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covRNA: discovering covariate associations in large-scale gene expression data
Lara Urban1,2, Christian W Remmele1, Marcus Dittrich1,3
1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany.
BMC Research Notes
|February 26, 2020
Summary
The covRNA tool integrates gene expression data with sample and gene annotations. It visualizes correlations and identifies factors influencing expression patterns, aiding biological interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Interpreting gene expression data is complex.
- Current ordination methods lack annotation integration.
- A need exists for tools that incorporate sample and gene annotations.
Purpose of the Study:
- To develop a tool for assessing and visualizing gene expression data correlations.
- To enable discovery of covariates affecting expression patterns.
- To provide a user-friendly interface for analyzing annotated gene expression data.
Main Methods:
- The Bioconductor package covRNA offers a fast and convenient interface.
- It employs statistical permutation tests and ordination for relationship analysis.
- Methods are adapted from ecological research (fourthcorner, RLQ) for gene expression data.
Main Results:
- covRNA effectively tests and visualizes relationships between sample/gene covariates and gene expression.
- The tool is suitable for RNA-Seq read counts and microarray intensities.
- A high-performance parallelized implementation supports large-scale data analysis.
Conclusions:
- covRNA facilitates unsupervised analysis of annotated gene expression data.
- It enhances biological interpretation by revealing covariate effects.
- The package includes modules for gene filtering and plotting for a complete workflow.
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