Run a supervied PCA (SPCA) dimensionality reduction supervised by a cell-cell kernel. SPCA is used to capture a linear transformation which maximizes its dependency to the given cell-cell kernel. We use SNN graph as the kernel to supervise the linear matrix factorization.
RunSPCA(object, ...) # S3 method for default RunSPCA( object, assay = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = FALSE, seed.use = 42, ... ) # S3 method for Assay RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) # S3 method for Seurat RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.name = "spca", reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... )
Arguments passed to other methods and IRLBA
Name of Assay SPCA is being run on
Total Number of SPCs to compute and store (50 by default)
dimensional reduction key, specifies the string before the number for the dimension names. SPC by default
Graph used supervised by SPCA
Print the top genes associated with high/low loadings for the SPCs
Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
Features to compute SPCA on. If features=NULL, SPCA will be run using the variable features for the Assay.
dimensional reduction name, spca by default
Returns Seurat object with the SPCA calculation stored in the reductions slot
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ. Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds. Pattern Recognition. 2011 Jul 1;44(7):1357-71. https://www.sciencedirect.com/science/article/pii/S0031320310005819?casa_token=AZMFg5OtPnAAAAAA:_Udu7GJ7G2ed1-XSmr-3IGSISUwcHfMpNtCj-qacXH5SBC4nwzVid36GXI3r8XG8dK5WOQui;