CumulativeVarPlot: Plot of the Cumulative Variance due to the Principal...

In gPCA: Batch Effect Detection via Guided Principal Components Analysis

Description

The function plots the cumulative variance of the principal components.

Usage

CumulativeVarPlot(out, ug = "unguided", ...)

Arguments

out	object resulting from gPCA.batchdetect() call.
ug	"guided" or "unguided". Do you want the cumulative variance from guided or unguided PCA plotted.
...	any other plot calls.

Details

This function plots the cumulative variance of the principal components from guided or unguided PCA calculated as (for the unguided case)

Var_l=\frac{∑_{i=1}^l \var(PC_u)_i}{∑_{i=1}^n \var(PC_u)_i}

for the lth principal component (l=1,…,n).

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, PCplot

Examples

# CumulativeVarPlot(out,ug="unguided",col="blue")

Example output

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