identifyVariableMolecules: Identify molecules of high variability

In theMILOlab/SPATA2: A Toolbox for Spatial Expression Analysis

Identify molecules of high variability

Description

Identfies molecules that are outliers on a mean-variability plot as proposed by Seurat.

Usage

identifyVariableMolecules(
  object,
  n_mol = 2500,
  method = "vst",
  assay_name = activeAssay(object),
  ...
)

Arguments

object	An object of class SPATA2 or, in case of S4 generics, objects of classes for which a method has been defined.
n_mol	Numeric value. The number of molecules to be identified. Given to nfeatures of Seurat::FindVariableFeatures().
method	Character value. The identification method. One of 'vst', 'mean.var.plot' or 'dispersion'. Given to selection.method of Seurat::FindVariableFeatures().
assay_name	Only relevant if the SPATA2 object contains more than one assay: Denotes the assay of interest and thus the molecular modality to use. Defaults to the active assay as set by activateAssay().
...	Used to absorb deprecated arguments or functions.

Value

The updated input object, containing the added, removed or computed results.

Examples

library(SPATA2)
data("example_data")

object <- loadExampleObject("UKF269T")

# default is method = 'vst'
object <- identifyVariableMolecules(object, n_mol = 2000)

# multiple results can be stored simultaneously
object <- identifyVariableMolecules(object, n_mol = 3000, method = "mean.var.plot")

# fails, cause results for two methods are stored and method = NULL
vars_vst <- getVariableMolecules(object)

vars_vst <- getVariableMoleculs(object, method = "vst") # works

# should work
vars_mvp <- getVariableMolecules(object, method = "mean.var.plot")

length(vars_vst)
length(vars_mvp)

theMILOlab/SPATA2 documentation built on Feb. 8, 2025, 11:41 p.m.