Shinyngs is an R package designed to facilitate downstream analysis of RNA-seq and similar expression data with various exploratory plots and data mining tools. It is unrelated to the recently published Shiny Transcritome Analysis Resource Tool (START), though it was probably developed at the same time as that work.
A companion R package, zhangneurons, contains an example dataset to illustrate the features of Shinyngs, as well as the code required to produce it.
A Shinyngs example is running at https://pinin4fjords.shinyapps.io/shinyngs_example/ and contains a subset of the example data (due to limited resources on shinyapps.io).
Shinyngs differs to START and other similar applications (see also Degust), in that no effort is made to provide analysis capabilities. The envisaged process is:
- RNA-seq data is analysed, producing a set of matrices, and p/q values generated for a given set of comparisons.
- Matrix and comparison data is loaded into the modified SummarizedExperiment structure provided by Shinyngs, and serialised. This is easily automated.
- Serialised object used as input to autmoatically produce the Shiny app using Shinyngs.
There are a great many experimental designs and analysis methods, and in building Shinyngs I've taken the view that analysis is best left to the analyst. The envisaged use case is that of a bioinformatician attempting to convey results of analysis to non-experts.
ShinyNGS provides a number of capabilities you may not find in other applications:
- Simple selection of gene sets by name/ annotation to modify the plots and tables shown.
- Progressive filters for differential analysis: "Show me all genes differential in these contrasts but NOT in these other contrasts"
- Large variety of visualisations: row-wise clustering, UpSet-style intersection plots, gene set enrichment barcode plots etc.
- Allow rapid exploration of data output more or less straight from RNA-seq piplelines etc.
- Where more parameters are provided, extend the exploratory tools available - e.g. for differential expression.
- A variety of single and multiple-panel Shiny applications- currently heatmap, pca, boxplot, dendrogram, gene-wise barplot, various tables and an RNA-seq app combining all of these.
- Leveraging of libraries such as DataTables and Plotly for rich interactivity.
- Takes input in an extension of the commonly used
SummarizedExperimentformat, calledExploratorySummarizedExperiment - Interface kept simple where possible, with complexity automatically added where required:
- Input field clutter reduced with the use of collapses from shinyBS (when installed).
- If a list of
ExploratorySummarizedExperiments is supplied (useful in situiations where the features are different beween matrices - e.g. from transcript- and gene- level analyses), a selection field will be provided. - If a selected experiment contains more than one assay, a selector will again be provided.
- For me: leveraging of Shiny modules. This makes re-using complex UI components much easier, and maintaining application code is orders of magnitude simpler as a result.
Shinyngs is built on Shiny 'modules'- most of which are in single files in the package code. As a consequence code is highly re-usable. Documentation forthcoming, but take a look at how the selectmatrix module is called by the PCA plots, boxplots etc.
shinyngs relies heavily on SummarizedExperiment. Formerly found in the GenomicRanges package, it now has its own package on Bioconductor: http://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html. This requires a recent version of R.
Graphical enhancements are provided by shinyBS and shinyjs
Strong recommendation for Chrome over Firefox - everything renders much more nicely.
devtools::install_github('pinin4fjords/shinyngs', upgrade_dependencies = FALSE)An example ExploratorySummarizedExperimentList based on the Zhang et al study of neurons and glia (http://www.jneurosci.org/content/34/36/11929.long) is included in the package, and this can be used to demonstrate available features.
library(shinyngs)
data("zhangneurons")
app <- prepareApp("rnaseq", zhangneurons)
shiny::shinyApp(app$ui, app$server)The function eselistFromYAML() is provided to help build your own objects given a config file.
Technical information can be accessed via the package documentation:
?shinyngsMore user-oriented documentation and examples of how to build your own apps in the vignette.
This is also accessible via the vignette command:
vignette('shinyngs')- More useful non-RNAseq functionality to be added
I can be reached on @pinin4fjords with any queries. Other contributors welcome.