q.pca | R Documentation |
Visualize gene expression similarity using principal coordinate analysis
q.pca(x, y, method = "linear", lower.thr = 0, n.gene = ncol(x), size = 1)
x |
A data matrix (row: samples, col: genes). |
y |
A vector of an environment in which the samples were collected. |
method |
A string to specify the method of regression for calculating R-squared values. "linear" (default), "quadratic" or "cubic" regression model can be specified. |
lower.thr |
The lower threshold of R-squared value to be indicated in a PCA plot (default: 0). |
n.gene |
The number of candidate genes for QuEST model to be indicated in a PCA plot (default: ncol(x)). |
size |
The size of symbols in a PCA plot (default: 1). |
A PCA plot
Takahiko Koizumi
data(Pinus) train <- q.clean(Pinus$train) target <- Pinus$target q.pca(train, target)
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