Description Usage Arguments Value Examples
Performs PCA svd variance decomposition and produces plot of the first two principal components
1 | batchqc_pca_svd(data.matrix, batch, mod = NULL)
|
data.matrix |
Given data or simulated data from rnaseq_sim() |
batch |
Batch covariate |
mod |
Model matrix for outcome of interest and other covariates besides batch |
res PCA list with two components v and d.
1 2 3 4 5 6 7 8 9 10 11 | 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)
batchqc_pca_svd(data.matrix, batch, mod=modmatrix)
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