RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis.

Margaux Haering,Bianca H Habermann

doi:10.12688/f1000research.54533.1

Abstract

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Highlights

The development of generation sequencing (NGS) methods has boosted the rapid generation of large datasets and RNA sequencing (RNA-seq) has become the standard for performing robust transcriptional profiling and quantifying gene expression in various contexts
RNfuzzyApp offers ID conversion, orthology assignment and enrichment analysis using gprofiler2.9 We show the usability of RNfuzzyApp on two examples: an RNA-seq dataset of the ageing limb muscle of mouse, as well as developmental time-series RNA-seq data of the Drosophila melanogaster leg
We found 177 genes differentially regulated between 12 and 3 months, 873 genes differentially regulated between ages 27 and 3 months and 31 genes differentially expressed between ages 12 and 27 months when using an FDR of 0.01 and a log2FC of |0.5|

Summary

Introduction

The development of generation sequencing (NGS) methods has boosted the rapid generation of large datasets and RNA sequencing (RNA-seq) has become the standard for performing robust transcriptional profiling and quantifying gene expression in various contexts. While web-based, user-friendly R shiny apps have become available recently for differential expression analysis and data visualization of RNA-seq data,[1,2,3,4,5,6,7] the analysis of time-series data within R remains largely command-line based and challenging for bench scientists without programming knowledge. We here present RNfuzzyApp, a user-friendly, web-based R shiny app with an intuitive user interface for the full workflows of differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers ID conversion, orthology assignment and enrichment analysis using gprofiler2.9 We show the usability of RNfuzzyApp on two examples: an RNA-seq dataset of the ageing limb muscle of mouse, as well as developmental time-series RNA-seq data of the Drosophila melanogaster leg

Methods

Results

Conclusion