Oncoprint from a Delimited Mutation Matrix
Oncoprint graphs offer a visually appealing and informative way to explore genomic alterations across multiple samples in cancer research. They condense complex data into an easily digestible format, highlighting the frequency and type of mutations, copy number variations, and other genomic events within a tumor cohort. This allows researchers to quickly identify patterns and potential therapeutic targets. These graphs typically display samples as rows and genomic alterations as columns, using color-coding to represent different mutation types or copy number changes. Oncoprints are invaluable for identifying frequently mutated genes, characterizing tumor heterogeneity, and guiding personalized cancer medicine strategies. The visualization helps researchers to understand cancer evolution and develop effective treatment plans.
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}
// Create the configuration for the graph
var config = {
"graphType": "Heatmap",
"oncoprintCNA": "data2",
"overlaysThickness": 100,
"oncoprintPresorted": true,
"oncoprintMUT": "data3"
}
// Event used to create graph (optional)
var events = false
// Call the CanvasXpress function to create the graph
var cX = new CanvasXpress("canvasId", data, config, events);
</script>
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</html>
library(canvasXpress)
y=read.table("https://www.canvasxpress.org/data/r/cX-oncoprint5-dat.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
y2=read.table("https://www.canvasxpress.org/data/r/cX-oncoprint5-dat2.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
y3=read.table("https://www.canvasxpress.org/data/r/cX-oncoprint5-dat3.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(
data=list(y=y, data2=y2, data3=y3),
graphType="Heatmap",
oncoprintCNA="data2",
oncoprintMUT="data3",
oncoprintPresorted=TRUE,
overlaysThickness=100
)
