TCGA Chart

Data obtained from Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data.

# TCGA data

It is common in the field of Genomics to explore the gene expression profile of one or a list of genes involved in a pathway of interest. Here, we present some exploratory data analysis with the TCGA data.

The expression data can be displayed faceted by variable. It can also be displayed faceted by dataset and colored by the sample factor, dataset - cancer type.


The Cancer Genome Atlas (TCGA) data is a publicly available data containing clinical and genomic data across 33 cancer types. These data include gene expression, CNV profiling, SNP genotyping, DNA methylation, miRNA profiling, exome sequencing, and other types of data.

Configuration

The configuration for the visualization above is shown below. You can always access the data and its configuration in any CanvasXpress visualization through the file menu under File → Reproducible Research → Show JSON code. See additional information under User Interface - Menus. For convience you can click this .

{
   "binned" : "true",
   "boxplotWhiskersType" : "single",
   "colorBy" : "dataset",
   "colorScheme" : "JCO",
   "graphOrientation" : "vertical",
   "graphType" : "Boxplot",
   "groupingFactors" : ["dataset"],
   "histogramBins" : 150,
   "layoutTopology" : "1X3",
   "legendColumns" : 3,
   "legendPosition" : "top",
   "legendScaleFontFactor" : 1.5,
   "segregateVariablesBy" : ["variable"],
   "showBoxplotOriginalData" : "true",
   "smpLabelRotate" : 90,
   "useOpenShapes" : "true"
}

R Code

The R code for the visualization above is shown below. For convenience the data and the meata data in the excert is shown in a url accessible tab delimited file to facilitate readability. You can always access the full R code in any CanvasXpress visualization through the file menu under File → Reproducible Research → Show R code. See additional information under User Interface. For convience you can click this .

library(canvasXpress)
y=read.table("https://www.canvasxpress.org/data/cX-exprtcga-dat.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
x=read.table("https://www.canvasxpress.org/data/cX-exprtcga-smp.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(
  data=y,
  smpAnnot=x,
  binned=TRUE,
  boxplotWhiskersType="single",
  colorBy="dataset",
  colorScheme="JCO",
  graphOrientation="vertical",
  graphType="Boxplot",
  groupingFactors=list("dataset"),
  histogramBins=150,
  layoutTopology="1X3",
  legendColumns=3,
  legendPosition="top",
  legendScaleFontFactor=1.5,
  segregateVariablesBy=list("variable"),
  showBoxplotOriginalData=TRUE,
  smpLabelRotate=90,
  useOpenShapes=TRUE
)

# Events and Functionality

Out of the box events

Events are pre-configured in all CanvasXpress visualizations. Refer to documentation to further customize events. Most visualizations have mouseover, click, dbl-click, wheel, context-menu, drag and drop. Press the 'ESC' key to reset the plot.

User Interfaces for exploring and configuring visualization

All visualization come with multiple user interfaces to allow data exploration and configuration. Dbl-click to bring configurator, context-menu to open menus, mouse-over top right to visualization to show toolbar and select either the funnel to open filters and data table or the tools to open the data explorer. See further documentation under User Interfaces.

Drag'n Drop files on the CanvasXpress Visualization

You can drag and drop any delimited text file on the visualization to create a new CanvasXpress plot. Similarly, you can drag and drop formated XML files from Wikipathways, KeGG, Metabase or GML. Furthermore, you can also drag and drop png or json files previously saved in CanvasvasXpress to re-create the visualization.