• Treffer 1 von 1
Zurück zur Trefferliste

covRNA: discovering covariate associations in large-scale gene expression data

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-229258
  • Objective The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. Results The Bioconductor package covRNA provides aObjective The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. Results The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.zeige mehrzeige weniger

Volltext Dateien herunterladen

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Autor(en): Lara Urban, Christian W. Remmele, Marcus Dittrich, Roland F. Schwarz, Tobias MüllerORCiD
URN:urn:nbn:de:bvb:20-opus-229258
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Medizinische Fakultät / Institut für Humangenetik
Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):BMC Reserach Notes
Erscheinungsjahr:2020
Band / Jahrgang:13
Aufsatznummer:92
Originalveröffentlichung / Quelle:BMC Reserach Notes (2020) 13:92 https://doi.org/10.1186/s13104-020-04946-1
DOI:https://doi.org/10.1186/s13104-020-04946-1
Allgemeine fachliche Zuordnung (DDC-Klassifikation):5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Freie Schlagwort(e):Fourthcorner analysis; High-throughput data; Microarray analysis; Multivariate analysis; Ordination methods; RLQ analysis; RNA-Seq analysis; Transcriptomics; Visualization
Datum der Freischaltung:22.04.2021
Sammlungen:Open-Access-Publikationsfonds / Förderzeitraum 2020
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International