pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

dc.contributor.authorMarini, Federico
dc.contributor.authorBinder, Harald
dc.date.accessioned2019-11-08T09:24:22Z
dc.date.available2019-11-08T10:24:22Z
dc.date.issued2019
dc.description.abstractBackground Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking. Results We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components. Conclusion pcaExplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.en_GB
dc.description.sponsorshipDFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
dc.identifier.doihttp://doi.org/10.25358/openscience-219
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/221
dc.language.isoeng
dc.rightsCC-BY-4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc610 Medizinde_DE
dc.subject.ddc610 Medical sciencesen_GB
dc.titlepcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal componentsen_GB
dc.typeZeitschriftenaufsatzde_DE
jgu.journal.titleBMC bioinformatics
jgu.journal.volume20
jgu.organisation.departmentFB 04 Medizin
jgu.organisation.nameJohannes Gutenberg-Universität Mainz
jgu.organisation.number2700
jgu.organisation.placeMainz
jgu.organisation.rorhttps://ror.org/023b0x485
jgu.pages.alternativeArt. 331
jgu.publisher.doi10.1186/s12859-019-2879-1
jgu.publisher.issn1471-2105
jgu.publisher.nameBioMed Central
jgu.publisher.placeLondon
jgu.publisher.urihttp://dx.doi.org/10.1186/s12859-019-2879-1
jgu.publisher.year2019
jgu.rights.accessrightsopenAccess
jgu.subject.ddccode610
jgu.type.dinitypeArticle
jgu.type.resourceText
jgu.type.versionPublished versionen_GB
opus.affiliatedMarini, Federico
opus.affiliatedBinder, Harald
opus.date.accessioned2019-11-08T09:24:22Z
opus.date.available2019-11-08T10:24:22
opus.date.modified2019-11-13T09:48:50Z
opus.identifier.opusid59402
opus.institute.number0463
opus.institute.number0424
opus.metadataonlyfalse
opus.organisation.stringFB 04: Medizin: Centrum für Thrombose und Hämostase (CTH)de_DE
opus.organisation.stringFB 04: Medizin: Institut für Med. Biometrie, Epidemologie und Informatikde_DE
opus.subject.dfgcode04-205
opus.type.contenttypeKeinede_DE
opus.type.contenttypeNoneen_GB

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