Description Usage Arguments Value Examples
Dimensionality reduction using PCA by computing principal components using highly variable genes
1 2 |
mat |
Variance normalized gene expression matrix. |
nGenes |
Number of most variable genes. (default: 1000) |
nPcs |
Number of principal components. (default: 100) |
verbose |
Verbosity (default: TRUE) |
... |
Additional parameters to pass to irlba |
Matrix with columns as cells and rows as principal component eigenvectors.
1 2 3 4 5 6 7 | {
data(pbmcA)
cd <- pbmcA[, 1:500]
mat <- cleanCounts(cd)
mat <- normalizeVariance(mat)
pcs <- getPcs(mat)
}
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