Description Usage Arguments Value Examples
Performing PCA on a dataset and create a list object with results.
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pheno_mx |
Phenotype matrix with diemnsions g x N, where g is the number of genes and N is the number of samples. |
assay_idx |
The assay index to be used to retrieved a single assay from the SummarizedExperiment object. |
scale_pheno |
Logical value specifying the scaling of row of the pheno_mx. Default is set to FALSE. |
The following entries will be generated in the output list pca_result
after running the example above.
rotation
: Matrix of principal component gene weights where each column represents a single component. (standard prcomp()
output)
x
: Matrix of the projections of the original data onto principal componets. Each column holds a projection. (standard prcomp()
output)
sdev
: The standard deviation (square root of the eigen values) of each principal components. (standard prcomp()
output)
percent_var
: The percent variance each principal component is explaining. Calculated based on sdev
.
peaks
: Indicating which gene has a gene weight larger than 2 standard deviations of its component gene weights.
center
: The mean values for each gene used to center the data. (standard prcomp()
output)
scale
: TRUE or FALSE value indicating whether the data was scaled. (standard prcomp()
output)
Three attributes are set within the list object. "PCAobject" for class
, "pca" for method
and "no" for covar_cor
.
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