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.
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).