Seurat (version 4.3.0.1)

RunSLSI: Run Supervised Latent Semantic Indexing

Description

Run a supervised LSI (SLSI) dimensionality reduction supervised by a cell-cell kernel. SLSI is used to capture a linear transformation of peaks that maximizes its dependency to the given cell-cell kernel.

Usage

RunSLSI(object, ...)
# S3 method for default
RunSLSI(
  object,
  assay = NULL,
  n = 50,
  reduction.key = "SLSI_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)
# S3 method for Assay
RunSLSI(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.key = "SLSI_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)
# S3 method for Seurat
RunSLSI(
  object,
  assay = NULL,
  features = NULL,
  n = 50,
  reduction.name = "slsi",
  reduction.key = "SLSI_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)

Value

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

Arguments

object

An object

...

Arguments passed to IRLBA irlba

assay

Name of Assay SLSI is being run on

n

Total Number of SLSI components to compute and store

reduction.key

dimensional reduction key, specifies the string before the number for the dimension names

graph

Graph used supervised by SLSI

verbose

Display messages

seed.use

Set a random seed. Setting NULL will not set a seed.

features

Features to compute SLSI on. If NULL, SLSI will be run using the variable features for the Assay.

reduction.name

dimensional reduction name