RunWPCA.Rd
Run a weighted PCA dimensionality reduction
RunWPCA(object, q=15)
### S3 method for class "Seurat"
## RunWPCA(object, q=15)
### S3 method for class "matrix"
## RunWPCA(object, q=15)
### S3 method for class "dgCMatrix"
## RunWPCA(object, q=15)
an object named "Seurat", "maxtrix" or "dgCMatrix". The object of class "Seurat" must include slot "scale.data".
an optional positive integer, specify the number of features to be extracted.
Nothing
For Seurat object, return a Seurat object. For objcet "matrix" and "dgCMatrix", return a object "matrix" with rownames same as the colnames of X
, and colnames "WPCA1" to "WPCAq".
Bai, J. and Liao, Y. (2017). Inferences in panel data with interactive effects using large covariance matrices. Journal of Econometrics, 200(1):59–78.
nothing
None
if (FALSE) {
library(Seurat)
seu <- gendata_RNAExp(height=20, width=20,p=100, K=4)
## log-normalization
seu <- NormalizeData(seu)
##
seu <- FindVariableFeatures(seu, nfeatures=80)
## Scale
seu <- ScaleData(seu)
## Run WPCA
seu <- RunWPCA(seu)
seu
## Run tSNE based on wpca
seu <- RunTSNE(seu, reduction='wpca')
seu
## Find SVGs
seu <- FindSVGs(seu, nfeatures=80)
(genes <- topSVGs(seu, ntop=10))
Idents(seu) <- factor(paste0("cluster", seu$true_clusters), levels=paste0("cluster",1:4))
RidgePlot(seu, features = genes[1:2], ncol = 2)
FeaturePlot(seu, features = genes[1:2], reduction = 'tsne' ,ncol=2)
}