The goal of this project is to build a beautiful parser of data that can interpret matrix data (with a specific use-case being gene expression matrices) and construct basic interactive plots for data exploration and preliminary analyses.
Use the online version of SparseData Cluster. See Installation for details on installing locally.
- Upload : Upload your own flat files (comma, tab, or semi-colon delimited) for analysis.
- Cluster : Pair-wise correlation is computed between observations (by default, rows of matrix input) and displayed as a heatmap. A summary of the matrix is also given as plain text.
- Rank : Choose 2 observations to view an interactive table of the differences for each feature. Note that when data is log2 transformed during Upload, these will correspond to log fold changes.
- This App depends on installation of the following R packages:
shiny(version >= 0.12.1),shinydashboard,shinyapps,markdown,gplots,RColorBrewer.
Open app.R and run the code in an interactive R session in the same directory
The application is organized into separate files as follows:
app.R: The top-level application that sources the rest of the necessary files to build the app and calls theshinyAppfunctionglobal.R: Globally needed packages and global variables to share data across multiple embedded appsheader.R: constructs the header barsidebar.R: constructs the sidebar; specific pages are delineated here via thetabNamefunction, and are similarly defined inbody.Rbody.R: page-level construction of eachtabNamespecified insidebar.Rserver.R: provides interactivity and backend calculations
Stefan Avey constructed the underlying base, and Rob Amezquita applied a slick coat of paint on it.