PCplot: Principal Component Plot

In gPCA: Batch Effect Detection via Guided Principal Components Analysis

Description

Produces principal component plots from either unguided or guided PCA.

Usage

PCplot(out, ug = "unguided", type = "1v2", npcs, ...)

Arguments

out	object resulting from gPCA.batchdetect() call.
ug	"guided" or "unguided". Do you want the cumulative variance from guided or unguided PCA plotted.
type	type of plot. Either "1v2" to plot the first two principal components, or "comp" to compare all principal component up to the level of npcs.
npcs	Number of principal compoents to plot when "comp" type is chosen.
...	any other plot calls.

Details

This function plots either the first principal component versus the second principal component (type="1v2") from guided or unguided PCA, or compares (type="comp") all combinations of the principal components up to the value of npcs.

Author(s)

Sarah Reese [e-mail]

References

Reese, S. E., Archer, K. J., Therneau, T. M., Atkinson, E. J., Vachon, C. M., de Andrade, M., Kocher, J. A., and Eckel-Passow, J. E. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal components analysis. Bioinformatics, (in review).

See Also

gPCA.batchdetect, gDist, CumulativeVarPlot

Examples

# PCplot(out,ug="unguided",type="1v2")
# PCplot(out,ug="unguided",type="comp",npcs=4)

Example output

gPCA documentation built on May 2, 2019, 4:02 p.m.