Helmholtz Gemeinschaft

Search
Browse
Statistics
Feeds

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

[thumbnail of Original Article]
Preview
PDF (Original Article) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB

Item Type:Article
Title:covRNA: discovering covariate associations in large-scale gene expression data
Creators Name:Urban, L., Remmele, C.W., Dittrich, M., Schwarz, R.F. and Müller, T.
Abstract: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 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.
Keywords:Multivariate Analysis, Fourthcorner Analysis, RLQ Analysis, Transcriptomics, High-Throughput Data, Visualization, Ordination Methods, RNA-Seq Analysis, Microarray Analysis
Source:BMC Research Notes
ISSN:1756-0500
Publisher:BioMed Central
Volume:13
Number:1
Page Range:92
Date:24 February 2020
Official Publication:https://doi.org/10.1186/s13104-020-04946-1
PubMed:View item in PubMed

Repository Staff Only: item control page

Downloads

Downloads per month over past year

Open Access
MDC Library