Visualization methods for differential expression analysis.

ABSTRACT:

Background

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.

Results

In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an i

SUBMITTER: Rutter L 

PROVIDER: S-EPMC6731617 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature