runScanpyFindHVG: runScanpyFindHVG Find highly variable genes and store in the...

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

runScanpyFindHVG Find highly variable genes and store in the input sce object

Description

runScanpyFindHVG Find highly variable genes and store in the input sce object

Usage

runScanpyFindHVG(
  inSCE,
  useAssay = "scanpyNormData",
  method = c("seurat", "cell_ranger", "seurat_v3"),
  altExpName = "featureSubset",
  altExp = FALSE,
  hvgNumber = 2000,
  minMean = 0.0125,
  maxMean = 3,
  minDisp = 0.5,
  maxDisp = Inf
)

Arguments

inSCE	(sce) object to compute highly variable genes from and to store back to it
useAssay	Specify the name of the assay to use for computation of variable genes. It is recommended to use log normalized data, except when flavor='seurat_v3', in which counts data is expected.
method	selected method to use for computation of highly variable genes. One of 'seurat', 'cell_ranger', or 'seurat_v3'. Default "seurat".
altExpName	Character. Name of the alternative experiment object to add if returnAsAltExp = TRUE. Default featureSubset.
altExp	Logical value indicating if the input object is an altExperiment. Default FALSE.
hvgNumber	numeric value of how many genes to select as highly variable. Default 2000
minMean	If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'. Default 0.0125
maxMean	If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'. Default 3
minDisp	If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'. Default 0.5
maxDisp	If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'. Default Inf

Value

Updated SingleCellExperiment object with highly variable genes computation stored getTopHVG, plotTopHVG

Examples

data(scExample, package = "singleCellTK")
## Not run: 
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
g <- getTopHVG(sce, method = "seurat", hvgNumber = 500)

## End(Not run)

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