

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.
SEEK ID: https://funginet.hki-jena.de/publications/138
PubMed ID: 32093752
Projects: B2
Publication type: Not specified
Journal: BMC Res Notes
Citation: BMC Res Notes. 2020 Feb 24;13(1):92. doi: 10.1186/s13104-020-04946-1.
Date Published: 24th Feb 2020
Registered Mode: Not specified

Views: 1587
Created: 15th Feb 2021 at 11:12
Last updated: 17th Jan 2024 at 10:24

This item has not yet been tagged.

None