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

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.

GXP: Analyze and Plot Plant Omics Data in Web Browsers

“…We revised platforms and software solutions that have been developed to provide the user with tools to quantify RNAseq raw data and contrast this quantified data in differential expression analysis. The revision includes tools that generate scientific plots, carry out clustering, principal component, and overrepresentation analysis [ 1 , 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 , 32 , 35 , 41 ]. However, the presented tools require either some programming expertise, or manual installation of software, or send potentially confidential data via the web to dedicated servers.…”

“…Among others, they provide means to plot the data, carry out clustering, and conduct principal component and overrepresentation analyses. A number of these tools specialize in RNAseq analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], most of which consume the raw gene expression count data produced by standard gene expression quantifiers [ 22 , 23 , 24 , 25 ] and enable the user to identify differentially expressed genes [ 6 , 7 , 8 , 9 , 11 , 12 , 13 , 15 , 16 , 20 , 21 , 26 , 27 ] and review the results in form of comprehensive reports and/or plots [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 18 , 19 , 20 , 21 , 26 , 27 , 28 ]. Some [ 7 , 8 , 9 ,…”

“…A number of these tools specialize in RNAseq analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], most of which consume the raw gene expression count data produced by standard gene expression quantifiers [ 22 , 23 , 24 , 25 ] and enable the user to identify differentially expressed genes [ 6 , 7 , 8 , 9 , 11 , 12 , 13 , 15 , 16 , 20 , 21 , 26 , 27 ] and review the results in form of comprehensive reports and/or plots [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 18 , 19 , 20 , 21 , 26 , 27 , 28 ]. Some [ 7 , 8 , 9 , 10 , 11 ,…”

“…Other tools focus on metabolomics [ 32 , 33 , 34 ] or, integrating RNAseq and Metabolomics data, the identification of genotype–phenotype relationships [ 35 ]. Some of the introduced tools [ 7 , 13 , 28 ] offer the elucidation of interactive plots where, e.g., hovering with the mouse over data points in a plot summarizing differential gene expression opens another plot illustrating the expression counts of the particular gene represented by the hovered-over data point. For a more detailed review of the above tools, see supplemental Text S1 .…”

The mitoXplorer 2.0 update: integrating and interpreting mitochondrial expression dynamics within a cellular context

“…FeatureCounts (19) was used to calculate read counts. Normalisation and differential expression analysis was done using DESeq2 via RNfuzzyApp (20). Enrichment analysis was done usine EnrichR (21), for further downstream analysis, we used the KEGG resource (22).…”