runFeatureSelection: Run Variable Feature Detection Methods

In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Run Variable Feature Detection Methods

Description

Wrapper function to run all of the feature selection methods integrated within the singleCellTK package including three methods from Seurat ("vst", "mean.var.plot" or dispersion) and the Scran modelGeneVar method.

This function does not return the names of the variable features but only computes the metrics, which will be stored in the rowData slot. To set a HVG list for downstream use, users should call setTopHVG after computing the metrics. To get the names of the variable features, users should call getTopHVG function after computing the metrics.

Usage

runFeatureSelection(inSCE, useAssay, method = "vst")

Arguments

inSCE	Input SingleCellExperiment object.
useAssay	Specify the name of the assay that should be used. Should use raw counts for "vst" method, or a normalized assay for other methods.
method	Specify the method to use for variable gene selection. Options include "vst", "mean.var.plot" or "dispersion" from Seurat and "modelGeneVar" from Scran. Default "vst"

Value

The input SingleCellExperiment object that contains the computed statistics in the rowData slot

See Also

runModelGeneVar, runSeuratFindHVG, getTopHVG, plotTopHVG

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runFeatureSelection(mouseBrainSubsetSCE,
                                           "logcounts",
                                           "modelGeneVar")

compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.