Interactive and Reproducible Research for RNA Sequencing Analysis

Play Interactive and Reproducible Research for RNA Sequencing Analysis
Sign in to queue


useR!2017: Interactive and Reproducible Research fo...

Keywords: RNA-Seq, Exploratory Data Analysis, Differential Expression, Interactivity, Reproducibility
Next generation sequencing technologies, such as RNA-Seq, generate tens of millions of reads to define the expression levels of the features of interest. A wide number and variety of software packages have been developed for accommodating the needs of the researcher, mostly in the R/Bioconductor framework. Many of them focus on the identification of differentially expressed (DE) genes (DESeq2, edgeR, (Love et al. 2015)) to discover quantitative changes between experimental groups, while other address alternative splicing, discovery of novel transcripts, or RNA editing.
Moreover, Exploratory Data Analysis is a common step to all these workflows, and despite its importance for generating highly reliable results, it is often neglected, as many of the steps involved might require a considerable proficiency of the user in the programming languages. Principal Components Analysis (PCA) is used often to obtain a dimension-reduced overview of the data (Jolliffe 2002).
Our proposal will address the two steps of Exploratory Data Analysis and Differential Expression analysis with two different packages, integrated and available in Bioconductor, namely pcaExplorer and ideal. We propose web applications developed in the Shiny framework which will also include support for reproducible analyses, thanks to an embedded text editor and a template document, to seamlessly generate HTML reports as a result of the user's exploration.
This solution, which we also outlined in (Marini and Binder 2016), serves as a concrete proof of principle of integrating the essential features of interactivity (as a proxy for accessibility) and reproducibility in the same tool, fitting both the needs of life scientists and experienced analyists, thus making our packages good candidates to become companion tools for each RNA-Seq analysis.
References Jolliffe, I T. 2002. "Principal Component Analysis, Second Edition." Encyclopedia of Statistics in Behavioral Science 30 (3): 487. doi:10.2307/1270093.

Love, Michael I., Simon Anders, Vladislav Kim, and Wolfgang Huber. 2015. "RNA-Seq workflow: gene-level exploratory analysis and differential expression." F1000Research 4: 1070. doi:10.12688/f1000research.7035.1.

Marini, Federico, and Harald Binder. 2016. "Development of Applications for Interactive and Reproducible Research : a Case Study." Genomics and Computational Biology 3 (1): 1–4.





Download this episode

The Discussion

Add Your 2 Cents