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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.…
Autor(en): | Lara Urban, Christian W. Remmele, Marcus Dittrich, Roland F. Schwarz, Tobias MüllerORCiD |
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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): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |