Data visualisation, interactive data analysis, statistical programming

Case study: Characterizing mutation-expression networks in multiple cancers

• Shila Ghazanfar's BIS2015 poster has an accompanying Shiny app.
• 4,443 tumours analysed over 19 cancers from TCGA
• R Shiny app to explore results among different parameters is available to download at www.maths.usyd.edu.au/u/sheilag/pacmen.zip

Instructions

• Check out Shila's fast forward poster session slide
• Download and unzip from www.maths.usyd.edu.au/u/sheilag/pacmen.zip
• Install a few R packages including customised version of networkD3 using:

install.packages(c("shiny", "igraph", "WGCNA", "devtools"))
source("http://bioconductor.org/biocLite.R")
biocLite(c("impute", "GO.db", "preprocessCore"))
devtools::install_github("garthtarr/networkD3", ref = "color")

• Run the app using a command like (replace ~/Downloads/ with the path to the pacmen unzipped folder):

shiny::runApp("~/Downloads/pacmen")

• Alternatively you can run the app by opening either the server.R or the ui.R files in RStudio and then clicking the Run App button in the top right of the code editor window.
• Explore the app to get a feel for the sorts of things you can do with Shiny.
• Check out Shila's twitter account

Common issues

• It may take a little while for the app to load, be patient! Serious computations are in progress.
• If the linkages are missing in the interactive graph, but you can see them in the static graph, you need to install a customised version of networkD3:

devtools::install_github("garthtarr/networkD3", ref = "color")

References

• Ghazanfar S and Yang JYH (2015). Characterizing mutation-expression network relationships in multiple cancers. F1000Research 2015, 4:1046 (poster) (doi: 10.7490/f1000research.1110789.1)
• Ghazanfar S and Yang JYH (2016). Characterizing mutation-expression network relationships in multiple cancers. Computational Biology and Chemistry Special Issue for APBC2016 (in press).