Visualization methods for differential expression analysis

Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users.

In this paper, a team led by researchers at Iowa State University 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. The researchers use public RNA-seq datasets to show that their new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. They also show that the new visualization tools can identify genes of interest in ways undetectable with models. Their R package “bigPint” includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available.

Parallel coordinate plots of clustered significant genes in the soybean iron metabolism data

rna-seq

Parallel coordinate plots of significant genes after hierarchical clustering of the soybean iron metabolism data. We can quickly confirm that Clusters 1 and 2 show the typical pattern for significant genes. Cluster 4 does not distinctively show the usual profile for significant genes. Cluster 3 looks similar to Cluster 2, except for unexpectedly large P.3 values

The researchers emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. They hope this work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.

Availability – The “bigPint” website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code.

Rutter L, Moran Lauter AN, Graham MA, Cook D. (2019) Visualization methods for differential expression analysis. BMC Bioinformatics 20(1):458. [article]

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