batchqc_pc_explained_variation: Returns explained variation for each principal components

In BatchQC: Batch Effects Quality Control Software

Description

Returns explained variation for each principal components

Usage

batchqc_pc_explained_variation(pcs, vars, condition, batch)

Arguments

pcs	Principal components in the given data
vars	Variance of the Principal components in the given data
condition	Condition covariate of interest
batch	Batch covariate

Value

Explained variation table for each principal components

Examples

nbatch <- 3
ncond <- 2
npercond <- 10
data.matrix <- rnaseq_sim(ngenes=50, nbatch=nbatch, ncond=ncond, npercond=
    npercond, basemean=10000, ggstep=50, bbstep=2000, ccstep=800, 
    basedisp=100, bdispstep=-10, swvar=1000, seed=1234)
batch <- rep(1:nbatch, each=ncond*npercond)
condition <- rep(rep(1:ncond, each=npercond), nbatch)
pdata <- data.frame(batch, condition)
modmatrix = model.matrix(~as.factor(condition), data=pdata)
pca <- batchqc_pca(data.matrix, batch, mod=modmatrix)
pcs <- t(data.frame(pca$x))
batchqc_pc_explained_variation(pcs, pca$sdev^2, condition, batch)

BatchQC documentation built on Nov. 8, 2020, 8:30 p.m.