RunSPCA: Run Supervised Principal Component Analysis

In satijalab/seurat: Tools for Single Cell Genomics

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, ...)

## Default S3 method:
RunSPCA(
  object,
  assay = NULL,
  npcs = 50,
  reduction.key = "SPC_",
  graph = NULL,
  verbose = FALSE,
  seed.use = 42,
  ...
)

## S3 method for class 'Assay'
RunSPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  reduction.key = "SPC_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)

## S3 method for class '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 class 'Seurat'
RunSPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  reduction.name = "spca",
  reduction.key = "SPC_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)

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

Value

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

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;

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.