zoomPC: Sample-specific PCA visualizations
In audrina/pcclust: PCA Analysis and Visualization for Clustering

Description Usage Arguments Value See Also Examples

Supporting PCA visualizations and sample-specific metadata including standard 2-component PCA plot labeled with query, a diverging lollipop chart showing the PC breakdown for the query, and an interactive PCA plot showing the location of the query in 3D space.

1	zoomPC(x, y, pcData, bestPCSet, clusters, iterationResults, outPath)

`x`	approximate x coordinate value from optimal PCA plot. See details for more information.
`y`	same as x.
`pcData`	a validated R matrix containing numeric scaled PCA data.
`bestPCSet`	a matrix whose columns contain the optimal principal components.
`clusters`	vector of predicted labels
`iterationResults`	list of iteration results from PCA filtering, where each iteation has one less PC. List elements are PCA matrices. Last entry in the list corresponds to the top 2 PCs for clustering.
`outPath`	path to ouput directory where pcclust_visualization folder was generated.

NULL. Outputs high quality .svg files in pcclust_visualization for each of the 2 plots.

ggplot

svg

plot3d

text3d

data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
bestPCSet <- iterationResults[[length(iterationResults)]]
clusterResults <- evaluateClusterQuality(bestPCSet)
optimalModel <- determineOptimalModel(bestPCSet)
clusters <- optimalModel$classification
out <- visualizePCA(bestPCSet, clusters, pcObj)
x <- 0.5
y <- -0.3
zoomPC(x, y, pcData, bestPCSet, clusters, iterationResults, out)