RunSLSI: Run Supervised Latent Semantic Indexing

In Seurat: Tools for Single Cell Genomics

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, ...)

## Default S3 method:
RunSLSI(
  object,
  assay = NULL,
  n = 50,
  reduction.key = "SLSI_",
  graph = NULL,
  verbose = TRUE,
  seed.use = 42,
  ...
)

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

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

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

Value

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

Seurat documentation built on May 29, 2024, 4:20 a.m.