Seurat: RunSPCA – R documentation

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RunSPCA

Run Supervised Principal Component Analysis

Description

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.

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 '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.
`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;

Seurat

Tools for Single Cell Genomics

v4.0.1

GPL-3 | file LICENSE

Authors

Andrew Butler [ctb] (<https://orcid.org/0000-0003-3608-0463>), Saket Choudhary [ctb] (<https://orcid.org/0000-0001-5202-7633>), Charlotte Darby [ctb] (<https://orcid.org/0000-0003-2195-5300>), Jeff Farrell [ctb], Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Jaison Jain [ctb] (<https://orcid.org/0000-0002-9478-5018>), Efthymia Papalexi [ctb] (<https://orcid.org/0000-0001-5898-694X>), Patrick Roelli [ctb], Rahul Satija [ctb] (<https://orcid.org/0000-0001-9448-8833>), Karthik Shekhar [ctb], Avi Srivastava [ctb] (<https://orcid.org/0000-0001-9798-2079>), Tim Stuart [ctb] (<https://orcid.org/0000-0002-3044-0897>), Kristof Torkenczy [ctb] (<https://orcid.org/0000-0002-4869-7957>), Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Satija Lab and Collaborators [fnd]

Initial release

2021-03-17