2022  Volume 38, Issue 17, Page(s) 4248–4251

    Abstract: Summary: Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the ... ...

    Abstract Summary: Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of dependencies that a parent uniquely brings to a package and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages.
    Availability and implementation: The package pkgndep and documentations are freely available from the Comprehensive R Archive Network https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 22 076 CRAN and Bioconductor packages retrieved on June 8, 2022 are available at https://pkgndep.github.io/.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Child ; Humans ; Software ; Ecosystem
    Language English
    Publishing date 2022-07-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btac449
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: spiralize: an R package for Visualizing Data on Spirals.

    Gu, Zuguang / Hübschmann, Daniel

    Bioinformatics (Oxford, England)

    2021  

    Abstract: Summary: Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. ...

    Abstract Summary: Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The flexibility and power of spiralize are demonstrated by five examples from real-world datasets.
    Availability and implementation: The spiralize package and documentations are freely available at the Comprehensive R Archive Network (CRAN) https://CRAN.R-project.org/package=spiralize.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Language English
    Publishing date 2021-11-26
    Publishing country England
    Document type Journal Article
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btab778
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Make Interactive Complex Heatmaps in R.

    Gu, Zuguang / Hübschmann, Daniel

    Bioinformatics (Oxford, England)

    2021  

    Abstract: Summary: Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ... ...

    Abstract Summary: Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ComplexHeatmap package. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one additional line of code. InteractiveComplexHeatmap also provides flexible functionalities for integrating interactive heatmap widgets to build more complex and customized Shiny web applications.
    Availability and implementation: The InteractiveComplexHeatmap package and documentations are freely available from the Bioconductor project: https://bioconductor.org/packages/InteractiveComplexHeatmap/. A complete and printer-friendly version of the documentation can also be found in Supplementary File 1.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Language English
    Publishing date 2021-12-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btab806
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: National Center for Tumor Diseases Precision Oncology Thesaurus for Drugs: A Curated Database for Drugs, Drug Classes, and Drug Targets in Precision Cancer Medicine.

    Kreutzfeldt, Simon / Horak, Peter / Hübschmann, Daniel / Knurr, Alexander / Fröhling, Stefan

    JCO clinical cancer informatics

    2023  Volume 7, Page(s) e2200147

    MeSH term(s) Humans ; Neoplasms/drug therapy ; Precision Medicine ; Medical Oncology ; Molecular Targeted Therapy ; Vocabulary, Controlled
    Language English
    Publishing date 2023-02-19
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2473-4276
    ISSN (online) 2473-4276
    DOI 10.1200/CCI.22.00147
    Database MEDical Literature Analysis and Retrieval System OnLINE

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