RunSPCA: Run Supervised Principal Component Analysis

Description

Run a supervised 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.

Usage

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 Assay5
RunSPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  reduction.key = "SPC_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  layer = "scale.data",
  ...
)
# 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,
  ...
)

Value

Returns Seurat object with the SPCA calculation stored in the reductions slot

Arguments

object: An object
...: Arguments passed to other methods and IRLBA
assay: Name of Assay SPCA is being run on
npcs: Total Number of SPCs to compute and store (50 by default)
reduction.key: dimensional reduction key, specifies the string before the number for the dimension names. SPC by default
graph: Graph used supervised by SPCA
verbose: Print the top genes associated with high/low loadings for the SPCs
seed.use: Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
features: Features to compute SPCA on. If features=NULL, SPCA will be run using the variable features for the Assay.
layer: Layer to run SPCA on
reduction.name: dimensional reduction name, spca by default

References

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;