Runs RunPCA on the embedded Seurat object and
returns the updated MatisseObject. The PCA result is stored inside
the Seurat object and accessible via GetSeurat(obj).
Usage
# S3 method for class 'MatisseObject'
RunPCA(
object,
assay = NULL,
features = NULL,
npcs = 50,
rev.pca = FALSE,
weight.by.var = TRUE,
verbose = TRUE,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.name = "pca",
reduction.key = "PC_",
seed.use = 42,
...
)Arguments
- object
A
MatisseObject.- assay
Name of Assay PCA is being run on
- features
Features to compute PCA on. If features=NULL, PCA will be run using the variable features for the Assay. Note that the features must be present in the scaled data. Any requested features that are not scaled or have 0 variance will be dropped, and the PCA will be run using the remaining features.
- npcs
Total Number of PCs to compute and store (50 by default)
- rev.pca
By default computes the PCA on the cell x gene matrix. Setting to true will compute it on gene x cell matrix.
- weight.by.var
Weight the cell embeddings by the variance of each PC (weights the gene loadings if rev.pca is TRUE)
- verbose
Print the top genes associated with high/low loadings for the PCs
- ndims.print
PCs to print genes for
- nfeatures.print
Number of genes to print for each PC
- reduction.name
dimensional reduction name, pca by default
- reduction.key
dimensional reduction key, specifies the string before the number for the dimension names. PC by default
- seed.use
Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
- ...
Additional arguments forwarded to
RunPCA(e.g.assay,npcs,features).